An intelligent material distribution method for production workshops based on a time-varying work station set

Abstract

The present invention discloses an intelligent material distribution method for a production workshop based on a time-varying workstation set. The method is as follows: Based on the workstation information and distribution vehicle information in the production workshop, a value range for the reorder point of the workstation is set. By calculating the workstation service time window and according to the cross relationship, the time-varying workstation set of each distribution cycle is dynamically divided. By coordinating the distribution decision-making elements and jointly optimizing with the adjacent distribution workstation set, the overall planning of the distribution path is realized. At the same time, a distribution optimization model under the time-varying workstation set is constructed, and a two-layer genetic algorithm is used for hierarchical solution to obtain a joint distribution plan including the reorder point of the workstation and the corresponding material distribution plan. By considering the time-varying characteristics of the distribution workstation set and dynamically planning the distribution path according to the distribution decision-making elements, the present invention enables the distribution plan to overall consider the dynamic changes in the material requirements of each workstation, reduces the total cost of material distribution, and realizes the intelligent planning of the distribution plan.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimizing the material distribution in a workshop, and particularly relates to an intelligent material distribution method for a production workshop based on a time-varying work station set. Background Art

[0002] With the innovative development and cross-integration of the new generation of information technology, the manufacturing industry, as the cornerstone of China's industrial development, is entering a stage of high-quality development. Its production technology is becoming increasingly mature, and the focus of enterprises is gradually expanding from a single production field to a more comprehensive non-production field. Among them, production logistics, as a key link in the non-production field, has attracted much attention.

[0003] Production logistics is the foothold of the deep integration of the manufacturing industry and the logistics industry, running through the entire production process and covering the flow and management of materials, information, and funds in the production process. Under the background of current intelligent manufacturing, accurate and intelligent material distribution is the key to the efficient and low-cost operation of production logistics. Its function has gradually evolved from traditional simple distribution to a key part with certain value-added functions, precisely controlling the smooth progress of the entire production process and being of great significance for improving production efficiency and reducing production risks.

[0004] Traditional material distribution strategies usually pre-determine the material distribution plan based on the production plan and adopt a distribution mode with a fixed work station set. Fixed distribution personnel deliver materials to designated work stations regularly according to the plan. Although this mode performs well in a stable demand environment, in today's highly dynamic and complex production environment, the deficiencies of this mode are gradually emerging. For example, problems such as being unable to effectively handle demand fluctuations, being difficult to adjust the distribution route and distribution time in real time, and insufficient response to the immediate needs of the production line limit the intelligent development of production logistics. The present invention makes up for the deficiencies of traditional strategies by reasonably planning the reorder point of work stations, determining the distribution work station set and distribution-related decisions in a time-varying state, and solving through constructing a distribution optimization model and adopting a double-layer genetic algorithm. Specifically: the reasonable planning of the reorder point of work stations enhances the visualization level and planning ability of material distribution; the determination of the distribution work station set and distribution-related decisions enables the distribution process to meet the dynamic needs of work stations; the application of the distribution optimization model and the solving algorithm enables the distribution process to be closely combined with the actual application scenario, realizing the intelligent planning of the distribution plan. These innovative improvements enable the present invention to be better applicable to different production situations and application scenarios, realizing intelligent, flexible, and efficient material distribution planning. Summary of the Invention

[0005] Aiming at the deficiencies of traditional distribution strategies, the present invention provides an intelligent material distribution method for a production workshop based on a time-varying workstation set, aiming to construct a more intelligent and flexible material distribution method, in order to achieve the dynamic coordination between the distribution process and real-time production requirements, and further help enterprises achieve the goal of cost reduction and efficiency improvement.

[0006] To achieve the above object, the present invention adopts the following technical solutions: An intelligent material distribution method for a production workshop based on a time-varying workstation set, comprising the following steps:

[0007] Step S1: Collect the workstation information and distribution vehicle information of the production workshop, and determine the time range for which material distribution needs to be executed;

[0008] Step S2: Based on the workstation information and distribution vehicle information collected in Step S1, limit the value range of the reorder point of the workstation;

[0009] Step S3: Based on the value range of the reorder point of the workstation set in Step S2, randomly set a reasonable reorder point for each workstation, and after combination, form a reorder point combination of workstations;

[0010] Step S4: Based on the reorder point combination of workstations and the workstation information collected in Step S1, calculate the service time window of the workstation, and determine the distribution workstation set of the current distribution cycle based on the intersection relationship of the service time windows of the workstations;

[0011] Step S5: According to the specific number of distribution workstations of the distribution vehicle, determine the calculation method of the departure time of the distribution vehicle, and based on the arrival time of the workstation, determine the calculation method of the material distribution quantity of the workstation;

[0012] Step S6: Based on the distribution workstation set determined in Step S4 and the calculation methods of the departure time of the distribution vehicle and the material distribution quantity of the workstation determined in Step S5, use the forward deduction method to calculate and evaluate the loading state of the distribution vehicle in real time, plan the material distribution path, and obtain the distribution planning result under the time-varying characteristics of the current distribution cycle;

[0013] Step S7: Based on the distribution planning result under the time-varying characteristics of the current distribution cycle obtained in Step S6, update the service time window of the workstation and the distribution workstation set of the next distribution cycle according to Step S4, and by comparing the service time window of the distribution workstation of the distribution vehicle at the end of the current distribution cycle with the earliest departure time of the next distribution cycle, optimize the coordination between the distribution workstation of the distribution vehicle at the end of the current distribution cycle and the adjacent distribution workstation set;

[0014] Step S8: Based on the workstation information and distribution vehicle information collected in Step S1, construct a distribution optimization model under the time-varying workstation set;

[0015] Step S9: Based on the distribution optimization model under the time-varying workstation set constructed in Step S8, use the inner genetic algorithm to solve the distribution optimization model, and repeat Step S5 - Step S7 until all material distribution tasks within the time range determined in Step S1 for which material distribution needs to be performed are completed, and output the overall distribution planning scheme at the current reorder point of the workstation;

[0016] Step S10: Record the overall distribution planning scheme obtained in Step S9, use the outer genetic algorithm to update the reorder point of the workstation, and repeat Step S4 - Step S9 until the maximum number of iterations is reached, and output the optimal distribution scheme including the combination of reorder points of the workstation and the distribution plan.

[0017] Further, in Step S1, collect the workstation information and distribution vehicle information in the production workshop to determine the time range for which material distribution needs to be performed; specifically:

[0018] The workstation information includes: the number of workstations, the driving distance between workstations, the available time of unit materials at the workstation, the workstation service time, the volume of the workstation bin, the maximum number of bins in the in-line inventory at the workstation, and the loadable quantity of the unit bin at the workstation;

[0019] The distribution vehicle information includes: the driving speed of the distribution vehicle and the maximum loading volume of the distribution vehicle;

[0020] The time range for which material distribution needs to be performed is preset.

[0021] Further, in Step S2, based on the workstation information and distribution vehicle information collected in Step S1, limit the value range of the reorder point of the workstation; specifically:

[0022] Set the minimum value level of the reorder point of the workstation as shown in formula (1):

[0023] (1);

[0024] Set the maximum value level of the reorder point of the workstation as shown in formula (2):

[0025] (2);

[0026] In the formula: is the reorder point of workstation ; is the minimum value level of the reorder point of the workstation; is the reorder point of workstation ; * is the multiplication sign; is the workstation number, belonging to the set of all workstations ; is the set of all workstations; The number of the dispatched delivery vehicle, belonging to the set of all delivery vehicles ; is the set of all delivery vehicles; is the distance between the warehouse and the work station , unit: m; is the driving speed of the delivery vehicle, unit: m / min; is the work station the available time of a unit of material, unit: s; is the work station the maximum number of bins in the line-side inventory of the work station; is the work station the loadable material quantity per bin.

[0027] Furthermore, in step S3, based on the reorder point value range of the work stations set in step S2, a reasonable reorder point for each work station is randomly set, and after combination, it becomes the reorder point combination of the work stations; specifically:

[0028] For each work station , based on the reorder point value range of the work stations , a random integer within the reorder point value range of the work stations is selected as the reorder point of the work station , and the reorder points of each work station are combined to form the reorder point combination of the work stations .

[0029] Furthermore, in step S4, based on the reorder point combination of the work stations and the work station information collected in step S1, the service time window of the work stations is calculated, and based on the intersection relationship of the service time windows of the work stations, the set of delivery work stations in the current delivery cycle is determined; specifically:

[0030] Step S41: Calculation of the service time window of the work stations;

[0031] The service time window of the work stations consists of the delivery arrival time window and the delivery departure time window. According to the time-varying characteristics, the setting standard of the service time window of the work stations is determined, and the specific calculation method is:

[0032] The earliest arrival time of the delivery is set as shown in formula (3):

[0033] (3);

[0034] In the formula, is the earliest arrival time of the th delivery to the work station ; is the time when the material is sent to the work station for the th time ; is the work station The quantity of materials after the th delivery to the workstation; is a delivery of the workstation ; is the set of material delivery times of the workstation

[0035] The latest arrival time of the delivery is set as shown in formula (4):

[0036] (4);

[0037] In the formula, is the latest arrival time of the th delivery to the workstation; is the service time of the workstation ;

[0038] Based on the delivery arrival time window, calculate the delivery departure time window of the workstation, which is divided into the earliest departure time and the latest departure time of the delivery, and is represented by formula (5) and formula (6) respectively:

[0039] (5);

[0040] (6);

[0041] In the formula, is the earliest departure time of the th delivery to the workstation; is the latest departure time of the th delivery to the workstation;

[0042] Step S42: Division of the delivery workstation set based on time window intersection;

[0043] Based on the intersection relationship between the workstation service time windows, divide the workstations that need to perform material delivery in the current delivery cycle to form a delivery workstation set. The division method is as follows:

[0044] Set the workstation with the earliest delivery request in the set of all workstations as , mark the earliest departure time as , and the latest departure time as ; For each workstation except the workstation with the earliest delivery request removed , if the workstation current earliest departure time Less than the latest departure time , then classify the work station and the first work station into the same distribution work station set, and the expression is as shown in formula (7):

[0045] (7);

[0046] In the formula, is the th distribution work station set.

[0047] Furthermore, in step S5, determine the calculation method of the departure time of the distribution vehicle according to the specific number of distribution work stations of the distribution vehicle, and determine the calculation method of the distribution quantity of the work station materials based on the arrival time of the work station; specifically:[[]]

[0048] Step S51: Calculation method of the departure time of the distribution vehicle;

[0049] Select the distribution departure time window and the distribution arrival time window as the basis respectively to calculate the departure time of the distribution vehicle. Assume that the distribution vehicle at the th distribution cycle has a departure time of , and the calculation method of the departure time of the distribution vehicle is divided into two cases: step S511 and step S512:

[0050] Step S511: When the number of work stations to be distributed is equal to 1, assume that the distribution work station is work station , and the calculation method of the departure time of the distribution vehicle is as shown in formula (8), that is, the earliest departure time of work station at the th distribution cycle;

[0051] (8);

[0052] In the formula, is the departure time of the distribution vehicle at the th distribution cycle; is a certain distribution cycle, belonging to the set of all distribution cycles ; is the set of all distribution cycles; is a 0-1 variable: if the distribution vehicle at the th distribution cycle is responsible for distributing materials to work station then is 1, otherwise is 0; is a 0-1 variable: if the th delivery of work station belongs to the th distribution cycle then is 1, otherwise is 0;

[0053] Step S512: When the number of workstations to be delivered is greater than 1, taking the latest arrival time of the delivery vehicle as the calculation basis, the departure time of the delivery vehicle is obtained by the backward deduction method; specifically:

[0054] Step S5121, for each workstation , according to the workstations in the current delivery cycle the latest arrival time of the th delivery is sorted in descending order. Let the workstation number with the largest value among the latest arrival time values be , the second largest workstation number be , then the arrival time of the th delivery cycle of workstation is expressed as shown in formula (9):

[0055] (9);

[0056] In the formula, is the latest arrival time of the delivery vehicle responsible for delivering workstation in the th delivery cycle; is the distance between workstation and workstation ; is the service time of workstation ;

[0057] Step S5122, remove workstation , update the original workstation to the new workstation , select the next workstation corresponding to workstation from the descending order of the latest arrival time as the new workstation , and execute formula (9) again until the arrival times of all delivery workstations of the delivery vehicle are calculated;

[0058] Step S5123, based on the finally calculated arrival time of the th delivery cycle of workstation , determine the departure time of the delivery vehicle in the th delivery cycle, which is expressed as shown in formula (10):

[0059] (10);

[0060] Wherein, is the distance between the warehouse and station b;

[0061] Step S52: Calculation method for the quantity of material distribution at the station;

[0062] When the delivery vehicle at the departure time of the th delivery cycle is determined, by tracking and recording the delivery frequencies of each station, identify that the delivery to station in the th delivery cycle belongs to the th delivery of station Then, based on the driving distance between stations and the driving speed of the delivery vehicle, further determine the arrival time of the th delivery at station ; The moment when station makes the th material request is calculated by formula (11):

[0063] (11);

[0064] Wherein: is the moment when station makes the th material request;

[0065] The specific quantity of the remaining materials at the arrival moment of the th delivery at station is calculated by formula (12):

[0066] (12);

[0067] Wherein: is the specific quantity of the remaining materials at the arrival moment of the th delivery at station ;

[0068] Finally, the number of remaining available bins at the arrival moment of the th delivery at station and the number of bins for the th delivery at station are calculated by formula (13) and formula (14) respectively:

[0069] (13);

[0070] (14);

[0071] Wherein, is the work station The remaining available bin quantity at the arrival time of the is the work station The number of bins for the

[0072] Furthermore, in step S6, based on the set of distribution workstations determined in step S4 and the calculation methods of the departure time of the distribution vehicle and the quantity of material distribution at the workstations determined in step S5, the loading state of the distribution vehicle is calculated and evaluated in real time using the forward deduction method, and the material distribution path is planned to obtain the distribution planning result under the time-varying characteristics of the current distribution cycle; specifically:

[0073] Step S61: Let the set of workstations to be distributed be , the number of workstations in the set of distribution workstations is , the set of all distribution vehicles is , the material distribution path of the distribution vehicle is , the maximum loading volume of the distribution vehicle is , and the departure time of the distribution vehicle is ; Suppose that when starting the path planning, the workstation is the first distribution workstation and the initial number is 1, the distribution vehicle is the first distribution vehicle and the initial number is 1, and at the same time, the initial material distribution path of the distribution vehicle is empty;

[0074] Step S62: Add the workstation to the material distribution path of the distribution vehicle , and calculate the corresponding departure time of the distribution vehicle and the current loading volume of the distribution vehicle;

[0075] Step S63: Then evaluate the loading state of the distribution vehicle:

[0076] Step S631: If and : When the current loading volume of the distribution vehicle does not exceed the maximum loading volume of the distribution vehicle and there is a next workstation, add the workstation to the material distribution path of the distribution vehicle , update to , and go to step S62;

[0077] Step S632: If and : If the current loading volume of the distribution vehicle does not exceed the maximum loading volume of the distribution vehicle and there is no next work station, record the distribution vehicle 's complete material distribution path, and obtain the material distribution paths of all distribution vehicles in the current distribution cycle;

[0078] Step S633: If and : If the current loading volume of the distribution vehicle exceeds the maximum loading volume of the distribution vehicle and there is a next work station, then record the remaining work stations in the current material distribution path except for the work station as the complete material distribution path of the distribution vehicle , and update to , use the work station as the distribution work station of the distribution vehicle , update to , and go to step S6;

[0079] Step S634: If and : If the current loading volume of the distribution vehicle exceeds the maximum loading volume of the distribution vehicle and there is no next work station, then record the remaining work stations in the current material distribution path except for the work station as the complete material distribution path of the distribution vehicle , and use the work station as the distribution work station of the distribution vehicle , and obtain the material distribution paths of all distribution vehicles in the current distribution cycle.

[0080] Furthermore, in step S7, based on the distribution planning result under the time-varying characteristics of the current distribution cycle obtained in step S6, update the service time window and distribution work station set of the next distribution cycle according to step S4, and through comparing the service time window of the distribution work station of the distribution vehicle at the end of the current distribution cycle with the earliest departure time of the next distribution cycle, perform collaborative optimization on the distribution work station of the distribution vehicle at the end of the current distribution cycle and the adjacent distribution work station set; specifically:

[0081] Step S61, assume that the distribution vehicle that departs from the warehouse latest in the current distribution cycle is distribution vehicle , and all the work stations responsible for distribution by distribution vehicle are combined into work station group , and the number of work stations in work station group is ;

[0082] Step S62, define that in the current distribution cycle, the latest departure time of each work station in the work station group responsible for distribution by distribution vehicle is , the earliest departure time for the next delivery cycle is ;

[0083] Step S63, when , then the delivery vehicle The work station group responsible for delivery is collaboratively optimized with the adjacent delivery work station set;

[0084] Step S64, if the latest departure time of each work station in the work station group responsible for delivery is greater than the earliest departure time of the next delivery cycle The work station group responsible for delivery, then each work station in it is merged into the next delivery cycle for joint delivery, and the work station data is updated; otherwise, the original material delivery path is maintained.

[0085] Furthermore, in step S8, based on the work station information and delivery vehicle information collected in step S1, a delivery optimization model under a time-varying work station set is constructed; specifically:

[0086] Step S81: Construction of the objective function; considering the delivery vehicle startup cost, delivery vehicle driving cost, line-side inventory cost, time window penalty cost, and line-side inventory penalty cost in the total cost, with the total objective of minimizing the total cost of material delivery;

[0087] Step S811: The calculation formula for the delivery vehicle startup cost is shown in formula (15):

[0088] (15);

[0089] In the formula, is the delivery vehicle startup cost; is the single startup cost of the delivery vehicle; is the total number of startups of the delivery vehicle;

[0090] Step S812: The calculation formula for the delivery vehicle driving cost is shown in formula (16):

[0091] (16);

[0092] In the formula, is the delivery vehicle driving cost; is the unit driving cost of the delivery vehicle; is a 0-1 variable: if the delivery vehicle in the th delivery cycle travels from work station to work station If is 1, otherwise is 0; is the workstation and the workstation the distance between;

[0093] Step S813: The calculation formula of the in-line inventory cost is shown in Formula (17):

[0094] (17);

[0095] In the formula, is the in-line inventory cost; is the in-line inventory unit volume cost; is the workstation the number of materials when the is the workstation the volume of the bin, unit m 3 ; is the time when the materials are sent to the workstation for the

[0096] Step S814: The calculation formula of the time window penalty cost is shown in Formula (18):

[0097] (18);

[0098] In the formula, is the time window penalty cost; is the early arrival penalty cost; is the late arrival penalty cost;

[0099] Step S815: The calculation formula of the in-line inventory penalty cost is shown in Formula (19):

[0100] (19);

[0101] In the formula, in-line inventory penalty cost; is the in-line inventory unit volume penalty cost;

[0102] To minimize the total material distribution cost, the objective function is constructed as shown in Formula (20):

[0103] (20);

[0104] In the formula: is the total distribution cost;

[0105] Step S82: Construction of constraint conditions;

[0106] Given the following constraints based on the operation conditions of the optimization process that conform to the actual production operation:

[0107] Constraint 1: , indicating that the reorder point values of each work station shall not exceed the value range;

[0108] Constraint 2: , indicating that each delivery vehicle starts from the warehouse and returns to the warehouse after completing the delivery task; in the formula, represents the delivery vehicle traveling from the warehouse to the work station in the th delivery cycle; represents the delivery vehicle traveling from the work station to the warehouse in the th delivery cycle;

[0109] Constraint 3: , indicating that the material delivery path needs to be connected, that is, when the delivery vehicle arrives at the work station , it must leave from the work station ; in the formula, represents the delivery vehicle traveling from the work station to the work station in the th delivery cycle; represents the delivery vehicle traveling from the work station to the work station in the th delivery cycle;

[0110] Constraint 4: , indicating that in each delivery cycle, there is exactly 0 or 1 delivery vehicle serving the work station , and it cannot be split into two or more vehicles;

[0111] Constraint 5: , indicating that the total loading volume of each delivery of the delivery vehicle shall not be greater than the maximum loading volume of the delivery vehicle; in the formula, is the maximum loading volume of the delivery vehicle, with the unit of m 3 ;

[0112] Constraint 6: , indicating that the number of in-line bins at each work station shall not exceed the maximum number of bins in the corresponding in-line inventory;

[0113] Constraint 7: , it indicates that between two workstations served by the same delivery vehicle, there should be a sequential order. Only after delivering to the previous workstation can the delivery to the next workstation be carried out; where, is the moment when the material is delivered to workstation in the th delivery cycle; is the moment when the material is delivered to workstation in the th delivery cycle; is an infinitely large value;

[0114] Constraint 8: , which means that the material delivery of workstation should be completed within the specified time window.

[0115] Furthermore, in step S9, based on the delivery optimization model under the time-varying workstation set constructed in step S8, the inner genetic algorithm is used to solve the delivery optimization model, and steps S5 - S7 are repeated until all material delivery tasks within the specified delivery time range in S1 are completed, and the overall delivery planning scheme under the reorder point of the current workstation is output; specifically:

[0116] The inner genetic algorithm is a part of the double-layer genetic algorithm, mainly responsible for, for each set of reorder point schemes, according to the delivery optimization model under the time-varying workstation set, solving the corresponding material delivery route, the departure time of the delivery vehicle, and the total delivery cost , and in the solution process, the coding method selects the real number coding method, and the genetic operations include the roulette wheel selection strategy, the two-point crossover strategy, and the two-point mutation strategy;

[0117] The basic idea of the solution by the inner genetic algorithm is: based on the workstation reorder point combination , within the time range for executing material delivery preset in S1, according to the intersection between the workstation service time windows, determine the time-varying workstation set during each delivery task execution, and use the inner genetic algorithm to plan the material delivery route. Through continuous iterative calculation, select the scheme with the minimum total delivery cost as the optimal material delivery route for the delivery cycle. At the same time, update the workstation information in real time after the planning of each delivery cycle is determined, and solve for the next delivery cycle until all delivery tasks within the preset time range for executing material delivery are completed, and output the optimal material delivery routes, the departure times of the delivery vehicles, the various delivery costs, and the total delivery cost for each delivery cycle under the workstation reorder point combination .

[0118] Further, in step S10, record the overall material distribution planning scheme obtained in step S9, update the reorder point of each work station using the outer genetic algorithm, and repeat steps S4 - S9 until the maximum number of iterations is reached, and output the optimal distribution scheme including the combination of reorder points of each work station and the distribution plan; specifically:

[0119] The outer genetic algorithm is a part of the double - layer genetic algorithm and is mainly responsible for adjusting the combination of reorder points of each work station , as the basis of the distribution plan, the real - number coding method is selected for the coding method during the solution process, and the genetic operations include the ternary tournament selection strategy, the uniform crossover strategy, and the multi - point mutation strategy;

[0120] The basic idea of the solution of the outer genetic algorithm is: in each iteration, according to the value range of the reorder point of each work station , update the combination of reorder points of each work station using genetic operations , and use the inner genetic algorithm to determine the corresponding distribution plan, record the optimal combination of reorder points of each work station and the corresponding material distribution planning scheme in each iteration, and at the same time compare the total distribution cost of the obtained distribution plan with the optimal total distribution cost in the previous iteration. If the distribution plan under the new reorder point combination can reduce the total distribution cost, then update the current optimal reorder point combination to the new reorder point of the work station, and repeat the iteration until the set maximum number of iterations is satisfied. Finally, obtain the optimal combination of reorder points of each work station in all iterations and the optimal material distribution path, the departure time of the distribution vehicle, various distribution costs, and the total distribution cost of each distribution cycle of the corresponding distribution plan, and use this as the final distribution plan.

[0121] Compared with the prior art, the present invention has the following advantages:

[0122] 1. By combining the work - station information in the production workshop and the distribution vehicle information, the present invention limits the value range of the reorder point of each work station and optimizes and solves the reorder point of each work station based on this. It not only ensures the dynamic balance between supply and demand of the production line, but also enhances the visualization level and planning ability of the distribution process. At the same time, by combining the work - station information in the production workshop and the distribution vehicle information, the set range of the reorder point of each work station can be flexibly adjusted, so as to intelligently adapt to different production workshops and has stronger adaptability.

[0123] 2. Based on the time - varying characteristics of the distribution work - station set and according to the cross - relationship between the service time windows of each work station, the present invention dynamically divides the distribution work - station set of each distribution cycle. This enables the material distribution plan to flexibly respond to the real - time needs of work stations in different cycles, ensures the continuity of the production process, and realizes a high degree of consistency between material distribution and production requirements.

[0124] 3. By comprehensively coordinating the dynamic changes and the influencing relationships among the departure time, the delivery quantity, and the route planning in the delivery process, the present invention determines the specific delivery route, and based on the working station service time window, coordinates and optimizes the delivery working stations of the end delivery vehicle and the adjacent time windows. This process effectively improves the dynamic response ability and flexibility of the delivery process, ensures more accurate delivery decisions, and helps to improve the overall production efficiency of the workshop.

[0125] 4. By comprehensively considering the delivery vehicle driving and the in-line inventory management process, and combining constraints such as the full-case feeding strategy and the working station service time window, the present invention constructs a delivery optimization model under a time-varying working station set. Through a double-layer genetic algorithm, the complex decision-making problem is decomposed into two interrelated and mutually supportive optimization levels, realizing the joint optimization of the working station reorder point and the material delivery plan. It not only realizes a high degree of fit between the material delivery and the actual production process, ensures the comprehensive and flexible planning of the delivery route, but also makes the overall planning process more intelligent and scientific through the combination of dynamic programming and intelligent algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0126] Figure 1 is a schematic diagram of the overall framework of the intelligent material delivery method for a production workshop based on a time-varying working station set in the present invention;

[0127] Figure 2 is an optimal solution iteration diagram when using a double-layer genetic algorithm to solve the problem in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0128] The present invention will be further described in detail below with reference to the drawings and embodiments.

[0129] Please refer to Figure 1 , the technical solution provided by the present invention: an intelligent material delivery method for a production workshop based on a time-varying working station set, including the following steps:

[0130] Step S1: Collect the working station information and the delivery vehicle information of the production workshop, and determine the time range for which material delivery needs to be performed;

[0131] Step S2: Based on the working station information and the delivery vehicle information collected in Step S1, limit the value range of the working station reorder point;

[0132] Step S3: Based on the value range of the working station reorder point set in Step S2, randomly set a reasonable working station reorder point for each working station, and after combination, form a working station reorder point combination;

[0133] Step S4: Based on the working station reorder point combination and the working station information collected in Step S1, calculate the working station service time window, and determine the delivery working station set for the current delivery cycle based on the intersection relationship of the working station service time windows;

[0134] Step S5: Determine the calculation method for the departure time of the delivery vehicle based on the specific number of delivery stations of the delivery vehicle, and determine the calculation method for the quantity of material delivery to the stations based on the arrival time at the stations;

[0135] Step S6: Based on the set of delivery stations determined in Step S4 and the calculation methods for the departure time of the delivery vehicle and the quantity of material delivery to the stations determined in Step S5, use the forward deduction method to calculate and evaluate the loading status of the delivery vehicle in real time, plan the material delivery route, and obtain the delivery planning result under the time-varying characteristics of the current delivery cycle;

[0136] Step S7: Based on the delivery planning result under the time-varying characteristics of the current delivery cycle obtained in Step S6, update the service time window of the stations and the set of delivery stations for the next delivery cycle according to Step S4. By comparing the service time window of the delivery stations of the delivery vehicle at the end of the current delivery cycle with the earliest departure time of the next delivery cycle, optimize the coordination between the delivery stations of the delivery vehicle at the end of the current delivery cycle and the adjacent set of delivery stations;

[0137] Step S11: Based on the station information and delivery vehicle information collected in Step S1, construct a delivery optimization model under the time-varying set of stations;

[0138] Step S14: Based on the delivery optimization model under the time-varying set of stations constructed in Step S8, use the inner genetic algorithm to solve the delivery optimization model, and repeat Step S5 - Step S7 until all material delivery tasks within the time range determined in Step S1 for which material delivery needs to be performed are completed, and output the overall delivery planning scheme under the current reorder point of the stations;

[0139] Step S17: Record the overall delivery planning scheme obtained in Step S9, use the outer genetic algorithm to update the reorder point of the stations, and repeat Step S4 - Step S9 until the maximum number of iterations is reached, and output the optimal delivery scheme including the combination of reorder points of the stations and the delivery planning.

[0140] Further, in Step S1, collect the station information and delivery vehicle information of the production workshop, and determine the time range for which material delivery needs to be performed; specifically:

[0141] The station information includes: the number of stations, the driving distance between stations, the available time of unit material at the stations, the station service time, the volume of the station bin, the maximum number of bins in the in-line inventory at the stations, and the loadable quantity of the unit bin at the stations;

[0142] The delivery vehicle information includes: the driving speed of the delivery vehicle and the maximum loading volume of the delivery vehicle;

[0143] The time range for which material delivery needs to be performed is preset.

[0144] Further, in step S2, based on the station information and distribution vehicle information collected in step S1, the value range of the reorder point for each station is limited; specifically:

[0145] Set the minimum value level of the reorder point for each station as shown in formula (1):

[0146] (1);

[0147] Set the maximum value level of the reorder point for each station as shown in formula (2):

[0148] (2);

[0149] In the formula: is the reorder point for station ; is the minimum value level of the reorder point for the station; is for station the maximum value level of the reorder point; * is the multiplication sign; is the station number, belonging to the set of all stations ; is the set of all stations; is the number of the dispatched distribution vehicle, belonging to the set of all distribution vehicles ; is the set of all distribution vehicles; is the distance between the warehouse and station , unit: m; is the driving speed of the distribution vehicle, unit: m / min; is for station the available time of unit material at the station, unit: s; is for station the maximum number of bins of the in-line inventory at the station; is for station the loadable material quantity per bin at the station.

[0150] Further, in step S3, based on the value range of the reorder point for each station set in step S2, a reasonable reorder point is randomly set for each station, and after combination, it becomes the reorder point combination for each station; specifically:

[0151] For each station , based on the value range of the reorder point for the station , randomly select an integer within the value range of the reorder point as the reorder point for station , and combine the reorder points of each station to form the reorder point combination for each station .

[0152] Further, in step S4, based on the station reorder point combination and the station information collected in step S1, calculate the station service time window, and determine the distribution station set of the current distribution cycle based on the intersection relationship of the station service time windows; specifically:

[0153] Step S41: Calculation of the station service time window;

[0154] The station service time window consists of the distribution arrival time window and the distribution departure time window. According to the time-varying characteristics, determine the setting standard of the station service time window, and the specific calculation method is as follows:

[0155] The earliest arrival time of the distribution is set as shown in formula (3):

[0156] (3);

[0157] In the formula, is the earliest arrival time of the th distribution to the station; is the moment when the th time the material is sent to the station ; is the quantity of materials after the th distribution to the station arrives; is a certain distribution of the station , belonging to the set of material distribution times of the station ; is the set of material distribution times of the station ;

[0158] The latest arrival time of the distribution is set as shown in formula (4):

[0159] (4);

[0160] In the formula, is the latest arrival time of the th distribution to the station; is the service time of the station ;

[0161] Based on the distribution arrival time window, calculate the distribution departure time window of the station, which is divided into the earliest departure time and the latest departure time of the distribution, and is represented by formula (5) and formula (6) respectively:

[0162] (5);

[0163] (6);

[0164] In the formula, is the work station The earliest departure time of the is the work station The latest departure time of the

[0165] Step S42: Division of the distribution work station set based on the time window intersection;

[0166] Based on the intersection relationship between the work station service time windows, divide the work stations that need to perform material distribution in the current distribution cycle to form a distribution work station set. The division method is as follows:

[0167] Set the work station that initiates the distribution request earliest in the set of all work stations as , mark the earliest departure time as , and the latest departure time as ; For each work station except the work station that initiates the distribution request earliest , if the current earliest departure time of the work station is less than the latest departure time , then classify the work station into the same distribution work station set as the first work station. The expression is as shown in formula (7):

[0168] (7);

[0169] In the formula, is the th distribution work station set.

[0170] Furthermore, in step S5, according to the specific number of work stations served by the distribution vehicle, determine the calculation method of the distribution vehicle departure time, and based on the work station arrival time, determine the calculation method of the work station material distribution quantity; specifically:

[0171] Step S51: Calculation method of the distribution vehicle departure time;

[0172] Respectively select the distribution departure time window and the distribution arrival time window as the basis to calculate the distribution vehicle departure time. Assume that the distribution vehicle departs at in the th distribution cycle. The calculation method of the distribution vehicle departure time is divided into two cases: step S511 and step S512:

[0173] Step S511: When the number of work stations to be distributed is equal to 1, assume that the distribution work station is the work station , the calculation method of the departure time of the distribution vehicle is shown in formula (8), which is the workstation the earliest departure time of the

[0174] th distribution cycle;

[0175] In the formula, is the departure time of the distribution vehicle in the th distribution cycle; is a certain distribution cycle, belonging to the set of all distribution cycles ; is the set of all distribution cycles; is a 0-1 variable: if in the th distribution cycle, the distribution vehicle is responsible for delivering materials to the workstation then is 1, otherwise is 0; is a 0-1 variable: if the th delivery of the workstation belongs to the th distribution cycle, then is 1, otherwise is 0;

[0176] Step S512: When the number of workstations to be distributed is greater than 1, taking the latest arrival time of the distribution vehicle as the calculation benchmark, use the backward deduction method to obtain the departure time of the distribution vehicle; specifically:

[0177] Step S5121, for each workstation , according to the latest arrival time of the th delivery of the current distribution cycle at the workstation value, perform a descending order sorting. Let the workstation number with the largest value among the latest arrival time values be , the second largest workstation number be , then the expected arrival time of the workstation in the th distribution cycle is expressed as shown in formula (9):

[0178] (9);

[0179] In the formula, is the latest arrival time of the distribution vehicle in the th distribution cycle responsible for delivering materials to the workstation ; is the workstation and workstation The distance between For workstation service time;

[0180] Step S5122, remove the workstation , the original workstation Update to new workstation , from the latest arrival time Select the workstation in descending order The corresponding next station is used as the new station , execute formula (9) again until the arrival time of all delivery vehicles at all delivery stations of the delivery vehicle is calculated;

[0181] Step S5123, based on the final calculated workstation No. Delivery cycle arrival time , determine the delivery vehicle In the Departure time of delivery cycle , expressed as formula (10):

[0182] (10);

[0183] In the formula, is the distance between the warehouse and workstation b;

[0184] Step S52: a method for calculating the quantity of material delivered to a workstation;

[0185] When delivery vehicle In the Departure time of delivery cycle After confirmation, the delivery frequency of each station is tracked and recorded to identify the first Delivery cycle to workstation The delivery belongs to the workstation The number of deliveries is then further determined based on the driving distance between workstations and the speed of the delivery vehicle. No. The arrival time of the delivery ; Workstation No. The time of initiating the material request is calculated by formula (11):

[0186] (11);

[0187] Where: For workstation No. The moment when the material request is initiated for the first time;

[0188] Workstation The Specific quantity of the remaining materials at the arrival moment of the

[0189] th delivery is calculated by formula (12):

[0190] In the formula: is the workstation The Specific quantity of the remaining materials at the arrival moment of the

[0191] Finally, the number of remaining available bins at the arrival moment of the th delivery at workstation and the number of bins for the th delivery at workstation are calculated by formula (13) and formula (14) respectively: The th delivery are calculated by formula (13) and formula (14) respectively:

[0192] (13);

[0193] (14);

[0194] In the formula, is the number of remaining available bins at the arrival moment of the th delivery at workstation, and is the workstation The number of bins for the

[0195] Furthermore, in step S6, based on the set of delivery workstations determined in step S4 and the calculation methods of the departure time of the delivery vehicle and the quantity of material distribution at the workstation determined in step S5, the loading state of the delivery vehicle is calculated and evaluated in real time using the forward deduction method, and the material distribution path is planned to obtain the distribution planning result under the time-varying characteristics of the current distribution cycle; specifically:

[0196] Step S61, let the set of workstations to be delivered be , the number of workstations in the set of delivery workstations be , the set of all delivery vehicles be , the material distribution path of delivery vehicle be , the maximum loading volume of the delivery vehicle be , and the departure time of the delivery vehicle be ; Suppose that when starting the path planning, workstation is the first delivery workstation and the initial number is 1, and delivery vehicle Is the first delivery vehicle and the initial number Is 1, and the initial material delivery path of the delivery vehicle is empty;

[0197] Step S62, add the work station To the material delivery path of the delivery vehicle And calculate the corresponding departure time of the delivery vehicle And the current loaded volume of the delivery vehicle ; ;

[0198] Step S63, then evaluate the loading status of the delivery vehicle:

[0199] Step S631: If And : The current loaded volume of the delivery vehicle does not exceed the maximum loaded volume of the delivery vehicle and there is a next work station, then add the work station To the material delivery path of the delivery vehicle Update To And go to step S62;

[0200] Step S632: If And : The current loaded volume of the delivery vehicle does not exceed the maximum loaded volume of the delivery vehicle and there is no next work station, then record the complete material delivery path of the delivery vehicle To obtain the material delivery paths of all delivery vehicles in the current delivery cycle;

[0201] Step S633: If And : The current loaded volume of the delivery vehicle exceeds the maximum loaded volume of the delivery vehicle and there is a next work station, then record the remaining work stations in the current material delivery path except the work station As the complete material delivery path of the delivery vehicle At the same time, update To Take the work station As the delivery work station of the delivery vehicle Update To And go to step S6;

[0202] Step S634: If And : The current loaded volume of the delivery vehicle exceeds the maximum loaded volume of the delivery vehicle and there is no next work station, then record the remaining work stations in the current material delivery path except the work station As the complete material delivery path of the delivery vehicle At the same time, take the work station As a delivery vehicle at the delivery station, the material delivery paths of all delivery vehicles in the current delivery cycle are obtained.

[0203] Furthermore, in step S7, based on the delivery planning result under the time-varying characteristics of the current delivery cycle obtained in step S6, update the station service time window and the delivery station set for the next delivery cycle according to step S4. By comparing the service time window of the delivery station of the last delivery vehicle in the current delivery cycle with the earliest departure time of the next delivery cycle, coordinate and optimize the delivery station of the last delivery vehicle in the current delivery cycle and the adjacent delivery station set; specifically:

[0204] Step S61, assume that the delivery vehicle with the latest departure from the warehouse in the current delivery cycle is delivery vehicle , and all the stations responsible for delivery by delivery vehicle are combined into a station group , and the number of stations in station group is ;

[0205] Step S62, define that in the current delivery cycle, the latest departure time of each station in the station group responsible for delivery by delivery vehicle is , and the earliest departure time of the next delivery cycle is ;

[0206] Step S63, when , then coordinate and optimize the station group responsible for delivery by delivery vehicle with the adjacent delivery station set;

[0207] Step S64, if the latest departure time of each station in the station group responsible for delivery by delivery vehicle is greater than the earliest departure time of the next delivery cycle, then merge each station in the station group responsible for delivery by delivery vehicle into the next delivery cycle for combined delivery and update the station data; otherwise, maintain the original material delivery path.

[0208] Furthermore, in step S8, based on the station information and delivery vehicle information collected in step S1, construct a delivery optimization model under the time-varying station set; specifically:

[0209] Step S81: Construction of the objective function; The start-up cost of the distribution vehicle, the driving cost of the distribution vehicle, the line-side inventory cost, the time-window penalty cost, and the line-side inventory penalty cost are considered in the total cost, and the total objective is to minimize the total cost of material distribution;

[0210] Step S811: The calculation formula for the start-up cost of the distribution vehicle is shown in formula (15):

[0211] (15);

[0212] In the formula, is the start-up cost of the distribution vehicle; is the single start-up cost of the distribution vehicle; is the total number of start-ups of the distribution vehicle;

[0213] Step S812: The calculation formula for the driving cost of the distribution vehicle is shown in formula (16):

[0214] (16);

[0215] In the formula, is the driving cost of the distribution vehicle; is the unit driving cost of the distribution vehicle; is a 0-1 variable: If the distribution vehicle in the th distribution cycle travels from station to station then is 1, otherwise is 0; is the distance between station and station ;

[0216] Step S813: The calculation formula for the line-side inventory cost is shown in formula (17):

[0217] (17);

[0218] In the formula, is the line-side inventory cost; is the unit volume cost of the line-side inventory; is the quantity of materials when the th delivery arrives at station ; is the bin volume of station , in m 3 ; is the th time of delivering materials to station ;

[0219] Step S814: The calculation formula for the time window penalty cost is shown in Equation (18):

[0220] (18);

[0221] In the formula, is the time window penalty cost; is the early arrival penalty cost; is the late arrival penalty cost;

[0222] Step S815: The calculation formula for the line-side inventory penalty cost is shown in Equation (19):

[0223] (19);

[0224] In the formula, is the line-side inventory penalty cost; is the line-side inventory penalty cost per unit volume;

[0225] To minimize the total material distribution cost, the objective function is constructed as shown in Equation (20):

[0226] (20);

[0227] In the formula: is the total distribution cost;

[0228] Step S82: Construction of constraint conditions;

[0229] Based on the operation conditions of the optimization process conforming to the actual production operation, the following constraint conditions are given:

[0230] Constraint condition 1: , indicating that the reorder point value of each work station shall not exceed the value range;

[0231] Constraint condition 2: , indicating that the distribution vehicle starts from the warehouse each time during distribution and returns to the warehouse after completing the distribution task; In the formula, indicates the distribution vehicle travels from the warehouse to the work station in the th distribution cycle; indicates the distribution vehicle travels from the work station to the warehouse in the th distribution cycle;

[0232] Constraint condition 3: , indicating that the material distribution path needs to be connected, that is, when the distribution vehicle arrives at the work station , it must leave from the work station ; In the formula, Represents the delivery vehicle In the th delivery cycle, it travels from workstation to workstation ; Represents the delivery vehicle In the th delivery cycle, it travels from workstation to workstation ;

[0233] Constraint four: , which means that in each delivery cycle, there is exactly 0 or 1 delivery vehicle serving workstation , and it cannot be split into two or more vehicles;

[0234] Constraint five: , which means that the total loading volume of each delivery of the delivery vehicle shall not be greater than the maximum loading volume of the delivery vehicle; where is the maximum loading volume of the delivery vehicle, with the unit of m 3 ;

[0235] Constraint six: , which means that the number of in-line bins at each workstation shall not exceed the maximum number of bins in the corresponding in-line inventory;

[0236] Constraint seven: , which means that between two workstations served by the same delivery vehicle, there should be a sequence. Only after delivering to the previous workstation can the delivery to the next workstation be executed; where is the time when the th delivery cycle delivers materials to workstation ; is the time when the th delivery cycle delivers materials to workstation ; is an infinitely large value;

[0237] Constraint eight: , which means that the material delivery of workstation should be completed within the specified time window.

[0238] Furthermore, in step S9, based on the delivery optimization model under the time-varying workstation set constructed in step S8, the inner genetic algorithm is used to solve the delivery optimization model, and steps S5 - S7 are repeated until all material delivery tasks within the delivery time range specified in S1 are completed, and the overall delivery planning scheme at the reorder point of the current workstation is output; specifically:

[0239] The inner genetic algorithm is a part of the double - layer genetic algorithm, mainly responsible for, for each set of reorder point solutions, according to the distribution optimization model under the time - varying workstation set, solving the corresponding material distribution path, the departure time of distribution vehicles, and the total distribution cost , and in the solving process, the real - number coding method is selected for the coding method, and the genetic operations include the roulette wheel selection strategy, the two - point crossover strategy, and the two - point mutation strategy;

[0240] The basic idea of the inner genetic algorithm for solving is: based on the workstation reorder point combination , within the time range for executing material distribution preset in S1, according to the intersection between workstation service time windows, determine the time - varying workstation set during each distribution task execution, and use the inner genetic algorithm to plan the material distribution path. Through continuous iterative calculation, select the solution with the minimum total distribution cost as the optimal material distribution path for the distribution cycle. At the same time, update the workstation information in real - time after the planning of each distribution cycle is determined, and solve for the next distribution cycle until all distribution tasks within the preset time range for executing material distribution are completed, and output the workstation reorder point combination for each distribution cycle's optimal material distribution path, the departure time of distribution vehicles, various distribution costs, and the total distribution cost under the workstation reorder point combination

[0241] Furthermore, in step S10, record the overall material distribution planning solution obtained in step S9, use the outer genetic algorithm to update the workstation reorder points, and repeat steps S4 - S9 until the maximum number of iterations is reached, and output the optimal distribution plan including the workstation reorder point combination and the distribution plan; specifically:

[0242] The outer genetic algorithm is a part of the double - layer genetic algorithm, mainly responsible for adjusting the workstation reorder point combination , as the basis for distribution planning. In the solving process, the real - number coding method is selected for the coding method, and the genetic operations include the ternary tournament selection strategy, the uniform crossover strategy, and the multi - point mutation strategy;

[0243] The basic idea of the outer genetic algorithm for solving is: in each iteration, according to the value range of the workstation reorder points , use genetic operations to update the workstation reorder point combination , and the inner genetic algorithm is used to determine the corresponding distribution plan. Record the optimal station reorder point combination and the corresponding material distribution plan in each iteration. At the same time, compare the total distribution cost of the obtained distribution plan with the optimal total distribution cost in the previous iteration. If the distribution plan under the new reorder point combination can reduce the total distribution cost, update the current optimal reorder point combination to the new station reorder point, and repeat the iteration until the set maximum number of iterations is met. Finally, obtain the optimal material distribution path, the departure time of the distribution vehicle, various distribution costs, and the total distribution cost in each distribution cycle of the optimal station reorder point combination and the corresponding distribution plan in all iterations, and use this as the final distribution plan.

[0244] The following takes the material distribution within a single shift of the indoor unit branch of a manufacturing enterprise as a specific example to illustrate the distribution planning effect of the invention method:

[0245] The working system of this enterprise is a four-shift three-operation system. The material distribution adopts the full-box feeding mode. The layout of the product assembly line is linear, including a total of 1 material warehouse and 14 stations, and each station corresponds to a material type. The distribution task of materials is carried out by distribution vehicles with unified models according to the hard time window standard. Due to the limitation of the workshop road, the speed of the distribution vehicle is constant at 83.33 m / min. The distribution vehicle can carry a maximum of 3 tooling carts, and the total maximum loading volume is 6.75 m 3 , and the service time of the distribution vehicle at each station is 2 minutes, and the loading time in the warehouse is 0.5 minutes / box. The cost parameters in the distribution process are: the single start-up cost of the distribution vehicle = 30 yuan / time; the driving cost = 0.1 yuan / m; the unit volume cost of the line-side inventory = 0.2 yuan / min; the early arrival penalty cost = 1000 yuan / min; the late arrival penalty cost = 1000 yuan / min; the unit volume penalty cost of the line-side inventory = 0.4 yuan / min.

[0246] At the same time, the following relevant assumptions are made for the research problem:

[0247] (1) Distribution-related assumptions: All distribution vehicles are of the same model, with a constant driving speed, a fixed single start-up cost of the distribution vehicle, and traffic jams and distribution vehicle failures during the distribution process are not considered.

[0248] (2) Station-related assumptions: The line-side inventory at each station is in a full inventory state at the beginning, and the service time and material consumption speed of each station are known. At the same time, in each distribution, the materials of the same station cannot be split for distribution.

[0249] (3) Material-related assumptions: The capacities of the delivery vehicles and the line-side inventory are both expressed in terms of bins. When using the materials, the materials in the bin can only be replaced during the next delivery after they are exhausted.

[0250] In addition to the above parameters, the remaining relevant parameters for each workstation are shown in Table 1 as follows: including the volume of the bins at each workstation , the number of items that can be loaded per bin , the maximum number of bins at the line side and the available time per unit of material ; The travel distances between workstations are shown in Table 2: showing the distances between workstations and between workstations and the material warehouse, where 0 represents the material warehouse and 1-14 represent each workstation.

[0251] Table 1 Workstation-related parameters

[0252]

[0253] Table 2 Travel distances between workstations

[0254]

[0255] The double-layer genetic algorithm is used to solve the above example, and the parameter settings are divided into the settings of the upper-layer genetic algorithm and the lower-layer genetic algorithm. Among them, the parameters of the upper-layer genetic algorithm are: the population size U N is 50, the maximum number of iterations U G is 200, the crossover probability U PM is 0.9, the mutation probability U PC is 0.1; The parameter settings of the lower-layer genetic algorithm are: the population size L N is 50, the maximum number of iterations L G is 50, the crossover probability L PM is 0.85, the crossover probability L PC is 0.05.

[0256] See the algorithm iteration graph in Figure 2 , as the number of algorithm iterations gradually increases, the total delivery cost shows a decreasing trend. After 157 iteration cycles, the optimal solution of the delivery plan is obtained. By decoding the optimal solution of the delivery plan, the calculation results are shown in Table 3.

[0257] Table 3 Optimal solution of algorithm iteration

[0258]

[0259] The calculation results in Table 3 consist of three parts: The uppermost part shows the optimal station reorder point combination, ensuring that material distribution can meet the material requirements of each station at a relatively low cost; the middle part shows the material distribution route arrangement and the departure time of the distribution vehicle for each cycle based on the station reorder point. Each material distribution is sorted by station and executed according to the departure time in the brackets. For example, 0-6-4-0 (68.918) means that the distribution vehicle departs from the warehouse at 68.918 minutes, delivers materials to stations 6 and 4 in sequence, and returns to the warehouse after the delivery. From the results obtained in this part, it can be seen that based on the corresponding station reorder point, the material distribution within a shift can be split into 14 distribution cycles, including a total of 27 distribution tasks, and the material distribution station sets of the distribution vehicle in each cycle show time-varying characteristics, highlighting the flexibility and effectiveness of the algorithm in coping with actual demand changes; the lowermost part shows that the total cost of material distribution within a shift is 4,173.78 yuan, the number of distribution vehicles required for distribution is 4, and from the fact that the value of the time window penalty cost in the cost component is 0, it can be seen that each distribution can complete the distribution task according to the distribution requirements within the specified time, meeting each constraint condition and achieving the optimization of the distribution plan.

[0260] To illustrate the superiority of the method, the results in the above table are compared and analyzed with the traditional material distribution strategy adopted by this workshop. Currently, the material distribution strategy of this workshop adopts a fixed-station distribution mode. Specifically: The production workshop is equipped with a total of 5 distribution vehicles, and each distribution vehicle is responsible for the material distribution task of a fixed station. The station allocation remains unchanged. At the same time, the reorder point of each station is set to the material loading capacity of the corresponding station's unit bin. When a material distribution request is sent from a certain station, the distributor responsible for the corresponding station will comprehensively check the line-side inventory situation of each station under his responsibility, and determine the material replenishment quantity of each station according to the overall inventory situation. Finally, a unified material distribution is implemented based on the time when the distribution request is initiated.

[0261] In addition, since the traditional distribution strategy does not consider the time window correlation between stations when setting the distribution departure time, and only executes the material distribution according to the time when the earliest distribution request is initiated, the time window penalty cost is not considered in the comparison. The final comparison results are shown in Table 4. The results on the left are the traditional material distribution strategy, and the results on the right are the material distribution strategy in the present invention.

[0262] Table 4 Comparison of Results under Two Distribution Strategies

[0263]

[0264] As can be seen from the above table, the present invention shows significant superiority compared with traditional strategies. In terms of the driving of distribution vehicles, by optimizing the transportation route and the scheduling of distribution vehicles, the present invention reduces the startup cost and driving cost of distribution vehicles by 12.90% and 8.72% respectively, and successfully reduces the number of distribution vehicles required for distribution, significantly improving the transportation efficiency. In terms of line-side inventory management, although the penalty cost of line-side inventory has increased slightly, compared with the substantial 21.12% decrease in line-side inventory cost, the advantages of the present invention in inventory management are more prominent. Through the dual optimization of the driving process of distribution vehicles and the line-side inventory management process, the total cost of material distribution has decreased from 4,746.7 yuan to 4,173.78 yuan, a reduction of 12.07%, fully demonstrating that the present invention can effectively respond to the fluctuations of production demand by flexibly adjusting the material distribution plan, and ultimately help enterprises achieve the purpose of cost reduction and efficiency improvement, showing high adaptability and flexibility.

[0265] Overall, by setting the reorder point of workstations, considering the dynamic coordination of distribution decision-making elements, combining with time-varying workstation sets, and aiming at minimizing the total distribution cost, the present invention uses a two-layer genetic algorithm to jointly optimize the setting problem of the reorder point of workstations and the corresponding material distribution plan. While reducing the material distribution cost, the distribution plan can meet the dynamic needs among workstations, improving the efficiency and dynamics in the distribution process. In addition, by combining time-varying characteristics with actual production data, the present invention can adapt to different production situations and application scenarios through intelligent decision-making, not only meeting the multiple constraint conditions brought by the complex workshop environment, but also enhancing the fit between material distribution and the production process, bringing new perspectives and methods to the research of workshop material distribution.

[0266] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent material distribution in a production workshop based on a time-varying workstation set, characterized in that: The following steps are involved: Step S1: Collect the workstation information and delivery vehicle information of the production workshop to determine the time range in which material delivery needs to be executed; Step S2: Based on the workstation information and delivery vehicle information collected in step S1, the value range of the workstation reorder point is limited; Step S3: Based on the workstation reorder point value range set in step S2, a reasonable workstation reorder point is randomly set for each workstation, and the combination becomes a workstation reorder point combination; Step S4: Based on the workstation reorder point combination and the workstation information collected in step S1, the workstation service time window is calculated, and the delivery workstation set of the current delivery cycle is determined based on the cross relationship of the workstation service time windows; Step S5: Determine a method for calculating the departure time of the delivery vehicle based on the specific number of delivery stations of the delivery vehicle, and determine a method for calculating the number of materials delivered to the station based on the arrival time of the station; Step S6: Based on the set of delivery stations determined in step S4 and the calculation method of the departure time of the delivery vehicle and the quantity of material delivered to the station determined in step S5, the forward method is used to calculate and evaluate the loading status of the delivery vehicle in real time, plan the material delivery path, and obtain the distribution planning result under the time-varying characteristics of the current delivery cycle; Step S7: Based on the distribution planning result under the time-varying characteristics of the current distribution cycle obtained in step S6, the service time window of the station and the distribution station set of the next distribution cycle are updated according to step S4, and the distribution station of the end distribution vehicle of the current distribution cycle is collaboratively optimized with the adjacent distribution station set by comparing the service time window of the distribution station of the end distribution vehicle of the current distribution cycle with the earliest departure time of the next distribution cycle; Step S8: Based on the workstation information and delivery vehicle information collected in step S1, a delivery optimization model under a time-varying workstation set is constructed; Step S9: Based on the distribution optimization model under the time-varying workstation set constructed in step S8, the distribution optimization model is solved by using the inner genetic algorithm, and steps S5 to S7 are repeated until all material distribution tasks within the time range determined in step S1 to be required for material distribution are completed, and the total distribution planning scheme under the current workstation re-order point is output; Step S10: Record the overall distribution plan obtained in step S9, use the outer genetic algorithm to update the workstation reorder point, repeat steps S4-S9 until the maximum number of iterations is reached, and output the optimal distribution plan including the workstation reorder point combination and distribution planning.

2. According to claim 1, a production workshop intelligent material distribution method based on a time-varying workstation set is characterized by: In step S1, the workstation information and delivery vehicle information of the production workshop are collected to determine the time range for material delivery; specifically: Workstation information includes: the number of workstations, the travel distance between workstations, the available time of materials per workstation, the service time of the workstation, the volume of the workstation material box, the maximum number of material boxes in the line inventory of the workstation, and the loadable number of material boxes per workstation; The delivery vehicle information includes: the driving speed of the delivery vehicle, the maximum loading volume of the delivery vehicle; The time frame within which material delivery needs to be performed is pre-set.

3. According to claim 2, a production workshop intelligent material distribution method based on time-varying workstation sets is characterized by: In step S2, based on the workstation information and delivery vehicle information collected in step S1, the value range of the workstation reorder point is limited; specifically: The minimum value level of the reorder point of the workstation is set as shown in formula (1): （1）； The maximum value level of the reorder point of the workstation is set as shown in formula (2): （2）； Where: For workstation reorder point; is the minimum reorder point level of the workstation; For workstation The maximum value level of the reorder point; * is the multiplication sign; Is the station number, belonging to the collection of all stations ; is the collection of all workstations; The number of the dispatched delivery vehicle, belonging to the collection of all delivery vehicles ; It is the collection of all delivery vehicles; For warehouse and workstation The distance between them, unit: m; is the driving speed of the delivery vehicle, unit: m / min; For workstation The usable time of a unit material, unit: s; For workstation Maximum number of bins for line-side inventory; For workstation The amount of material that can be loaded into a unit container.

4. According to claim 3, a production workshop intelligent material distribution method based on a time-varying workstation set is characterized in that: In step S3, based on the workstation reorder point value range set in step S2, a reasonable workstation reorder point is randomly set for each workstation, and the combination becomes a workstation reorder point combination; specifically: For each workstation , based on the value range of the reorder point of the workstation , randomly select an integer in the interval of the workstation reorder point as the workstation Reorder point , and combine the reorder points of each workstation to form a workstation reorder point combination .

5. According to claim 4, a method for intelligent material distribution in a production workshop based on a time-varying workstation set is characterized in that: In step S4, based on the workstation reorder point combination and the workstation information collected in step S1, the workstation service time window is calculated, and the delivery workstation set of the current delivery cycle is determined based on the cross relationship of the workstation service time window; specifically: Step S41: Calculation of workstation service time window; The workstation service time window consists of the delivery arrival time window and the delivery departure time window. Based on the time-varying characteristics, the setting standard of the workstation service time window is determined. The specific calculation method is as follows: The earliest delivery arrival time is set as shown in formula (3): （3）； In the formula, For workstation No. The earliest arrival time of the delivery; For the Deliver materials to the workstation moment; For workstation No. The quantity of materials after the first delivery; For workstation A delivery of a workstation The number of material delivery times ; For workstation The collection of material delivery times; The latest arrival time of delivery is set as shown in formula (4): （4）； In the formula, For workstation No. The latest arrival time of the delivery; For workstation service time; Based on the delivery arrival time window, the delivery departure time window of the workstation is calculated, which is divided into the earliest departure time of delivery and the latest departure time of delivery, which are expressed by formula (5) and formula (6) respectively: （5）； （6）； In the formula, For workstation No. The earliest departure time for the delivery; For workstation No. The latest departure time for the delivery; Step S42: dividing the delivery station set based on the intersection of time windows; Based on the cross-relationship between the workstation service time windows, the workstations that need to perform material distribution in the current distribution cycle are divided into distribution workstation sets. The division method is as follows: A collection of all workstations The earliest workstation to initiate a delivery request is set to , marking the earliest departure time as The latest departure time is ; To remove the workstation that initiated the earliest delivery request Each workstation outside If the workstation Current Earliest Departure Time Less than the latest departure time , then the workstation It is classified into the same delivery station set as the first station, and the expression is shown in formula (7): （7）； In the formula, For the A set of delivery stations.

6. The method for intelligent material distribution in a production workshop based on a time-varying workstation set according to claim 5, characterized in that: In step S5, the method for calculating the departure time of the delivery vehicle is determined based on the specific number of delivery stations of the delivery vehicle, and the method for calculating the number of materials delivered to the station is determined based on the arrival time of the station; specifically: Step S51: Calculation method of the delivery vehicle departure time; Select the delivery departure time window and delivery arrival time window as the basis to calculate the delivery vehicle departure time. Assuming that the delivery vehicle In the The departure time of the delivery cycle is The method for calculating the departure time of the delivery vehicle is divided into two cases: step S511 and step S512: Step S511: When the number of workstations to be delivered is equal to 1, it is assumed that the delivery workstation is workstation , the calculation method of the delivery vehicle departure time is shown in formula (8), which is No. The earliest departure time of a delivery cycle; （8）； In the formula, For delivery vehicles In the The departure time of each delivery cycle; is a certain delivery cycle, belonging to the set of all delivery cycles ; is the collection of all delivery cycles; is a 0-1 variable: if Delivery cycle delivery vehicles Responsible for delivering materials to workstations but is 1, otherwise is 0; Is a 0-1 variable: If the workstation No. The second delivery belongs to Delivery cycle is 1, otherwise is 0; Step S512: When the number of workstations to be delivered is greater than 1, the latest arrival time of the delivery vehicle is used as the calculation basis, and the departure time of the delivery vehicle is obtained by the reverse calculation method; specifically: Step S5121, for each workstation , according to the workstation of the current delivery cycle No. The latest arrival time of the delivery The values ​​of are sorted in descending order, and the largest station number among the values ​​of the latest arrival time is , the second largest station number is , then the workstation No. Delivery cycle arrival time It is expressed as formula (9): （9）； In the formula, For the Delivery cycle delivery vehicles Responsible for delivery workstation The latest arrival time; For workstation and workstation The distance between For workstation service time; Step S5122, remove the workstation , the original workstation Update to new workstation , from the latest arrival time Select the workstation in descending order The corresponding next station is used as the new station , execute formula (9) again until the arrival time of all delivery vehicles at all delivery stations of the delivery vehicle is calculated; Step S5123, based on the final calculated workstation No. Delivery cycle arrival time , determine the delivery vehicle In the Departure time of delivery cycle , expressed as formula (10): （10）； In the formula, is the distance between the warehouse and workstation b; Step S52: a method for calculating the quantity of material delivered to a workstation; When delivery vehicle In the Departure time of delivery cycle After confirmation, the delivery frequency of each station is tracked and recorded to identify the first Delivery cycle to workstation The delivery belongs to the workstation The number of deliveries is then further determined based on the driving distance between workstations and the speed of the delivery vehicle. No. The arrival time of the delivery ; Workstation No. The time of initiating the material request is calculated by formula (11): （11）； Where: For workstation No. The time when the material request was first initiated; Workstation No. The specific quantity of remaining materials at the time of the next delivery is calculated by formula (12): （12）； Where: For workstation No. The specific quantity of remaining materials at the time of the next delivery arrival; Finally, the workstation No. The number of remaining available containers and workstations at the time of the next delivery No. The number of containers delivered each time is calculated using formula (13) and formula (14): （13）； （14）； In the formula, For workstation No. The number of available containers remaining at the time of the next delivery arrival. For workstation No. The number of boxes delivered.

7. The method for intelligent material distribution in a production workshop based on a time-varying workstation set according to claim 6, characterized in that: In step S6, based on the set of delivery stations determined in step S4 and the calculation method of the departure time of the delivery vehicle and the quantity of material delivered to the station determined in step S5, the forward method is used to calculate and evaluate the loading status of the delivery vehicle in real time, plan the material delivery path, and obtain the distribution planning result under the time-varying characteristics of the current distribution cycle; specifically: Step S61, set the station set that needs to be delivered to , the number of workstations in the distribution workstation concentration is , the set of all delivery vehicles is , delivery vehicles The material distribution path is , the maximum loading volume of the delivery vehicle is , the delivery vehicle departure time is ; When starting path planning, the workstation The first delivery station and the initial number 1, delivery vehicle This is the first delivery vehicle and the initial number is 1, and the initial material delivery path of the delivery vehicle is empty; Step S62: Add delivery vehicle Material delivery path and calculate the corresponding delivery vehicle departure time and the current loading volume of the delivery vehicle ; Step S63, then evaluate the loading status of the delivery vehicle: Step S631: If and :The current loading volume of the delivery vehicle does not exceed the maximum loading volume of the delivery vehicle and there is a next workstation, then the workstation Add to delivery vehicle Update the material distribution path of for , and go to step S62; Step S632: If and : If the current loading volume of the delivery vehicle does not exceed the maximum loading volume of the delivery vehicle and there is no next station, the delivery vehicle is recorded The complete material distribution path of the distribution vehicle is obtained by obtaining the material distribution paths of all distribution vehicles in the current distribution cycle; Step S633: If and : If the current loading volume of the delivery vehicle exceeds the maximum loading volume of the delivery vehicle and there is a next workstation, the workstation will be removed from the current material delivery path. The remaining workstations are recorded as delivery vehicles. The complete material distribution path is updated at the same time for , the workstation As a delivery vehicle Delivery station, update for , and go to step S6; Step S634: If and : If the current loading volume of the delivery vehicle exceeds the maximum loading volume of the delivery vehicle and there is no next workstation, the workstation will be removed from the current material delivery path. The remaining workstations are recorded as delivery vehicles. The complete material distribution path, while the workstation As a delivery vehicle The distribution station can obtain the material distribution paths of all distribution vehicles in the current distribution cycle.

8. The method for intelligent material distribution in a production workshop based on a time-varying workstation set according to claim 7, characterized in that: In step S7, based on the distribution planning result under the time-varying characteristics of the current distribution cycle obtained in step S6, the station service time window and the distribution station set of the next distribution cycle are updated according to step S4, and the distribution station of the end distribution vehicle of the current distribution cycle is collaboratively optimized with the adjacent distribution station set by comparing the service time window of the distribution station of the end distribution vehicle of the current distribution cycle with the earliest departure time of the next distribution cycle; specifically, as follows: Step S61, assuming that the delivery vehicle that departs from the warehouse the latest in the current delivery cycle is the delivery vehicle , delivery vehicles All workstations responsible for delivery are combined into a workstation group , workstation group The number of workstations in indivual; Step S62, define the delivery vehicles in the current delivery cycle Delivery team The latest departure time for each workstation is , the earliest departure time for the next delivery cycle is ; Step S63, when , then the delivery vehicle Delivery team Co-optimize with adjacent delivery station sets; Step S64: If the delivery vehicle Delivery team The latest departure time for each workstation Both are greater than the earliest departure time of the next delivery cycle , then the delivery vehicle Delivery team The various workstations in the distribution cycle are merged into the next distribution cycle for joint distribution, and the workstation data is updated; otherwise, the original material distribution path is maintained.

9. The method for intelligent material distribution in a production workshop based on a time-varying workstation set according to claim 8, characterized in that: In step S8, based on the workstation information and delivery vehicle information collected in step S1, a delivery optimization model under a time-varying workstation set is constructed; specifically: Step S81: constructing the objective function; taking the delivery vehicle startup cost, delivery vehicle driving cost, line-side inventory cost, time window penalty cost and line-side inventory penalty cost into account in the total cost, with minimizing the total cost of material distribution as the overall goal; Step S811: The calculation formula for the delivery vehicle startup cost is shown in formula (15): （15）； In the formula, start-up costs for delivery vehicles; The single startup cost of the delivery vehicle; is the total number of starts of the delivery vehicle; Step S812: The calculation formula of the delivery vehicle driving cost is shown in formula (16): （16）； In the formula, The driving cost of the delivery vehicle; is the unit driving cost of the delivery vehicle; 0-1 variable: If the delivery vehicle In the Delivery cycle from workstation Travel to the workstation but is 1, otherwise is 0; For workstation With workstation The distance between Step S813: The calculation formula of line-side inventory cost is shown in formula (17): （17）； In the formula, is the line-side inventory cost; is the unit volume cost of line-side inventory; For workstation No. The quantity of material when the first delivery arrives; For workstation The volume of the material box, in m 3 ; For the Deliver materials to the workstation moment; Step S814: The calculation formula of the time window penalty cost is shown in formula (18): （18）； In the formula, Penalize costs for time windows; Penalty costs for early arrival; Penalty costs for late arrival; Step S815: The calculation formula of the line side inventory penalty cost is shown in formula (19): （19）； In the formula, Line side inventory penalty costs; Penalty cost per unit volume of line-side inventory; In order to minimize the total cost of material distribution, the objective function is constructed as shown in formula (20): （20）； Where: is the total delivery cost; Step S82: constructing constraint conditions; Based on the optimization process in line with the actual production operation conditions, the following constraints are given: Constraint 1: , indicating that the reorder point value of each workstation must not exceed the value range; Constraint 2: , which means that the delivery vehicle starts from the warehouse every time and returns to the warehouse after completing the delivery task; where, Indicates delivery vehicle In the A delivery cycle from the warehouse to the workstation ; Indicates delivery vehicle In the Delivery cycle from workstation Drive to the warehouse; Constraint three: , indicating that the material distribution path must be connected, that is, the distribution vehicle arrives at the workstation , must be from the workstation Leave; in the formula, Indicates delivery vehicle In the Delivery cycle from workstation Travel to the workstation ; Indicates delivery vehicle In the Delivery cycle from workstation Travel to the workstation ; Constraint 4: , indicating that in each delivery cycle, there is only 0 or 1 delivery vehicle as a workstation Services cannot be split into two or more vehicles; Constraint 5: , indicating that the total loading volume of the delivery vehicle for each delivery shall not be greater than the maximum loading volume of the delivery vehicle; where, The maximum loading volume of the delivery vehicle, in m 3 ; Constraint six: , indicating that the number of line-side material boxes at each workstation must not exceed the corresponding maximum number of line-side inventory material boxes; Constraint seven: , which means that the order between the two workstations delivered by the same delivery vehicle must be sequential. Only after the previous workstation is delivered, the next workstation can be delivered. In the formula, For the Delivery cycle to deliver materials to the workstation moment; For the Delivery cycle to deliver materials to the workstation moment; is an infinite value; Constraint 8: , indicating the workstation The material delivery must be completed within the specified time window.

Images