A flow shop item buffer scheduling method, device, medium and product

Abstract

The application discloses a kind of flow shop article buffer scheduling method, equipment, medium and product, it is related to production scheduling field.First, it is built based on buffer scheduling model, including multiple decision variables;Then based on scheduling model, the objective function and constraint condition of flow shop article buffer scheduling are constructed;According to task requirement, flow shop article buffer scheduling scheme is three-section coding, determines the number of coding bits;According to the number of coding bits, the dimension of particle is determined, and the population initialization of particle swarm algorithm and the improvement of particle speed and position updating process are carried out, to generate improved particle swarm algorithm;The objective function is used as the fitness function of improved particle swarm algorithm, and the global optimal position is output by using improved particle swarm algorithm, which can effectively improve the quality and convergence speed of solution;Global optimal position is three-section decoding, and the optimal scheme is used to guide flow shop article buffer scheduling, which can greatly improve overall production efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of production scheduling, in particular to a flow shop article buffer scheduling method, device, medium and product. BACKGROUND

[0002] Flow shop scheduling problem (FSSP) is a key research topic in the field of production scheduling, and its core is how to efficiently arrange the processing sequence of workpieces (articles) on multiple machines, so as to achieve the purpose of minimizing the completion time. Flow shop scheduling is a key factor to provide manufacturing production plan and ensure normal processing of production process, but not all scheduling schemes can fully meet the production needs. With the diversification of processed products, the processing mode gradually develops towards multi-batch small batch, and the more complex constraint conditions in the production system, the optimization scheme of workshop scheduling becomes more and more complex. Therefore, how to optimize the production scheduling scheme and realize efficient production is the main challenge of flow shop scheduling problem. It is necessary to select the processing workpiece, processing machine and processing route order based on the known information such as workpiece type and machine efficiency, so as to reduce the total processing time, processing cost, machine waiting time and other factors, improve the production efficiency of enterprises and meet the needs of customers.

[0003] In the traditional flow shop scheduling method, there are many limitations. For example, a single heuristic algorithm often has difficulty in obtaining high-quality solutions when dealing with complex scheduling problems, and the convergence speed is slow. For example, a simple genetic algorithm may fall into a local optimal solution and cannot quickly find a globally optimal scheduling scheme. SUMMARY

[0004] The purpose of the present application is to provide a flow shop article buffer scheduling method, device, medium and product, which effectively improves the quality and convergence speed of the solution by improving the particle swarm algorithm, so as to quickly find a globally optimal scheduling scheme and improve the overall production efficiency.

[0005] To achieve the above purpose, the present application provides the following solutions.

[0006] In a first aspect, the present application provides a flow shop article buffer scheduling method, comprising:

[0007] A scheduling model based on buffer is constructed, and the scheduling model includes a plurality of decision variables; the plurality of decision variables include a set of completion of each article warehouse transfer and loading time , a set of elevator time from buffer to article warehouse , a set of elevator time from article warehouse to buffer , a set of time of transfer in modular buffer Opening and closing time of end protection device Time of entering and fixing of the carrier into the processing station Time of exiting and fixing of the carrier from the processing station ;

[0008] A target function and constraint condition of the flow shop buffer scheduling are constructed based on the scheduling model;

[0009] A three-section coding is performed on the flow shop buffer scheduling scheme according to the task requirement, and the number of coding bits is determined;

[0010] The dimension of the particle is determined according to the number of coding bits, and the population initialization, particle speed and position updating process of the particle swarm algorithm are improved to generate an improved particle swarm algorithm;

[0011] The target function is taken as the fitness function of the improved particle swarm algorithm, and the global optimal position is output by the improved particle swarm algorithm;

[0012] The global optimal position is decoded in three sections to generate the optimal scheme of the flow shop buffer scheduling.

[0013] Optionally, the scheduling model based on the buffer is constructed, and specifically includes:

[0014] A set of transfer and loading times in the goods warehouse is constructed ; wherein is the marshalling number of the transfer personnel in the goods warehouse, , is the number of marshalling; is the goods type number, , is the number of goods types; is the time for the transfer personnel in the first group to transfer and load the first type of goods in the goods warehouse;

[0015] A set of times for the elevator to transfer the goods from the buffer to the goods warehouse is constructed ; wherein is the goods warehouse number, , is the number of goods warehouses; is the time for the elevator to transfer the goods from the buffer to the first goods warehouse;

[0016] A set of times for the elevator to transfer the goods from the goods warehouse to the buffer is constructed ; wherein is the time for the elevator to transfer the goods from the first goods warehouse to the buffer;

[0017] A set of transfer times in the modular buffer is constructed ; wherein is a modular buffer position number, , is a modular buffer quantity; is a transit time of the th modular buffer.

[0018] Optionally, the target function and constraint condition of the flow shop item buffer scheduling are constructed based on the scheduling model, and specifically include:

[0019] The transit time of the th vehicle is calculated according to a plurality of decision variables included in the scheduling model ; , is a vehicle quantity;

[0020] The end time of the transit of the th vehicle is calculated according to the formula ; wherein is the start time of the transit of the th vehicle;

[0021] The target function and constraint condition of the flow shop item buffer scheduling are constructed with the shortest total transit time as the optimization objective ; wherein max and min represent taking the maximum value and the minimum value, respectively; is the latest end time of the transit of all vehicles; is the shortest total transit time; and the constraint condition includes item inventory limit, processing equipment selection limit of the item warehouse, vehicle carrying capacity limit, transit adapter limit carried by the vehicle, elevator carrying capacity limit, and buffer space limit.

[0022] Optionally, the flow shop item buffer scheduling scheme is encoded in three sections according to the task requirements, and the number of encoding bits is determined, and specifically includes:

[0023] The inventory items in each item warehouse are divided into a plurality of vehicles according to the maximum number of each type of item that can be transited by a vehicle, and then each full vehicle is arranged and numbered according to the type of the item as ; the first section of the encoding is the scheduling selection encoding of the th full vehicle;

[0024] The number of vehicles required to transit each type of item is determined according to the type of item required to be transited and the corresponding item quantity required by the current task ;

[0025] The selected number is ​The processes performed by the full-load carriers are sequentially encoded, each full-load carrier including three processes of preparation, transfer and buffer, to generate a second segment of encoding, referred to as dispatching sequence encoding, with ;

[0026] the warehouse transfer personnel selected for each process in the second segment of encoding are encoded by grouping numbers , lift numbers or modular buffer zone position numbers to generate a third segment of encoding with 64 bits, referred to as personnel, facility and location selection encoding;

[0027] The number of encoding bits .

[0028] Optionally, the dimension of the particle is determined according to the number of encoding bits, and the population initialization of the particle swarm algorithm and the particle speed and position updating process are improved to generate an improved particle swarm algorithm, specifically including:

[0029] In the population initialization stage, a chaotic sequence is generated by using a sinusoidal chaotic mapping as the initial position of the particle swarm;

[0030] The dimension of the particle is set to , and the particle speed updating formula is improved as: ; wherein, and are the values of the speed of the particle in the first dimension at the moment and the moment; ; is a compression factor; is an adaptive inertia weight; is an individual learning factor, is a group learning factor; and are random numbers uniformly distributed in the interval [0,1]; is the individual optimal position of the particle in the first dimension, is the value of the global optimal position in the first dimension; is the value of the position of the particle in the first dimension at the moment;

[0031] The particle position updating formula is set as: , and when the particle updates the position, randomly reassigns part of the dimensions of the particle; wherein is a particle In the first dimensional position at the value of the moment.

[0032] Optionally, the compression factor The calculation formula is: .

[0033] Optionally, the adaptive inertia weight The calculation formula is: ; wherein is the maximum inertia weight, is the minimum inertia weight, is the maximum iteration number.

[0034] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the flow shop buffer scheduling method.

[0035] In a third aspect, the present application provides a computer readable storage medium, having a computer program stored thereon, wherein the computer program is executed by a processor to implement the flow shop buffer scheduling method.

[0036] In a fourth aspect, the present application provides a computer program product, comprising a computer program, wherein the computer program is executed by a processor to implement the flow shop buffer scheduling method.

[0037] According to the embodiments provided by the present application, the following technical effects are disclosed.

[0038] The flow shop buffer scheduling method, device, medium and product provided by the present application firstly construct a scheduling model based on a buffer zone, which includes a plurality of decision variables; then construct a target function and a constraint condition of the flow shop buffer scheduling based on the scheduling model; encode the flow shop buffer scheduling scheme in three segments according to the task requirements, determine the number of coding bits; determine the dimension of the particle according to the number of coding bits, and improve the population initialization and the particle speed and position updating process of the particle swarm algorithm to generate an improved particle swarm algorithm; take the target function as the fitness function of the improved particle swarm algorithm, and output the global optimal position by using the improved particle swarm algorithm, which can effectively improve the quality and convergence speed of the solution; decode the global optimal position in three segments, and use the generated optimal scheme to guide the flow shop buffer scheduling, which can greatly improve the overall production efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below only illustrate some of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0040] Figure 1 A flowchart of a flow water workshop article buffer scheduling method. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0042] The present application proposes a flow water workshop article buffer scheduling method, device, medium and product, aiming to effectively improve the solution quality and convergence speed by improving the particle swarm algorithm, so as to quickly find the globally optimal scheduling scheme.

[0043] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0044] In an exemplary embodiment, as shown in Figure 1 , a flow water workshop article buffer scheduling method is provided, including the following steps 1 to 6.

[0045] Step 1: Construct a buffer-based scheduling model, which includes a plurality of decision variables.

[0046] The plurality of decision variables includes a set of transfer and loading time in each article warehouse , a set of lift time from buffer to article warehouse , a set of lift time from article warehouse to buffer , a set of time in modular buffer , opening and closing time of end protection device , loading time of carrier into processing station and fixing time , and unloading time of carrier from processing station and fixing time . The transferred articles in the present application mainly refer to primary processing products or workpieces, which need to be scheduled between the article warehouse (i.e. primary processing product warehouse) and the modular buffer zone (referred to as buffer zone).

[0047] The set of time for transferring and loading in the warehouse of each article is represented as follows:

[0048] (1);

[0049] wherein is the group number of the transfer personnel in the single article warehouse, , is the group number; is the article type number, , is the article type number; is the time for the transfer personnel in the first group to transfer and load the article of the first type in the article warehouse.

[0050] The set of time for the elevator from the buffer zone to the article warehouse is represented as follows:

[0051] (2);

[0052] wherein is the article warehouse number, , is the article warehouse number; is the time for the elevator from the buffer zone to the first article warehouse.

[0053] The set of time for the elevator from the article warehouse to the buffer zone is represented as follows:

[0054] (3);

[0055] wherein is the time for the elevator from the first article warehouse to the buffer zone.

[0056] The set of time for the transfer in the modular buffer zone is represented as follows:

[0057] (4);

[0058] wherein is the position number of the modular buffer zone, , is the number of the modular buffer zone; is the transfer time of the first modular buffer zone.

[0059] The time for opening and closing the end protection device , the time for the carrier to enter the processing station and be fixed And the vehicle exiting the processing station and the fixed time. This is a fixed value, set according to the actual situation of the assembly line workshop.

[0060] Step 2: Construct the objective function and constraints for the buffer scheduling of goods in the flow workshop based on the scheduling model.

[0061] Based on the multiple decision variables included in the scheduling model, the following formula is used to calculate the first... When the vehicle is turned around :

[0062] (5);

[0063] in , This refers to the number of vehicles.

[0064] Furthermore, the following formula is used to calculate the first... End time of vehicle transfer :

[0065] (6);

[0066] in For the first The start time of the vehicle transfer.

[0067] With the goal of minimizing the total transfer time, the objective function for the buffer scheduling of goods in the assembly line workshop is constructed as follows:

[0068] (7);

[0069] Where max and min represent taking the maximum and minimum values, respectively; This is the latest end time for all vehicle transfers; The shortest total transfer time is defined as the time from the start of the transfer by the first vehicle to the end of the transfer when the last vehicle completes all the procedures.

[0070] The constraints of the above objective function include inventory limits, restrictions on the selection of processing equipment in the warehouse, vehicle capacity limits, restrictions on the transfer adapters loaded on the vehicle, elevator capacity limits, and buffer space limits.

[0071] The item inventory limit refers to the requirement that the total quantity of a certain type of item transferred from a particular item warehouse by all vehicles shall not exceed the inventory of that type of item in that item warehouse. The item inventory limit can be represented as follows:

[0072] (8);

[0073] In the formula, the number of the i-th type of items transported by the j-th vehicle from the k-th item warehouse; the inventory of the i-th type of items in the k-th item warehouse.

[0074] The processing equipment selection restriction of an item warehouse means that, due to the layout design of the processing equipment, the items of each item warehouse can only be transported to the corresponding lift, that is:

[0075] (9);

[0076] wherein, represents the i-th type of items transported by the k-th item warehouse through the j-th lift; is the set of lifts available to the k-th item warehouse. The vehicle carrying capacity restriction means that the number of the i-th type of items transported by the j-th vehicle is not more than the upper limit of the single transportation quantity of the i-th type of items by the j-th vehicle, that is:

[0077] (10);

[0078] wherein, is the number of the i-th type of items transported by the j-th vehicle,

[0079] is the upper limit of the single transportation quantity of the i-th type of items by the j-th vehicle. The common transportation principle also includes that the j-th vehicle only transports items from the k-th item warehouse, that is: (11); wherein,

[0080] represents the i-th type of items transported by the j-th vehicle; represents the i-th type of items stored in the k-th item warehouse. The transportation adapter restriction of the vehicle means that, due to the fact that a vehicle can only load a type of transportation adapter, each vehicle can only transport one type of items, which is represented as follows:

[0081]

[0082]

[0083] ​​​​​​​​​​​​​​​​​​

[0084] (12);

[0085] wherein, is the total number of the types of the articles transported by the i-th vehicle. is the total number of the types of the articles transported by the i-th vehicle.

[0086] The elevator capacity limit refers to the number of vehicles transported by each type of elevator in a single transfer, which is not more than the elevator capacity, and is expressed as follows:

[0087] (13);

[0088] wherein, is the number of vehicles transported by the i-th elevator in a single transfer; is the number of vehicles transported by the i-th elevator in a single transfer; is the maximum number of vehicles transported by the i-th elevator in a single transfer. The buffer space limit refers to the number of vehicles parked in a certain buffer, which is not more than the maximum number of vehicles parked in the buffer, and is expressed as follows:

[0089]

[0090] (14); wherein,

[0091] indicates that the i-th vehicle is parked in the j-th buffer; is the maximum number of vehicles parked in the j-th buffer. Step 3: According to the task requirements, the three-segment coding of the flow shop article buffer scheduling scheme is performed, and the number of coding bits is determined. According to the mathematical model of the flow shop article buffer scheduling scheme (i.e. the scheduling model), the three-segment coding of the flow shop article buffer scheduling scheme is performed according to the task requirements, and the number of coding bits is determined for subsequent improvement of the particle swarm algorithm.

[0092] Specifically, the inventory articles in each article warehouse are divided into multiple vehicles according to the maximum number of vehicles that can transport each type of article, and then each full load vehicle is arranged and numbered in order of article type as , then the first segment coding is

[0093] the scheduling selection coding of the i-th full load vehicle.

[0094] Further, according to the types of articles required to be transported by the current task and the corresponding article demand quantity, the number of vehicles required to transport each type of article is determined . Then the selected number is

[0095] ​​​​​the processes performed by the full-load carriers are sequentially encoded, each full-load carrier including three processes of preparation, transfer and buffering, to generate a second segment of bits, i.e., a dispatch order code. The preparation process refers to the preparation of the full-load carrier in the primary processing product warehouse, which is performed by the product warehouse transfer personnel; the transfer process refers to the vertical transfer of the full-load carrier by the elevator; and the buffering process refers to the modular buffering.

[0096] The product warehouse transfer personnel selected for each process in the second segment of code is encoded as a group number , an elevator number , or a modular buffering zone position number to generate a third segment of code, i.e., a personnel, facility and location selection code; the number of code bits .

[0097] If the process performed by the full-load carrier numbered in the second segment of code is the preparation process, the corresponding position in the third segment of code is encoded as the group number of the product warehouse transfer personnel performing the preparation process ; if the process performed by the full-load carrier numbered in the second segment of code is the transfer process, the corresponding position in the third segment of code is encoded as the elevator number performing the transfer process ; and if the process performed by the full-load carrier numbered in the second segment of code is the buffering process, the corresponding position in the third segment of code is encoded as the modular buffering zone position number performing the buffering process .

[0098] To explain the above three-segment coding method in detail, a specific example is used below for illustration. Assume that 5 cars of A-type products and 3 cars of B-type products, i.e., a total of 8 cars of products, need to be transferred from the product warehouse to the buffering zone. The number of carriers required for transferring each type (A-type and B-type) of products = 8. The inventory products in each product warehouse are divided into a maximum number of carriers that can transfer each type of product, at which time the A-type products in the product warehouse satisfy 6 cars, the B-type products satisfy 6 cars, and a total of = 12 full-load carriers. The full-load carriers are then arranged and numbered in order according to the product type as . The three-segment coding is as follows:

[0099] The first segment, the dispatch selection code of the 12 full-load carriers: [1 2 3 5 4 6 10 11 8 12 9 7];

[0100] The second segment, The scheduling order encoding of the bits: [2 1 4 1 3 8 4 10 5 11 3 11 2 3 5 10 2 18 10 8 5 4 11];

[0101] The third segment, The personnel, facility and location selection encoding of the bits: [2 1 4 1 3 2 2 4 5 5 1 2 2 8 11 7 6 2 5 4 3 2 1].

[0102] First, according to the task requirements, the first five of the numbers 1 to 6 representing the A-type full-load carriers in the first segment encoding are taken, i.e. = No. 1, 2, 3, 4, 5 full-load carriers, the first three of the numbers 7 to 12 representing the B-type full-load carriers are taken, i.e. = No. 8, 10, 11 full-load carriers, a total of = 8 carriers are transported. Second, the second and third segment encodings are constructed according to the results obtained from the first segment encoding, and the encoding method is as described above. The processes performed by the selected full-load carriers numbered are sequentially encoded to generate the second segment scheduling order encoding; the personnel, facility and location selection encoding of the selected full-load carriers numbered , the elevator numbers or the modular buffer location numbers are encoded to generate the third segment personnel, facility and location selection encoding.

[0103] Correspondingly, when decoding the three-segment encoding, the second and third segment encodings are read from left to right in sequence. The numbers in the second segment encoding are full-load carrier numbers. The first occurrence of a number indicates that the full-load carrier performs the preparation within the product warehouse (preparation process), the second occurrence indicates that the vertical transportation is performed (transportation process), and the third occurrence indicates that the modular buffering is performed (buffering process). The numbers in the corresponding positions in the third segment encoding represent the personnel group numbers , the elevator numbers or the modular buffer location numbers .

[0104] For example, the scheduling scheme represented by the above three-segment encoding is introduced as follows. Among them, the first segment encoding [1 2 3 54 6 10 11 8 12 9 7] combines =8 indicates that the 1st, 2nd, 3rd, 4th, 5th full load carriers are selected to transport the required 5 cars of A type goods, and the 8th, 10th and 11th full load carriers are selected to transport the required 3 cars of B type goods. The second and third segments of the code represent the scheduling sequence: the 2nd full load carrier is prepared in the primary processing product warehouse by the 2nd group of goods warehouse transfer personnel, the 1st full load carrier is prepared in the primary processing product warehouse by the 1st group of goods warehouse transfer personnel, the 4th full load carrier is prepared in the primary processing product warehouse by the 4th group of goods warehouse transfer personnel, the 1st full load carrier is vertically transferred by the 1st elevator, the 3rd full load carrier is prepared in the primary processing product warehouse by the 3rd group of goods warehouse transfer personnel, the 8th full load carrier is prepared in the primary processing product warehouse by the 2nd group of goods warehouse transfer personnel, the 4th full load carrier is vertically transferred by the 2nd elevator, the 10th full load carrier is prepared in the primary processing product warehouse by the 4th group of goods warehouse transfer personnel, the 5th full load carrier is prepared in the primary processing product warehouse by the 5th group of goods warehouse transfer personnel, the 11th full load carrier is prepared in the primary processing product warehouse by the 5th group of goods warehouse transfer personnel, the 3rd full load carrier is vertically transferred by the 1st elevator, the 11th full load carrier is vertically transferred by the 2nd elevator, the 2nd full load carrier is vertically transferred by the 2nd elevator, the 3rd full load carrier is modularly buffered at the 8th modular buffer position, the 5th full load carrier is vertically transferred by the 1st elevator, the 10th full load carrier is vertically transferred by the 1st elevator, the 2nd full load carrier is modularly buffered at the 7th modular buffer position, the 1st full load carrier is modularly buffered at the 6th modular buffer position, the 8th full load carrier is vertically transferred by the 2nd elevator, the 10th full load carrier is modularly buffered at the 5th modular buffer position, the 8th full load carrier is modularly buffered at the 4th modular buffer position, the 5th full load carrier is modularly buffered at the 3rd modular buffer position, the 4th full load carrier is modularly buffered at the 2nd modular buffer position, and the 11th full load carrier is modularly buffered at the 1st modular buffer position.

[0105] Step 4: Determine the dimension of the particle according to the number of code bits, and improve the population initialization and particle speed and position updating process of the particle swarm algorithm to generate an improved particle swarm algorithm.

[0106] The improvement of the particle swarm algorithm in this application is reflected in the following aspects, specifically including 4.1) to 4.6).

[0107] 4.1) Sine chaotic particle swarm initialization.

[0108] In the population initialization stage, a chaotic sequence is generated as the initial position of the particle swarm using Sine chaotic mapping (sine chaotic mapping). The chaotic sequence generation method is as follows:

[0109] (15);

[0110] wherein, is the inventory chaos state value of the current iteration; is the inventory chaos state value of the next iteration; is the key control parameter. After the iteration is completed, represents the particle position after chaos. The instability of the chaos sequence is used for particle initialization, so that the distribution of particles is more random. The improved particle swarm optimization algorithm using Sine chaos particle swarm initialization converges earlier and has higher solution accuracy than the particle swarm optimization algorithm using the basic initialization method.

[0111] 4.2) Adaptive inertia weight control.

[0112] By adaptively adjusting the inertia weight in the particle velocity update formula with the number of iterations, the particle swarm optimization algorithm is given a larger inertia weight at the beginning of optimization to maintain the global search ability, and a smaller inertia weight at the later stage of optimization to improve the local search ability and improve the solution efficiency.

[0113] The calculation formula of the adaptive inertia weight is as follows:

[0114] (16);

[0115] wherein, is the adaptive inertia weight, is the maximum inertia weight, is the minimum inertia weight. is the current iteration number, also known as the iteration time; is the maximum iteration number.

[0116] On this basis, considering that the setting of parameters has a great influence on the solution efficiency and results of the basic particle swarm optimization algorithm, inaccurate parameter setting can easily fall into a local optimal solution or have a slow convergence speed. To this end, the present application sets =0.9, =0.4.

[0117] 4.3) Increase the compression factor.

[0118] The compression factor is used to adjust the influence degree of the learning factor , , balance the learning degree of the particle individual to the group experience and individual experience, and make the particle swarm optimization algorithm converge faster. The calculation formula of the compression factor is as follows:

[0119] (17);

[0120] wherein, is the individual learning factor in the particle velocity update formula, is the group learning factor, .

[0121] 4.4) Improved particle velocity and position update formula.

[0122] Using compression factor and adaptive inertia weight The particle velocity update formula is as follows:

[0123] (18);

[0124] where, denotes the th particle, is the iteration number or iteration time, denotes the th dimension, , is the number of encoding bits of the three-segment encoding. and are the values of the velocity of the th particle in the th dimension at the th time (the th iteration) and the th time (the th iteration), respectively. is the compression factor; is the adaptive inertia weight. and are random numbers uniformly distributed in the interval [0, 1], which are independently generated for each dimension of each particle. is the individual optimal position of the th particle in the th dimension, that is, the individual historical optimal position found by the th particle so far. is the value of the global optimal position in the th dimension, that is, the global optimal position found by the entire population so far. is the value of the position of the th particle in the th dimension at the th time, that is, the current position of the th particle in the th dimension at the iteration th time.

[0125] The particle position update formula is:

[0126] (19);

[0127] wherein is a particle In the first dimension is the value of the position at the time instant. 4.5) Adjust the position of the particles that exceed the solution space limit.

[0128] By analogy with the concept of chromosome mutation in genetic algorithms, when the particle swarm updates the position, randomly reassign the particle's partial dimension.

[0129] 4.6) Improve the fitness function as the objective function.

[0130] The above objective function (7) is used as the fitness function of the improved particle swarm algorithm, and the constraint conditions include the item inventory limit (8), the processing equipment selection limit of the item warehouse (9), the carrier transport capacity limit (10), the transfer adapter limit loaded by the carrier (12), the elevator transport capacity limit (13), and the buffer space limit (14), and can also include the common transfer principle (11).

[0131] In the improved particle swarm algorithm proposed in the present application, when the particle swarm is initialized, a chaotic sequence is generated using Sine chaotic mapping. The instability of the chaotic sequence can make the distribution of the particles more random. Further, the inertia weight is controlled to be self-adaptive with the iteration number, so that the particle swarm algorithm is given a larger inertia weight in the early optimization stage to maintain the global search ability, and a smaller inertia weight in the later optimization stage to improve the local search ability and improve the solving efficiency. On this basis, considering that the setting of parameters has a great influence on the solving efficiency and results of the basic particle swarm algorithm, otherwise it is easy to fall into a local optimal solution or slow convergence speed, therefore

[0132] = 0.9, = 0.4. Further, the compression factor is used to adjust the influence degree of the learning factor , , balance the learning degree of the particle individual to the group experience and individual experience, so that the particle swarm algorithm converges faster. When the particle swarm updates the position, by analogy with the concept of chromosome mutation in genetic algorithms, randomly reassign the particle's partial dimension.

[0133] Compared with the prior art, the present application improves the particle swarm algorithm by Sine chaotic initialization and setting adaptive function dynamic particle inertia weight, compression learning factor and particle mutation, improves the initial global search ability, and solves the problem of falling into a local optimal solution. The improved particle swarm algorithm of the present application can effectively improve the solution quality and convergence speed, and compared with the basic particle swarm algorithm, the average convergence speed is improved by 36%.

[0134] ​Step 5: Use the objective function as the fitness function of the improved particle swarm algorithm, and use the improved particle swarm algorithm to output the global optimal position.

[0135] The process of using the improved particle swarm optimization algorithm to output the global optimal position includes the following steps (5.1) to (5.8).

[0136] 5.1) Data Input and Three-Segment Coding: Input the item type, required quantity, process, and time for each process; filter items in the warehouse that meet the requirements; and determine the number of digits in the code. .

[0137] 5.2) Sine chaotic particle swarm initialization: During initialization, the particle swarm uses the Sine chaotic map to generate a chaotic sequence as the initial position of the particle swarm.

[0138] 5.3) Calculate the adaptive inertia weights Based on the current iteration number The inertia weight is adaptively adjusted using formula (16).

[0139] 5.4) Calculate fitness: Calculate the fitness value for each particle. The fitness function is the objective function itself, and the fitness value is the value of the objective function.

[0140] 5.5) Update individual optimal position and global optimal position: Calculate the fitness value of each particle in the first swarm, record the individual optimal fitness value and particle number, and obtain the optimal position of the first particle. The position where a particle has the highest fitness value during the iteration process, i.e., the optimal position for that individual particle. Calculate the fitness value of each particle in the next swarm, record the individual's optimal fitness value and particle number, compare it with the fitness value of the previous swarm, and take the minimum value to obtain the best position found by the population so far, i.e., the global optimal position. For example, if the population is an array of 300 rows, then each row represents one population, and the optimization is performed row by row.

[0141] 5.6) Update particle velocity and position: based on the individual optimal position of the particle. and global optimal position The improved formulas (18) and (19) are used to update the velocity and position of the particles.

[0142] 5.7) Adjusting the positions of particles that exceed the solution space limit: Drawing on the concept of chromosome mutation in genetic algorithms, when updating the positions of the particle swarm, the dimensions of some particles that exceed the solution space limit are randomly reassigned.

[0143] 5.8) Check if the maximum number of iterations is met. If yes, the loop is exited and the global optimal position of the entire population is obtained, i.e., the optimal solution of the flow shop buffer scheduling is output Otherwise, return to step 5.3).

[0144] The application optimizes the initial particle swarm position by improving the particle swarm algorithm, and the calculation result can jump out of the local optimal solution when the particle dimension is large, so that the algorithm converges earlier and the solution accuracy is higher.

[0145] Step 6: The global optimal position is decoded in three sections to generate the optimal solution of the flow shop buffer scheduling.

[0146] According to the three-section encoding and decoding method described in step 3, the global optimal position is decoded in three sections to generate the optimal solution of the flow shop buffer scheduling. Specifically, the selected full-load carrier number can be determined by reading the first section of the code; the scheduling order and the warehouse transfer personnel grouping number , the elevator number or the modular buffer zone position number used to perform each process can be determined by reading the second and third sections of the code. The flow shop buffer scheduling according to the optimal solution obtained by the improved particle swarm algorithm can reasonably arrange the processing sequence of workpieces (articles) on multiple machines in the limited buffer zone, greatly improving the processing efficiency.

[0147] In an exemplary embodiment, the application also provides a computer device, which can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement the flow shop buffer scheduling method.

[0148] In an exemplary embodiment, the application also provides a computer-readable storage medium having a computer program stored thereon, which is executed by the processor to implement the flow shop buffer scheduling method.

[0149] In one exemplary embodiment, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the flow shop item buffer scheduling method.

[0150] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by computer program instruction related hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiment methods. Any reference to memory or other medium in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0151] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0152] The technical features of the above embodiments can be combined in any way. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0153] The principles and implementation manners of the present application are described herein by using specific examples, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will have changes. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for buffering and scheduling items in a production line, characterized in that, include: Construct a buffer-based scheduling model, which includes multiple decision variables; Multiple decision variables include the set of times for completing the transfer and loading of each item within the warehouse. The time set of the elevator from the buffer zone to the goods warehouse. The time set for the elevator to move from the goods warehouse to the buffer zone. Modular buffer transit time set End protection device opening and closing time The entry of the vehicle into the processing station and the fixed time And the vehicle exiting the processing station and the fixed time. ; Based on the scheduling model, the objective function and constraints for the buffer scheduling of goods in the flow workshop are constructed. Specifically, this includes: calculating the first... When the vehicle is turned around ; , For the number of vehicles; according to the formula Calculate the first End time of vehicle transfer ;in For the first The start time of vehicle transfer; with the goal of minimizing the total transfer time, construct an objective function for the buffer scheduling of goods in the assembly line workshop. and constraints; where max and min represent taking the maximum and minimum values, respectively; This is the latest end time for all vehicle transfers; The minimum total transfer time is specified; the constraints include restrictions on inventory, restrictions on the selection of processing equipment in the warehouse, restrictions on vehicle capacity, restrictions on the transfer adapters loaded on the vehicle, restrictions on elevator capacity, and restrictions on buffer space. Based on the task requirements, a three-stage coding system was implemented for the material buffer scheduling scheme in the assembly line workshop. The number of digits in the code was determined, specifically including: dividing the inventory items in each material warehouse into multiple vehicles based on the maximum quantity of each type of item that can be transported by one vehicle; then, arranging and numbering the fully loaded vehicles according to the type of items. The first segment is encoded as follows: Dispatch selection code for fully loaded vehicles; determine the number of vehicles required to transport each type of item based on the type of goods to be transferred in the current task and the corresponding quantity required. ; select the number as The processes performed by a fully loaded vehicle are coded sequentially. Each fully loaded vehicle includes three processes: preparation, transfer, and buffering, generating... The second segment of the code is called the scheduling sequence code; where The warehouse transfer personnel selected for each process in the second coding segment will be grouped and numbered. Elevator Number Or modular buffer location number Encode and generate The third segment of the code is called the personnel, facility, and location selection code; the number of bits in the code is... ; The particle dimension is determined based on the number of bits in the encoding, and the population initialization and particle velocity and position update processes of the particle swarm optimization algorithm are improved to generate an improved particle swarm optimization algorithm. Specifically, this includes: in the population initialization stage, using a sinusoidal chaotic map to generate a chaotic sequence as the initial position of the particle swarm; setting the particle dimension to... The particle velocity update formula was improved to: ;in, and Particles In the The speed of the dimension is Time and The value at time; ; It is the compression factor; For adaptive inertia weights; For individual learning factors, For group learning factors; and is a random number uniformly distributed in the interval [0,1]. It is a particle In the The optimal position of an individual in dimension. Is the globally optimal position at the ? The value of the dimension; It is a particle In the The position of the dimension is The value at time; set the particle position update formula to: Furthermore, when updating the particle's position, some dimensions of the particle are randomly reassigned; where It is a particle In the The position of the dimension is The value at time; the compression factor The calculation formula is: The adaptive inertia weight The calculation formula is: ;in For maximum inertia weight, For minimum inertia weight, This represents the maximum number of iterations. The objective function is used as the fitness function of the improved particle swarm optimization algorithm, and the improved particle swarm optimization algorithm is used to output the global optimal position. The optimal global position is decoded in three stages to generate the optimal solution for the buffer scheduling of items in the production line.

2. The method for buffering and scheduling items in a production line according to claim 1, characterized in that, The construction of the buffer-based scheduling model specifically includes: Complete the collection of transfer and loading times for each item within the warehouse. ;in Assign group numbers to personnel involved in the transfer of goods to the warehouse. , Number of groups; Number the item type. , Quantity of item type; For the first The group of transfer personnel will the first The time required for these types of goods to be transferred and loaded into vehicles within the goods warehouse; Construct the time set of the elevator from the buffer to the item warehouse ;in Number the item warehouse. , This refers to the number of items in the warehouse. For the elevator from the buffer zone to the first The time for each item warehouse; Constructing the elevator from the item warehouse to the buffer time set ;in For the elevator from the first Time from item warehouse to buffer zone; Constructing a modular buffer transit time set ;in Modular buffer location numbering, , Number of modular buffers; For the first Transfer time of each modular buffer.

3. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the flow workshop item buffer scheduling method according to any one of claims 1 to 2.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the flow workshop item buffering scheduling method as described in any one of claims 1 to 2.

5. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the flow workshop item buffering scheduling method as described in any one of claims 1 to 2.

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