Assembly workshop machine and AGV combined scheduling method considering optimal path

Abstract

The invention relates to a special assembly workshop machine and AGV combined scheduling optimization method considering optimal path selection, and the method comprises the following steps: initializing configuration parameters according to a workshop assembly process; a general assembly workshop machine and AGV scheduling scheme is taken as a solution, and a machine and AGV combined scheduling multi-target optimization model considering an AGV transportation optimal path is established; designing a multi-objective learning type artificial bee colony algorithm; and optimizing the scheduling scheme through iterative computation, and finally obtaining a plurality of optimized scheduling schemes which accord with actual production. Compared with a traditional scheduling mode, the method considers the selection of the optimal path during AGV transportation, greatly improves the scheduling efficiency and accuracy of the special assembly workshop, reduces the scheduling time cost and energy cost while providing a reasonable and effective scheduling scheme for workshop scheduling, and improves the scheduling efficiency. The overall production efficiency of an assembly workshop is improved, the order delay condition is reduced, and consumption reduction and efficiency improvement of enterprises are achieved.

Description

Technical Field

[0001] The invention relates to a method for jointly scheduling machines and AGVs in an assembly workshop taking an optimal path into consideration, and belongs to the field of production scheduling in an assembly workshop. Background Art

[0002] In the field of modern manufacturing and intelligent logistics, the improvement of automation and intelligence requires that various tasks in the workshop can be efficiently and flexibly scheduled and coordinated. AGV (Automated Guided Vehicle), as an automated transportation equipment widely used in manufacturing, warehousing and logistics, plays an increasingly important role in assembly workshops. However, with the complexity of production tasks and the expansion of workshop scale, how to ensure that AGVs do not conflict during transportation and can coordinate with various machines and equipment in the workshop has become a key issue in improving production efficiency and ensuring production safety. Therefore, the research on the joint scheduling optimization technology of assembly workshop machines and AGVs for AGV conflict-free transportation is particularly important.

[0003] The joint scheduling of machines and AGVs in the assembly workshop refers to the process of collaboratively optimizing the scheduling of machines and AGVs in the assembly workshop. In traditional assembly workshops, machine scheduling and material handling are usually carried out independently, usually completing machine scheduling first, and then material handling by manual or other means. Joint scheduling combines the two, fully considering the machine operating status and material handling requirements, aiming to optimize the assembly process and resource utilization efficiency, avoid machine waiting due to missing materials, and reduce manual intervention, thereby improving production efficiency and resource utilization.

[0004] The key to the joint scheduling optimization problem is how to coordinate the work order, transportation path and timing between AGV and workshop machines. The scheduling of AGV is not just a traditional task allocation problem, but also involves the coordinated work with various production equipment in the workshop. For example, AGV needs to transport materials and parts back and forth between different workstations. How to ensure that its transportation path does not conflict with the working machines and equipment, and how to ensure that AGV does not affect the production efficiency of the machines and equipment during transportation are the core tasks of joint scheduling optimization. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for jointly scheduling machines and AGVs in an assembly workshop taking into account the optimal path. Compared with traditional scheduling, the joint scheduling of AGVs and machines fully considers the transportation time cost, transportation route conflicts and other issues encountered by AGV transportation, and provides a reasonable AGV conflict-free path solution. At the same time, taking into account factors such as machine location and path length, the AGV transportation time is also considered as a factor in the selection of machines, and the optimal route with no conflict, obstacle avoidance and shortest running time is selected for the AGV. It can effectively reduce the time cost and energy cost of production and assembly in the general assembly workshop and improve the fresh production efficiency. And through reasonable AGV route planning, conflict-free transportation in the workshop can be achieved to ensure workshop production safety.

[0006] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0007] A method for jointly scheduling machines and AGVs in an assembly workshop considering an optimal path comprises the following steps:

[0008] 1) Based on the joint scheduling process of machines and AGVs in special assembly workshops, a heuristic initialization scheduling scheme is proposed;

[0009] 2) Use the A* algorithm and time window to find the conflict-free shortest time path selection for AGV transportation tasks, and optimize and adjust the scheduling plan based on the shortest time path through a multi-objective learning artificial bee colony algorithm;

[0010] 3) Select the optimized assembly workshop machine and AGV joint scheduling scheme for production scheduling.

[0011] The step 1) comprises the following steps:

[0012] 1.1) Initialize the scheduling information of AGV and machine processing;

[0013] 1.2) Assume that the total number of machines is M, the machine code is m, m∈(1,M), the total number of AGVs is W, the AGV code is w, w∈(1,W), and a batch of orders requires the production of n types of workpieces, each workpiece requires J processes, the process number is j, j∈(1,J). Encode the order scheduling plan to form a real number vector of n×(2×J+1) dimensions. Each individual represents a scheduling plan. The dimensional order of the individual represents the order of the machines and AGVs in the scheduling plan during the processing. The scheduling plan encoding form of a workpiece processing process is X=|j w ,j m ,j w ,j m ,...,j w |, then the real vector form of the individual is [X1,X2,...,X n ];

[0014] 1.3) Based on the initialized scheduling information, the initial population is heuristically created to randomly generate N individuals within the set range, i.e., N special assembly workshop scheduling plans.

[0015] The step 1.3) comprises the following steps:

[0016] 1.3.1) Randomly generate a production process sequence based on scheduling information;

[0017] 1.3.2) Traverse the AGVs and machines in the production process sequence in sequence;

[0018] 1.3.3) Determine whether the traversal is completed. If completed, output the individuals including the current process sequence, AGV sequence and machine sequence; otherwise, traverse the current idle AGV;

[0019] 1.3.4) Calculate the arrival time of each AGV, select the AGV number with the shortest arrival time, and generate the AGV sequence;

[0020] 1.3.5) Traverse all optional machines required for the processing process, calculate the completion time on each optional machine, generate the machine number with the shortest completion time, generate the machine sequence, and return to step 1.3.3).

[0021] The step 2) comprises the following steps:

[0022] 2.1) Initialize the maximum number of iterations T in the artificial bee colony algorithm parameters max , the number of unimproved generations L of individuals and the total number of individuals N as the population size;

[0023] 2.2) Determine whether the current number of iterations has reached the maximum number T max , if reached, stop the iteration and output the scheduling plan; otherwise, execute step 2.3);

[0024] 2.3) Using A * The algorithm solves the shortest path for each individual without path conflict;

[0025] 2.4) Record the transportation time and path of each transportation task of each AGV without path conflict, and generate a new individual;

[0026] 2.5) According to the number of new individuals, determine whether to generate a non-dominated sorting hierarchy. If not, execute step 2.6). Otherwise, for the population P N Perform express non-dominated sorting, calculate the crowding distance, sort the crowding distance, and retain the individuals whose order meets the threshold:

[0027] 2.6) Integrate the generated new individuals into P N Form a new population Pnew , the scale is N;

[0028] 2.7) Establish an optimization model for the joint scheduling of machines and AGVs in the assembly workshop;

[0029] 2.8) Based on the optimization model, P is ordered according to the hierarchy of the target value dominance relationship. new Conduct screening;

[0030] 2.9) Increase the current number of iterations by 1 and return to step 2.2).

[0031] The step 2.3) is specifically as follows:

[0032] Set each individual to be a new individual:

[0033] {x i,j |x i,j ∈[X1,X2,...,X n ],i=1,2,…,N,j=1,2,…n′(2′J+1)}

[0034] Assign the shortest distance without path conflict to each new individual. The starting point and end point of each transportation task of each AGV are known in the new individual. Use A * The algorithm solves the shortest path f of the AGV's transportation task l (r):

[0035] f l (r) = g(r) + h(r)

[0036]

[0037] Among them, l represents the AGV number, g(r) represents the length of the path that the AGV has traveled, h(r) represents the Euclidean distance between the current location of the AGV and the target end point, (x, y) represents the coordinates of the target end point of the AGV, (x', y') represents the current position coordinates of the AGV, and all tasks F(n) of each AGV are expressed as:

[0038]

[0039] Among them, L represents the total number of AGVs, the transportation routes of all AGVs are recorded, and the position and current time of the AGV are recorded using a time window. If the distance between two AGVs at the same time is less than one path unit, it is considered a conflict between the two AGVs.

[0040] When there is a conflict between two AGVs, the following methods are used to avoid conflict:

[0041] Case 1: The conflict point is not the AGV turning point.

[0042] If the path is occupied and the two AGVs are obstacles to each other, the AGV that arrives later will change its path;

[0043] Case 2: The conflict point is the AGV turning point.

[0044] Two AGVs occupy the turning point at the same time, and the path of AGV1 after passing the turning point is the path of AGV2 before passing the turning point. Then calculate the transportation time after AGV1 changes the path. If the transportation time is greater than the waiting time, AGV1 waits for AGV2 to pass the turning point, releases the path, and then occupies the path through the turning point; otherwise, AGV1 changes the path;

[0045] Case 3: The conflict point is the AGV turning point.

[0046] If two AGVs occupy an inflection point at the same time, and the path taken by the AGV after passing the inflection point is the path occupied by the other AGV, the AGV that arrives later will change its path;

[0047] Case 4: The conflict point is the node conflict of the AGV inflection point

[0048] If two AGVs occupy the turning point at the same time and go to different paths, one of the AGVs will wait for two unit times while the other AGV passes first.

[0049] The step 2.4) is specifically as follows:

[0050] According to an existing individual x i,j Find a new candidate individual v i,j :

[0051] v i,j =x i,j +a t,i ·d i,j (x i,j -x k,j )

[0052] Among them, random number k = 1, 2, ..., N, k1i, δ i,j is a coefficient randomly generated in the range of [-1,1], a t,i Represents the reward in the current search direction. If the individual is in v i,j The state is better than x i,j The state proves that the search direction is correct and reduces a t,i The value of is used to enhance the local search capability. If the individual is at time v i,j The status is not better than x i,j The state proves that the search direction is wrong and improves a t,iThe value of is used to enhance the global search capability and help jump out of the local solution. If an individual does not have any improvement within L generations, the individual is discarded and a new individual is generated according to the following formula:

[0053]

[0054] in, is a random number in the range [-1, 1], x min,j and x max,j is the minimum and maximum value of the j-th dimension in the current generation of individuals.

[0055] The congestion distance is specifically:

[0056]

[0057] F dism represents the crowding distance of the ith individual in the mth objective function, f m represents the mth objective function, x max represents the maximum value of all individuals under the m function, x min Indicates the minimum value.

[0058] The scheduling optimization objective function of the optimization model for the joint scheduling of the assembly workshop machine and AGV includes: minimizing the maximum completion time considering production efficiency, defined as f1; minimizing energy consumption considering energy cost, defined as f2; minimizing the total delay considering customer satisfaction, defined as f3, specifically:

[0059] (1) Set C i =FMt iJm +ht mL , is the completion time of the last process of the i-th workpiece, where FMt iJm represents the end time of the last process of the i-th workpiece, ht mL represents the time required for AGV to transport the processing machine m from the last process to the finished product area, then the minimized maximum processing time is expressed as:

[0060]

[0061] Where i = 1, 2, ..., n is the index of the workpiece, j = 1, 2, ..., J is the index of the workpiece process;

[0062] (2) Set f 21 is the load energy consumption of the machine and AGV during processing, f 22 The no-load energy consumption of the machine and AGV during processing, and the minimized total energy consumption are expressed as:

[0063]

[0064] f2=f 21 +f 22

[0065] Among them, SMt ijm FMt represents the start time of the jth process of the i-th workpiece on machine m. ijm represents the end processing time of the jth process of the i-th workpiece on machine m, SAt ijw FAt represents the transportation start time of the jth process of the i-th workpiece on AGVw, ijw SMt' represents the transportation completion time of the jth process of the i-th workpiece on AGVw. ijm FMt' represents the idle start time after the jth process of the i-th workpiece on machine m is completed. ijm It represents the idle time after the jth process of the i-th workpiece on machine m is completed, SAt' ijw FAt' represents the idle start time of the transport task of the jth process of the i-th workpiece on AGVw, ijw represents the idle end time of the transport task of the jth process of the i-th workpiece on AGVw, EM e , EM ne Indicates the load energy consumption and no-load energy consumption of the machine, EA e ,EA ne Indicates the load energy consumption and no-load energy consumption of AGV;

[0066] (3) Minimize total delay:

[0067]

[0068] Among them, C i , D i , R i They represent the completion time, delivery time, and raw material arrival time of job i respectively.

[0069] The step 2.8) comprises the following steps:

[0070] 2.8.1) Regularize all target values, establish a hyperplane with a 3-axis intercept of 1, divide the hyperplane boundary equally, and divide the hyperplane evenly to obtain several reference points, forming a reference point set Z;

[0071] 2.8.2) Associate all individuals in the first p layers to the nearest reference point, and calculate the distance d between each individual and its nearest reference point;

[0072] 2.8.3) Calculate the number of individuals associated with each reference point;

[0073] 2.8.4) For each reference point, select the first K individuals that are closest to it and are associated with it to enter the next generation population P N .

[0074] The present invention has the following beneficial effects and advantages:

[0075] 1. The present invention establishes an accurate joint scheduling model of machines and AGVs in the final assembly workshop and designs an algorithm with strong global search capability, fast convergence speed, and high solution accuracy, which enables fast and automatic computer calculation, greatly improves the efficiency of workshop production scheduling, reduces safety hazards, and ensures safe production.

[0076] 2. Compared with traditional scheduling, AGV and machine joint scheduling fully considers the transportation time cost, transportation route conflict and other issues encountered by AGV transportation, and provides a reasonable AGV conflict-free path plan, reducing production scheduling costs and achieving energy saving and efficiency improvement for enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 Flowchart of heuristically creating a new initial population;

[0078] Figure 2 Schematic diagram of AGV collision;

[0079] Figure 3 AGV node conflict diagram;

[0080] Figure 4 Flowchart of the AGV path optimization process;

[0081] Figure 5 Flow chart of the method of the present invention;

[0082] Figure 6 Map of an assembly workshop of a certain enterprise;

[0083] Figure 7 Anter chart of the optimization scheme for minimum completion time;

[0084] Figure 8 Multi-objective Pareto atlas. DETAILED DESCRIPTION

[0085] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0086] like Figure 5 As shown, a method for jointly scheduling machines and AGVs in an assembly workshop considering an optimal path includes the following steps:

[0087] Step 1: Heuristically initialize the scheduling scheme based on the joint scheduling process of machines and AGVs in the special assembly workshop;

[0088] Step 2: Use the A* algorithm and time window to find the conflict-free shortest time path selection for the AGV transportation task, and optimize and adjust the scheduling plan through the multi-objective learning artificial bee colony algorithm;

[0089] Step 3: Select the optimized assembly workshop machine and AGV joint scheduling plan for production scheduling.

[0090] The step 1 includes initializing N joint scheduling schemes P N , that is, the population size is N, as follows:

[0091] Step 1.1: Initialize the scheduling information of AGV and machine processing, including: a map of a company's special assembly workshop, the number of AGVs, the AGV no-load and loaded energy consumption, the number and location of machines, the machine no-load and loaded energy consumption, and the order size;

[0092] Step 1.2: Set the total number of machines to M, and the machine code is m, m∈(1,M); the total number of AGVs is W, and the AGV code is w, w∈(1,W). Set a batch of orders to produce n types of workpieces; each workpiece requires J processes, and the process number is j, j∈(1,J); encode the order scheduling plan to form a real number vector of n×(2×J+1) dimensions, where each individual represents a scheduling plan; the dimensional order of the individuals represents the order of the machines and AGVs in the scheduling plan during the processing; the scheduling plan encoding form for the processing process of a workpiece is X=|j w ,j m ,j w ,j m ,...,j w |; then the real vector form of the individual is [X1,X2,...,X n ];

[0093] Step 1.3: Based on the scheduling information initialized in step 1.1, a heuristic initial population is created to randomly generate N individuals within a set range, i.e., N special assembly workshop scheduling plans. Figure 1 It is a flowchart of the heuristic creation of a new initial population.

[0094] The step 2 comprises the following steps:

[0095] Step 2.1: Initialize algorithm parameters, including the maximum number of iterations T max , individual unimproved generations L, population size N;

[0096] Step 2.2: If the current number of iterations reaches the preset maximum number T max , then stop the iteration, output the scheduling plan, and continue with step 3; otherwise, continue with step 2.3;

[0097] Step 2.3: Set each individual as a new individual, as follows:

[0098] {x i,j |x i,j ∈[X1,X2,...,X n ],i=1,2,…,N,j=1,2,…n′(2′J+1)}

[0099] Assign the shortest distance without path conflict to each new individual. The starting point and end point of each transportation task of each AGV are known in the new individual. * The algorithm solves the shortest path for AGV's transportation task:

[0100] f l (r) = g(r) + h(r)

[0101] l represents the AGV number, g(r) represents the length of the path that the AGV has traveled, and h(r) represents the Euclidean distance from the current point of the AGV to the target end point:

[0102]

[0103] (x, y) represents the coordinates of the target end point of the AGV, (x', y') represents the current position coordinates of the AGV, and all tasks of each AGV are expressed as:

[0104]

[0105] L represents the total number of AGVs, records the transportation routes of all AGVs, and uses a time window to record the location and current time of the AGV. If the distance between two AGVs at the same time is less than one path unit, it is considered a conflict between the two AGVs, and is divided into the following four situations for conflict-free processing:

[0106] 1) The conflict point is not the opposite conflict of the AGV turning point. If the path is occupied, the two AGVs will hinder each other: the transportation path of the task of changing the AGV.

[0107] 2) The conflict point is the conflict of the AGV turning point. Two AGVs occupy the turning point at the same time. The path of AGV1 after passing the turning point is the path of AGV2 before passing the turning point: Method 1: AGV1 waits for AGV2 to pass the turning point, releases the path, and then occupies the path through the turning point. Method 2: AGV1 changes the path. Calculate the transportation time after AGV1 changes the path. If the transportation time is greater than the waiting time, use method 1. Otherwise, use method 2.

[0108] 3) The conflict point is the conflict of the AGV turning point. Two AGVs occupy the turning point at the same time. The path after the AGV passes the turning point is the path occupied by the other AGV: the AGV changes the path.

[0109] 4) The conflict point is the node conflict of the AGV turning point. Two AGVs occupy the turning point at the same time and go to different paths: one AGV waits for two unit times, and the other AGV passes first.

[0110] Figure 2 This is a schematic diagram of AGVs facing each other. Figure 3 It is a schematic diagram of AGV node conflict. Figure 4 It is a flow chart of the AGV path optimization process.

[0111] The transportation time and path of each transportation task of each AGV without path conflict are recorded to calculate the individual fitness value and plan the AGV route in workshop scheduling.

[0112] According to an existing individual x i,j To find a new candidate individual v i,j , this candidate individual is a neighbor of an existing individual:

[0113] v i,j =x i,j +a t,i ·d i,j (x i,j -x k,j )

[0114] Where k = 1, 2, ..., N, k is randomly determined, k1i; δ i,j Randomly generated in the range [-1,1], a t,i Represents the reward in the current search direction. If the individual is in v i,j The state is better than x i,j The state proves that the search direction is correct and reduces a t,i The value of is used to enhance the local search capability. If the individual is at time v i,j The status is not better than x i,j The state proves that the search direction is wrong and improves a t,i The value of is used to enhance the global search capability and help jump out of the local solution. Table 1 is the individual state and a t,i The specific situation of the value.

[0115] Table 1 Individual status relationship table

[0116] state <![CDATA[x i,j ]]> <![CDATA[v i,j ]]> Current Status feedback <![CDATA[Corresponding to a t > 1 Feasible solution Feasible solution <![CDATA[V i <In i ]]> 1 Rand[0,0.5] 2 <![CDATA[U i <In i ]]> -1 Rand[1,2] 3 <![CDATA[U i and V i No domination of each other]]> 0 Rand[0,1]

[0117] δ i,j 、a t,i To control x i,j The generation of new individuals around;

[0118] If an individual does not show any improvement within L generations, the individual is discarded; new individuals are generated according to the following formula:

[0119]

[0120] in, is a random number in the range [-1, 1]; x min,j and x max,j is the minimum and maximum value of the jth dimension in the current generation of individuals;

[0121] Step 2.4: If the non-dominated sorting hierarchy is not generated, proceed directly to step 2.5; otherwise, for the population P N Perform non-dominated sorting of express deliveries, calculate the congestion distance, and retain the better solution:

[0122]

[0123] F dism represents the crowding degree of the i-th individual in the m-th objective function. m represents the mth objective function.

[0124] x max represents the maximum value of all individuals under the m function, x min Indicates the minimum value.

[0125] Step 2.5: Integrate the new individuals generated in steps 2.3 and 2.4 into P N Form a new population P new , the scale is N;

[0126] Step 2.6: Establish an optimization model for the joint scheduling of machines and AGVs in the assembly workshop;

[0127] Step 2.7: Order P according to the level of the target value dominance relationship new To filter,

[0128] Step 2.8: Increase the current number of iterations by 1 and return to step 2.2.

[0129] The step 2.6 is as follows:

[0130] Step 2.6.1: Establish the scheduling optimization objective function for the machine and AGV to complete the production process. The optimization objectives of this function include: minimizing the maximum completion time considering production efficiency, defined as f1; minimizing energy consumption considering energy cost, defined as f2; minimizing total delay considering customer satisfaction, defined as f3; the optimization objectives are as follows:

[0131] 1) Set C i =FMt iJm +ht mL, is the completion time of the last process of the i-th workpiece, where FMt iJm represents the end time of the last process of the i-th workpiece, ht mL It represents the time required for AGV to transport from the processing machine m of the last process to the finished product area. The minimum maximum processing time is expressed as:

[0132]

[0133] Where i = 1, 2, ..., n is the index of the workpiece, j = 1, 2, ..., J is the index of the workpiece process;

[0134] 2) Set f 21 is the load energy consumption of the machine and AGV during processing, f 22 The no-load energy consumption of the machine and AGV during processing, and the minimized total energy consumption are expressed as:

[0135]

[0136] f2=f 21 +f 22

[0137] Among them, SMt ijm FMt represents the start time of the jth process of the i-th workpiece on machine m. ijm represents the end processing time of the jth process of the i-th workpiece on machine m, SAt ijw FAt represents the transportation start time of the jth process of the i-th workpiece on AGVw, ijw SMt' represents the transportation completion time of the jth process of the i-th workpiece on AGVw. ijm FMt' represents the idle start time after the jth process of the i-th workpiece on machine m is completed. ijm It represents the idle time after the jth process of the i-th workpiece on machine m is completed, SAt' ijw FAt' represents the idle start time of the transport task of the jth process of the i-th workpiece on AGVw, ijw It represents the idle end time of the transport task of the jth process of the i-th workpiece on AGVw. e , EM ne Indicates the load energy consumption and no-load energy consumption of the machine, EA e ,EA ne Indicates the load energy consumption and no-load energy consumption of AGV.

[0138] 3) Minimize total delay:

[0139]

[0140] Among them, C i , D i , R i They represent the completion time, delivery time, and raw material arrival time of job i respectively.

[0141] The step 2.7 is as follows:

[0142] Step 2.7.1: Regularize all target values; establish a hyperplane with a 3-axis intercept of 1, divide the hyperplane boundary equally, and divide the hyperplane evenly to obtain several reference points to form a reference point set Z;

[0143] Step 2.7.2: Associate all individuals in the first p layers to the nearest reference point, and calculate the distance d between each individual and its nearest reference point to associate each reference point with the individuals around it;

[0144] Step 2.7.3: Calculate the number of individuals associated with each reference point;

[0145] Step 2.7.4: For each reference point, select the first K individuals that are closest to it and associated with it to enter the next generation population P N .

[0146] The following takes the production scheduling calculation of a special assembly workshop of a certain enterprise as an example, and further illustrates a method for joint scheduling of assembly workshop machines and AGVs taking into account the optimal path of the present invention in combination with the accompanying drawings.

[0147] A method for jointly scheduling machines and AGVs in an assembly workshop considering an optimal path is provided for allocating production resources of machines and AGVs in an assembly workshop, comprising the following steps:

[0148] Step 1: Initialize 100 scheduling schemes P 100 , that is, the population size is 100;

[0149] Step 1.1: Initialize the information of the assembly workshop scheduling process, Figure 6 This is a map of an assembly workshop of a certain enterprise. There are 8 machines in the workshop and 4 AGVs. Table 2 shows the specific situation of the workshop production orders. i Indicates the type of workpiece i, O ij Indicates the jth process of workpiece i. It includes the order workpiece, raw material arrival time, process, and delivery time. There are 8 types of workpieces produced in this order, and each workpiece has 10 processes. Table 3 shows the energy consumption of each machine and AGV under load and no-load conditions per unit time.

[0150] Table 2 Orders with workpiece arrival time and delivery date (unit: minutes)

[0151]

[0152]

[0153]

[0154] Table 3 Machine and AGV numbers and energy consumption under no-load (unit: KW)

[0155] equipment M1 M2 M3 M4 M5 M6 M7 M8 AGV1 AGV2 AGV3 AGV4 serial number 1 2 3 4 5 6 7 8 9 10 11 12 No load 0.5 3 8 3 5 0.7 3 8 1.5 1.5 1.5 1.5 load 3 10 15 10 20 5 10 15 5 5 5 5

[0156] Step 1.2: Each workpiece requires 10 processes. At the beginning of assembly, AGV is required to transport the raw materials from the raw material area to the processing machine. After the machine is processed, the AGV transports it to the next processing machine for processing, and finally transports it to the finished product area. Each workpiece requires the coordination of machines and AGVs from production to storage. 10 processes require 10 machines from the beginning to storage, and AGVs are required to perform 11 transportation tasks. Therefore, each workpiece code requires 21 elements, which are composed of the machine and AGV numbers. The entire order is a vector composed of 168 elements.

[0157] Step 1.3: According to the scheduling information initialized in step 1.1, create an initial population and randomly generate 100 individuals, that is, 100 assembly workshop scheduling plans, as shown in Table 4;

[0158] Table 4 Initialized 100 individuals

[0159]

[0160]

[0161]

[0162] Step 2: Design a multi-objective learning artificial bee colony algorithm to optimize and adjust the solution;

[0163] Step 3: Select an optimized assembly workshop machine and AGV joint scheduling optimization solution for production scheduling, Figure 7 It is the Anter diagram of the optimal solution with minimum completion time. Figure 8 It is a multi-objective Pareto atlas.

[0164] Based on the traditional A* algorithm, the addition of time windows in the present invention puts forward new requirements for path planning. In a workshop, the AGV may need to reach certain specific locations within a specific time period, or avoid certain machines and equipment within a certain time period. Therefore, path planning must not only consider the length of the path, but also must meet the time window constraints during the path search process. The present invention combines Q-learning with the ABC algorithm, and can introduce the autonomous learning ability of reinforcement learning in the optimization process. Q-learning can guide the search process of the artificial bee colony algorithm by learning the reward values ​​of different scheduling strategies. In this way, the ABC algorithm can dynamically adjust the search strategy according to the actual scheduling situation, thereby further improving the efficiency and adaptability of scheduling.

[0165] As an important topic in intelligent manufacturing, the joint scheduling optimization of assembly workshop machines and AGVs for conflict-free transportation has far-reaching significance. Through reasonable scheduling optimization, not only can the transportation efficiency of AGVs in the workshop be improved, but also the coordinated operation with other equipment can be ensured to avoid conflicts and maximize resource utilization, providing strong support for the intelligent development of the manufacturing industry.

Claims

1. A method for joint scheduling of machines and AGVs in an assembly workshop considering the optimal path, characterized in that: The following steps are involved: 1) Based on the joint scheduling process of machines and AGVs in special assembly workshops, a heuristic initialization scheduling scheme is proposed; 2) Use the A* algorithm and time window to find the conflict-free shortest time path selection for AGV transportation tasks, and optimize and adjust the scheduling plan based on the shortest time path through a multi-objective learning artificial bee colony algorithm; 3) Select the optimized assembly workshop machine and AGV joint scheduling scheme for production scheduling.

2. The method for jointly scheduling machines and AGVs in an assembly workshop considering the optimal path according to claim 1, characterized in that: The step 1) comprises the following steps: 1.1) Initialize the scheduling information of AGV and machine processing; 1.2) Assume that the total number of machines is M, the machine code is m, m∈(1,M), the total number of AGVs is W, the AGV code is w, w∈(1,W), and a batch of orders requires the production of n types of workpieces, each workpiece requires J processes, the process number is j, j∈(1,J). Encode the order scheduling plan to form a real number vector of n×(2×J+1) dimensions. Each individual represents a scheduling plan. The dimensional order of the individual represents the order of the machines and AGVs in the scheduling plan during the processing. The scheduling plan encoding form of a workpiece processing process is X=|j w ,j m ,j w ,j m ,...,j w |, then the real vector form of the individual is [X1,X2,...,X n ]; 1.3) Based on the initialized scheduling information, the initial population is heuristically created to randomly generate N individuals within the set range, i.e., N special assembly workshop scheduling plans.

3. The method for jointly scheduling machines and AGVs in an assembly workshop considering the optimal path according to claim 2, characterized in that: Step 1.3) The following steps are involved: 1.3.1) Randomly generate a production process sequence based on scheduling information; 1.3.2) Traverse the AGVs and machines in the production process sequence in sequence; 1.3.3) Determine whether the traversal is completed. If completed, output the individuals including the current process sequence, AGV sequence and machine sequence; otherwise, traverse the current idle AGV; 1.3.4) Calculate the arrival time of each AGV, select the AGV number with the shortest arrival time, and generate the AGV sequence; 1.3.5) Traverse all optional machines required for the processing process, calculate the completion time on each optional machine, generate the machine number with the shortest completion time, generate the machine sequence, and return to step 1.3.3).

4. The method for jointly scheduling machines and AGVs in an assembly workshop considering the optimal path according to claim 1, characterized in that: The step 2) comprises the following steps: 2.1) Initialize the maximum number of iterations T in the artificial bee colony algorithm parameters max , the number of unimproved generations L of individuals and the total number of individuals N as the population size; 2.2) Determine whether the current number of iterations has reached the maximum number T max , if reached, stop the iteration and output the scheduling plan; otherwise, execute step 2.3); 2.3) Using A * The algorithm solves the shortest path for each individual without path conflict; 2.4) Record the transportation time and path of each transportation task of each AGV without path conflict, and generate a new individual; 2.5) According to the number of new individuals, determine whether to generate a non-dominated sorting hierarchy. If not, execute step 2.6). Otherwise, for the population P N Perform express non-dominated sorting, calculate the crowding distance, sort the crowding distance, and retain the individuals whose order meets the threshold: 2.6) Integrate the generated new individuals into P N Form a new population P new , the scale is N; 2.7) Establish an optimization model for the joint scheduling of machines and AGVs in the assembly workshop; 2.8) Based on the optimization model, P is ordered according to the hierarchy of the target value dominance relationship. new Conduct screening; 2.9) Increase the current number of iterations by 1 and return to step 2.2).

5. The method for jointly scheduling machines and AGVs in an assembly workshop considering the optimal path according to claim 4, characterized in that: The step 2.3) is specifically as follows: Set each individual to be a new individual: {x i,j |x i,j ∈[X1,X2,…,X n ],i=1,2,…,N,j=1,2,…n′(2′J+1)} Assign the shortest distance without path conflict to each new individual. The starting point and end point of each transportation task of each AGV are known in the new individual. Use A * The algorithm solves the shortest path f of the AGV's transportation task l (r): f l (r)=g(r)+h(r) Among them, l represents the AGV number, g(r) represents the length of the path that the AGV has traveled, h(r) represents the Euclidean distance between the current location of the AGV and the target end point, (x, y) represents the coordinates of the target end point of the AGV, (x', y') represents the current position coordinates of the AGV, and all tasks F(n) of each AGV are expressed as: Among them, L represents the total number of AGVs, the transportation routes of all AGVs are recorded, and the position and current time of the AGV are recorded using a time window. If the distance between two AGVs at the same time is less than one path unit, it is considered a conflict between the two AGVs.

6. The method for jointly scheduling machines and AGVs in an assembly workshop considering the optimal path according to claim 5, characterized in that: When there is a conflict between two AGVs, the following method is used to deal with the conflict: Case 1: The conflict point is not the AGV turning point. If the path is occupied and the two AGVs are obstacles to each other, the AGV that arrives later will change its path; Case 2: The conflict point is the AGV turning point. Two AGVs occupy the turning point at the same time, and the path after AGV1 passes the turning point is the path before AGV2 passes the turning point. Then calculate the transportation time after AGV1 changes the path. If the transportation time is greater than the waiting time, AGV1 waits for AGV2 to pass the turning point, releases the path, and then occupies the path through the turning point; otherwise, AGV1 changes the path; Case 3: The conflict point is the opposite conflict of the AGV turning point If two AGVs occupy an inflection point at the same time, and the path taken by the AGV after passing the inflection point is the path occupied by the other AGV, the AGV that arrives later will change its path; Case 4: The conflict point is the node conflict of the AGV inflection point If two AGVs occupy the turning point at the same time and go to different paths, one of the AGVs will wait for two unit times while the other AGV passes first.

7. The method for jointly scheduling machines and AGVs in an assembly workshop considering the optimal path according to claim 4, characterized in that: The step 2.4) is specifically as follows: According to an existing individual x i,j Find a new candidate individual v i,j : v i,j =x i,j +a t,i ·d i,j (x i,j -x k,j ) Among them, random number k = 1, 2, ..., N, k1i, δ i,j is a coefficient randomly generated in the range of [-1,1], a t,i Represents the reward in the current search direction. If the individual is in v i,j The state is better than x i,j The state proves that the search direction is correct and reduces a t,i The value of is used to enhance the local search capability. If the individual is at time v i,j The status is not better than x i,j The state proves that the search direction is wrong and improves a t,i The value of is used to enhance the global search capability and help jump out of the local solution. If an individual does not have any improvement within L generations, the individual is discarded and a new individual is generated according to the following formula: in, is a random number in the range [-1, 1], x min,j and x max,j is the minimum and maximum value of the j-th dimension in the current generation of individuals.

8. The method for jointly scheduling machines and AGVs in an assembly workshop considering the optimal path according to claim 4, characterized in that: The congestion distance is specifically: F dism represents the crowding distance of the ith individual in the mth objective function, f m represents the mth objective function, x max represents the maximum value of all individuals under the m function, x min Indicates the minimum value.

9. The method for jointly scheduling machines and AGVs in an assembly workshop considering the optimal path according to claim 4, characterized in that: The scheduling optimization objective function of the optimization model for the joint scheduling of the assembly workshop machine and AGV includes: minimizing the maximum completion time considering production efficiency, defined as f1; minimizing energy consumption considering energy cost, defined as f2; minimizing the total delay considering customer satisfaction, defined as f3, specifically: (1) Set C i =FMt iJm +ht mL , is the completion time of the last process of the i-th workpiece, where FMt iJm represents the end time of the last process of the i-th workpiece, ht mL represents the time required for AGV to transport the processing machine m from the last process to the finished product area, then the minimized maximum processing time is expressed as: Where i = 1, 2, ..., n is the index of the workpiece, j = 1, 2, ..., J is the index of the workpiece process; (2) Set f 21 is the load energy consumption of the machine and AGV during processing, f 22 The no-load energy consumption of the machine and AGV during processing, and the minimized total energy consumption are expressed as: f2=f 21 +f 22 Among them, SMt ijm FMt represents the start time of the jth process of the i-th workpiece on machine m. ijm represents the end processing time of the jth process of the i-th workpiece on machine m, SAt ijw FAt represents the transportation start time of the jth process of the i-th workpiece on AGV w, ijw SMt' represents the transportation completion time of the jth process of the i-th workpiece on AGV w. ijm FMt' represents the idle start time after the jth process of the i-th workpiece on machine m is completed. ijm It represents the idle time after the jth process of the i-th workpiece on machine m is completed, SAt' ijw FAt' represents the idle start time of the transport task of the jth process of the i-th workpiece on AGV w, ijw represents the idle end time of the transport task of the jth process of the i-th workpiece on AGV w, EM e , EM ne Indicates the load energy consumption and no-load energy consumption of the machine, EA e ,EA ne Indicates the load energy consumption and no-load energy consumption of AGV; (3) Minimize total delay: Among them, C i , D i , R i They represent the completion time, delivery time, and raw material arrival time of job i respectively.

10. The method for jointly scheduling machines and AGVs in an assembly workshop considering the optimal path according to claim 4, characterized in that: The step 2.8) comprises the following steps: 2.8.1) Regularize all target values, establish a hyperplane with a 3-axis intercept of 1, divide the hyperplane boundary equally, and divide the hyperplane evenly to obtain several reference points, forming a reference point set Z; 2.8.2) Associate all individuals in the first p layers to the nearest reference point, and calculate the distance d between each individual and its nearest reference point; 2.8.3) Calculate the number of individuals associated with each reference point; 2.8.4) For each reference point, select the first K individuals that are closest to it and are associated with it to enter the next generation population P N .

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