Resource-constrained job-shop scheduling method and system based on improved simulated annealing algorithm

Abstract

The application provides a resource-constrained job shop scheduling method and system based on an improved simulated annealing algorithm, and relates to the technical field of job shop scheduling.In the application, a job set to be processed and job shop scheduling resources are acquired, and a resource-constrained job shop scheduling model is constructed; then, the model is solved by using the improved simulated annealing algorithm to acquire an optimal scheduling scheme.On the basis of the traditional simulated annealing algorithm, machine resource consumption parameters are further considered, in the case that the total resource consumption does not exceed a given threshold, the properties and structure of the solution space are fully utilized, and a globally optimal scheduling scheme is obtained to minimize the weighted processing completion time and the sum of the weighted processing completion time and the total resource consumption.In addition, the discrete adaptive elite strategy of the algorithm enables the solution to dynamically adjust the global search and local search capabilities, avoids the population from falling into premature convergence, the precise control strategy of the algorithm always ensures the feasibility of the output solution, and the adaptive dynamic adjustment temperature strategy of the algorithm can ensure that the solution space is fully explored.

Description

Technical Field

[0001] This invention relates to the field of workshop scheduling technology, and specifically to a resource-constrained workshop scheduling method, system, storage medium, and electronic device based on an improved simulated annealing algorithm. Background Technology

[0002] Shop floor scheduling is a classic scheduling problem that optimizes one or more scheduling metrics by arranging the processing sequence of multiple jobs on multiple machines.

[0003] Simulated annealing is a common method for solving shop floor scheduling problems. However, traditional simulated annealing algorithms typically assume that machines do not consume resources and that resource supply is unlimited, which is clearly not consistent with real-world production environments.

[0004] Therefore, it is necessary to provide a workshop scheduling scheme with resource consumption constraints. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a resource-constrained workshop scheduling method, system, storage medium, and electronic device based on an improved simulated annealing algorithm, which solves the technical problem of ignoring resource consumption constraints.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A resource-constrained shop floor scheduling method based on an improved simulated annealing algorithm includes:

[0010] S1. Obtain the set of workpieces to be processed, as well as the workshop scheduling resources;

[0011] S2. Based on the set of workpieces and the workshop scheduling resources, construct a resource-constrained workshop scheduling model;

[0012] S3. The improved simulated annealing algorithm is used to solve the workshop scheduling model and obtain the optimal scheduling scheme.

[0013] Preferably, the workshop scheduling model in S2 includes:

[0014] Objective function:

[0015] Obj = minw j *C j (1)

[0016] and constraints:

[0017]

[0018]

[0019]

[0020] TC≤U (5)

[0021] x j,k ∈{0,1}(6)

[0022] The set of workpieces is N = {1, 2, ...,}, where n is the total number of workpieces;

[0023] The set of machines is M = {1,2,…,}, where m is the total number of machines;

[0024] p j Let be the processing time for workpiece j;

[0025] v k Let k be the speed of machine k;

[0026] or This represents the set of workpieces i that can be processed before workpiece j, according to the weighted shortest processing time rule, for machine k.

[0027] w j Let j be the weight of workpiece j;

[0028] C j Let be the completion time of workpiece j;

[0029] r k Let k be the resource consumption cost per unit time for machine k;

[0030] U represents the maximum number of resources given.

[0031] Tc represents the amount of resources consumed;

[0032] The objective function (1) represents minimizing the weighted job completion time;

[0033] Constraint (2) means that each workpiece must be assigned to one of the machines for processing;

[0034] Constraint (3) is used to calculate the completion time of each workpiece;

[0035] Constraint (4) is used to calculate the total resource consumption;

[0036] Constraint (5) indicates that the total resource consumption cannot exceed a given threshold;

[0037] Constraint (6) represents x j,k As the decision variable, if workpiece j is assigned to machine k for processing, then xj,k =1, otherwise 0.

[0038] Preferably, S3 includes:

[0039] S31, Parameter Initialization: Initial Temperature T max Termination temperature T min Annealing coefficient θ, Markov chain length L, coefficient of variation ρ;

[0040] S32. Generate an initial solution: For the solution X = {x1, x2, ..., ...}, j ,…, n Each x in} j x j Let j represent the machine to which workpiece j is assigned. A random integer value is generated within the interval [1, m] to form an initial solution. The objective function value and total resource consumption of the solution are calculated. If the total resource consumption of the solution is greater than a given threshold, a precise control strategy is used to optimize the solution to make it conform to the resource constraints. A sequence BEST_ of length n is created to store the best feasible solution that can be found up to the current time.

[0041] S33. At each temperature, iterate L times and use a discrete adaptive elitist strategy to generate a new solution;

[0042] S34, Update current temperature in

[0043] S35. Use the following screening strategy to determine whether to accept a new solution:

[0044] (1) If the objective function value of the new solution is better than the current optimal feasible solution and its resource consumption is less than the given threshold, then accept the new solution and update the system's optimal feasible solution; and reset the current temperature T to equal T. max ;

[0045] (2) If the objective function value of the new solution is worse than the current optimal feasible solution, but its resource consumption is less than a given threshold, then the probability of accepting the new solution is set as follows:

[0046] (3) If the objective function value of the new solution is worse than that of the current best feasible solution and its resource consumption is greater than the given threshold, the solution is re-optimized using the precise control strategy in S32; if the objective function value of the optimized solution is still greater than that of the current best feasible solution, the solution is discarded, otherwise the solution is accepted.

[0047] Where New_Obj is the objective function value of the new solution, New_TC is its resource consumption, and BEST_Obj is the objective function value of the current optimal feasible solution;

[0048] S36. Determine if the current temperature T is less than the termination temperature T. min If so, terminate and output the current best feasible solution, then decode to obtain the optimal scheduling scheme; otherwise, jump to S33 to continue generating a new solution.

[0049] Preferably, step S32 employs a precise control strategy to optimize the solution, including:

[0050] S10. Calculate the unit speed cost for each machine. And sort them according to the order of non-increasing cost per unit speed;

[0051] S20. Select the workpiece removal party: Select the machine pro with the highest current unit speed consumption cost and the number of workpieces assigned is greater than or equal to 1, and remove its current first workpiece. The number of jobs on this machine is then reduced by 1, as shown below:

[0052] pro = min{k|n k ≥1, 1≤j≤m-1}, 1≤pro≤m-1 (7)

[0053] TC≤U (8)

[0054] S30. Select the workpiece recipient: Select the machine with the shortest weighted completion time (acc) as the workpiece recipient. Add the workpieces from the outgoing machine to the machine in the receiving machine, and insert them according to the weighted shortest processing time rule. Increment the number of workpieces on the receiving machine by 1, as shown below:

[0055]

[0056] TC≤U (10)

[0057] S40. Recalculate the newly adjusted total resource consumption cost TC; if TC≤U, accept the current solution as the initial solution; otherwise, jump to S20.

[0058] Preferably, the discrete adaptive elitist strategy in S33 refers to:

[0059]

[0060] Where, α j A decimal number between [0,1] represents the probability that the job j corresponding to the new solution inherits the machine assigned to the current optimal feasible solution. j A random integer between [1, m] indicates that workpiece j is randomly assigned to any machine with a probability of 1-ρ; t is the current iteration number at each temperature.

[0061] A resource-constrained shop floor scheduling system based on an improved simulated annealing algorithm includes:

[0062] The acquisition module is used to acquire the set of workpieces to be processed, as well as workshop scheduling resources;

[0063] A construction module is used to construct a resource-constrained workshop scheduling model based on the set of workpieces and workshop scheduling resources;

[0064] The solution module is used to solve the workshop scheduling model using an improved simulated annealing algorithm to obtain the optimal scheduling scheme.

[0065] A storage medium storing a computer program for resource-constrained shop floor scheduling based on an improved simulated annealing algorithm, wherein the computer program causes a computer to execute the resource-constrained shop floor scheduling method as described above.

[0066] An electronic device, comprising:

[0067] One or more processors; memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing the resource-constrained shop scheduling method as described above.

[0068] (III) Beneficial Effects

[0069] This invention provides a resource-constrained workshop scheduling method, system, storage medium, and electronic device based on an improved simulated annealing algorithm. Compared with existing technologies, it has the following advantages:

[0070] In this invention, firstly, a set of workpieces to be processed and shop floor scheduling resources are obtained; then, based on the workpiece set and shop floor scheduling resources, a resource-constrained shop floor scheduling model is constructed; next, an improved simulated annealing algorithm is used to solve the shop floor scheduling model to obtain the optimal scheduling scheme. Building upon the traditional simulated annealing algorithm, machine resource consumption parameters are further incorporated into the design. Under the condition that the total resource consumption does not exceed a given threshold, the properties and structure of the solution space are fully utilized to obtain a globally optimal scheduling scheme that minimizes the weighted processing completion time. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1A block diagram illustrating a resource-constrained workshop scheduling method based on an improved simulated annealing algorithm, provided in an embodiment of the present invention;

[0073] Figure 2 A flowchart of an improved simulated annealing algorithm provided in an embodiment of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] This application provides a resource-constrained shop floor scheduling method, system, storage medium, and electronic device based on an improved simulated annealing algorithm, which solves the technical problem of ignoring resource consumption constraints and makes the shop floor scheduling problem better reflect the real production environment.

[0076] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0077] In the prior art, apart from the workshop scheduling process mentioned in the background art, it is usually considered that the machine does not need to consume resources and that the resource supply is unlimited.

[0078] Moreover, the temperature of the traditional simulated annealing algorithm decreases continuously, which also decreases the probability of accepting a worse solution, making it impossible to fully explore the solution space.

[0079] Secondly, when solving resource-constrained shop floor scheduling problems, traditional simulated annealing algorithms typically update the current solution by randomly selecting a number of jobs for machine adjustment, without fully utilizing the structure of elite solutions. This approach can easily lead to the solution getting stuck in a stagnant state.

[0080] Furthermore, when solving resource-constrained shop floor scheduling problems, traditional simulated annealing algorithms typically discard solutions that do not meet the resource constraints by directly discarding them or adding penalties, which can easily corrupt or skip optimal solutions. In addition, this approach cannot guarantee that the final solution will meet the resource constraints.

[0081] Accordingly, this invention improves the simulated annealing algorithm for solving the resource-constrained workshop scheduling problem, enhancing both the efficiency of the solution process and the quality of the solution. This includes:

[0082] ① Existing technical solutions typically cool the initial temperature linearly or exponentially, resulting in a continuously decreasing probability of accepting worse solutions and failing to fully explore the solution space. To address this issue, this invention proposes a self-learning temperature adjustment strategy to fully explore the solution space.

[0083] ② Existing technical solutions typically use random mutation to update solutions, leading to solutions quickly stagnating. To address this problem, this invention proposes a discrete adaptive elite mutation strategy to generate new solutions, preserving the structure of elite solutions to the greatest extent possible while adaptively updating them to prevent stagnation.

[0084] ③ Existing technical solutions typically handle infeasible solutions by discarding them or adding penalties, which can easily destroy or skip the optimal solution. To address this problem, this invention proposes a precise control strategy that preserves the structure of the solution to the greatest extent possible while ensuring the feasibility of the final output solution.

[0085] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0086] Example:

[0087] like Figure 1 As shown, this embodiment of the invention provides a resource-constrained shop floor scheduling method based on an improved simulated annealing algorithm, including:

[0088] S1. Obtain the set of workpieces to be processed, as well as the workshop scheduling resources;

[0089] S2. Based on the set of workpieces and the workshop scheduling resources, construct a resource-constrained workshop scheduling model;

[0090] S3. The improved simulated annealing algorithm is used to solve the workshop scheduling model and obtain the optimal scheduling scheme.

[0091] Based on the traditional simulated annealing algorithm, this invention further incorporates machine resource consumption parameters. Under the condition that the total resource consumption does not exceed a given threshold, it makes full use of the properties and structure of the solution space to obtain a globally optimal scheduling scheme to minimize the weighted processing completion time.

[0092] The following will detail each step of the above solution:

[0093] In step S1, the set of workpieces to be processed and the workshop scheduling resources are obtained.

[0094] It is necessary to further explain that the embodiments of the present invention are aimed at the shop floor scheduling problem with resource consumption constraints, which can be briefly described as: a set of workpieces N = {1,2,…,} and a set of machines M = {1,2,…,}. The processing time for each workpiece j is p. j Each workpiece j has a weight w. j The processing speed of each machine is v. k And each machine k has a unit resource consumption cost r. k Each machine can only process one workpiece at a time; each workpiece can only be assigned to any one of the machines for processing, and the processing cannot be interrupted; there is no priority order between workpieces.

[0095] Therefore, this step first provides the parameters and decision variables that will be used in the subsequent model:

[0096] The set of workpieces is N = {1, 2, ...,}, where n is the total number of workpieces;

[0097] The set of machines is M = {1,2,…,}, where m is the total number of machines;

[0098] p j Let be the processing time for workpiece j;

[0099] v k Let k be the speed of machine k;

[0100] This represents the set of workpieces i that can be processed before workpiece j for machine k, according to the weighted shortest processing time (WSPT) rule.

[0101] w j Let j be the weight of workpiece j;

[0102] C j Let be the completion time of workpiece j;

[0103] r k Let k be the resource consumption cost per unit time for machine k;

[0104] U represents the maximum number of resources given.

[0105] TC represents the amount of resources consumed;

[0106]

[0107] In step S2, a resource-constrained workshop scheduling model is constructed based on the set of workpieces and workshop scheduling resources.

[0108] This step involves constructing a mathematical model for the resource-constrained shop floor scheduling problem, namely, a resource-constrained shop floor scheduling model, including:

[0109] Objective function:

[0110] Obj = minw j *C j (1)

[0111] and constraints:

[0112]

[0113]

[0114]

[0115] TC≤U (5)

[0116] x j,k ∈{0,1} (6)

[0117] Wherein, the objective function (1) represents minimizing the weighted job completion time;

[0118] Constraint (2) means that each workpiece must be assigned to one of the machines for processing;

[0119] Constraint (3) is used to calculate the completion time of each workpiece;

[0120] Constraint (4) is used to calculate the total resource consumption;

[0121] Constraint (5) indicates that the total resource consumption cannot exceed a given threshold;

[0122] Constraint (6) represents x j,k As the decision variable, if workpiece j is assigned to machine k for processing, then x j,k =1, otherwise 0.

[0123] In step S3, the improved simulated annealing algorithm is used to solve the workshop scheduling model and obtain the optimal scheduling scheme.

[0124] like Figure 2 As shown, this step includes:

[0125] S31, Parameter Initialization: Initial Temperature T max Termination temperature T min Annealing coefficient Markov chain length L, coefficient of variation ρ;

[0126] S32. Generate initial solution: For the solution X = {x1, x2, ..., x...} j ,…,x nEach x in} j x j Let j represent the machine to which workpiece j is assigned. A random integer value is generated within the interval [1, m] to form an initial solution. The objective function value and total resource consumption of the solution are calculated. If the total resource consumption of the solution is greater than a given threshold, a precise control strategy is used to optimize the solution to make it conform to the resource constraints. A sequence BEST_X of length n is created to store the best feasible solution that can be found up to the current time.

[0127] S33. At each temperature, iterate L times and use a discrete adaptive elitist strategy to generate a new solution;

[0128] S34, Update current temperature in The adaptive dynamic temperature adjustment strategy designed here can fully explore the solution space;

[0129] S35. Use the following screening strategy to determine whether to accept a new solution:

[0130] (1) If the objective function value of the new solution is better than the current optimal feasible solution and its resource consumption is less than the given threshold, then accept the new solution and update the system's optimal feasible solution; and reset the current temperature T to equal T. max ;

[0131] (2) If the objective function value of the new solution is worse than the current optimal feasible solution, but its resource consumption is less than a given threshold, then the probability of accepting the new solution is set as follows:

[0132] (3) If the objective function value of the new solution is worse than that of the current best feasible solution and its resource consumption is greater than the given threshold, the solution is re-optimized using the precise control strategy in S32; if the objective function value of the optimized solution is still greater than that of the current best feasible solution, the solution is discarded, otherwise the solution is accepted.

[0133] Where New_Obj is the objective function value of the new solution, New_TC is its resource consumption, and BEST_Obj is the objective function value of the current optimal feasible solution;

[0134] S36. Determine if the current temperature T is less than the termination temperature T. min If so, terminate and output the current best feasible solution, then decode to obtain the optimal scheduling scheme; otherwise, jump to S33 to continue generating a new solution.

[0135] Specifically, S32 employs a precise control strategy to optimize the solution, including:

[0136] S10. Calculate the unit speed cost for each machine. And sort them according to the order of non-increasing cost per unit speed;

[0137] S20. Select the workpiece removal party: Select the machine pro with the highest current unit speed consumption cost and the number of workpieces assigned is greater than or equal to 1, and remove its current first workpiece. The number of jobs on this machine is then reduced by 1, as shown below:

[0138] pro = min{k|n k ≥1, 1≤j≤m-1}, 1≤pro≤m-1 (7)

[0139] TC≤U (8)

[0140] S30. Select the workpiece recipient: Select the machine with the shortest weighted completion time (acc) as the workpiece recipient. Add the workpieces from the outgoing machine to the machine in the receiving machine, and insert them according to the weighted shortest processing time rule. Increment the number of workpieces on the receiving machine by 1, as shown below:

[0141]

[0142] TC≤U (10)

[0143] S40. Recalculate the newly adjusted total resource consumption cost TC; if TC≤U, accept the current solution as the initial solution; otherwise, jump to S20.

[0144] As mentioned above, the precise adjustment strategy for infeasible solutions designed in the embodiments of the present invention can precisely control infeasible solutions, ensuring their feasibility while preserving their structure.

[0145] Specifically, the discrete adaptive elitist strategy in S33 refers to:

[0146]

[0147] Where, α j A decimal number between [0,1] represents the probability that the job j corresponding to the new solution inherits the machine assigned to the current optimal feasible solution. j A random integer between [1,] indicates that workpiece j is randomly assigned to any machine with a probability of 1-.

[0148]

[0149] Here, t represents the current iteration number at each temperature. To balance the diversity and convergence of solutions, a gradually increasing approach is adopted. In the initial stage, this allows for a higher probability of accepting new mutations and exploring more of the solution space. In the later stages, the structure of the solution is preserved, and mutations are only performed on a few locations to generate new solutions.

[0150] As mentioned above, the discrete adaptive elite strategy designed in the embodiments of the present invention can effectively balance the conflicts between global search and local search, diversity and convergence, and avoid premature convergence of the solution while preserving the structure of the elite solution.

[0151] This invention provides a resource-constrained shop floor scheduling system based on an improved simulated annealing algorithm, comprising:

[0152] The acquisition module is used to acquire the set of workpieces to be processed, as well as workshop scheduling resources;

[0153] A construction module is used to construct a resource-constrained workshop scheduling model based on the set of workpieces and workshop scheduling resources;

[0154] The solution module is used to solve the workshop scheduling model using an improved simulated annealing algorithm to obtain the optimal scheduling scheme.

[0155] This invention provides a storage medium storing a computer program for resource-constrained shop scheduling based on an improved simulated annealing algorithm, wherein the computer program causes a computer to execute the resource-constrained shop scheduling method as described above.

[0156] This invention provides an electronic device, comprising:

[0157] One or more processors; memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing the resource-constrained shop scheduling method as described above.

[0158] It is understood that the resource-constrained workshop scheduling system, storage medium, and electronic device based on the improved simulated annealing algorithm provided in the embodiments of the present invention correspond to the resource-constrained workshop scheduling method based on the improved simulated annealing algorithm provided in the embodiments of the present invention. The explanations, examples, and beneficial effects of the relevant contents can be referred to the corresponding parts of the resource-constrained workshop scheduling method, and will not be repeated here.

[0159] In summary, compared with existing technologies, it has the following beneficial effects:

[0160] 1. Based on the traditional simulated annealing algorithm, this embodiment of the invention further incorporates machine resource consumption parameters. Under the condition that the total resource consumption does not exceed a given threshold, it makes full use of the properties and structure of the solution space to obtain the globally optimal scheduling scheme to minimize the weighted processing completion time.

[0161] 2. The precise adjustment strategy for infeasible solutions designed in this embodiment of the invention can precisely control infeasible solutions, preserving the structure of the solution while ensuring its feasibility.

[0162] 3. The discrete adaptive elite strategy designed in the embodiments of the present invention can effectively balance the conflict between global search and local search, diversity and convergence, and avoid premature convergence of the solution while preserving the structure of the elite solution.

[0163] 4. The adaptive dynamic temperature adjustment strategy designed in the embodiments of the present invention can fully explore the solution space.

[0164] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0165] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for resource-constrained job-shop scheduling based on an improved simulated annealing algorithm, characterized in that, The method comprises the following steps: S1, acquiring a workpiece set to be processed and workshop scheduling resources; S2, constructing a resource-constrained workshop scheduling model according to the workpiece set and the workshop scheduling resources; S3, solving the workshop scheduling model by using an improved simulated annealing algorithm to acquire an optimal scheduling scheme; The workshop scheduling model in S2 comprises: an objective function: Obj = min w j *C j (1) and constraint conditions: TC≤U (5) x j,k ∈{0,1} (6) wherein the workpiece set is N={1, 2, …, n}, n is the total number of workpieces; the machine set is M={1, 2, …, m}, m is the total number of machines; p j is the processing time for the workpiece j; v k V is the velocity of the machine k; denotes the set of jobs i that are processed before job j on machine k according to the weighted shortest processing time rule; w j w is the weight of the workpiece j; C j the completion time for the workpiece j; r k rki is the cost per unit of resource consumed by machine k in time unit i; U is a given maximum resource quantity; TC is the consumed resource quantity; the objective function (1) represents a minimum weighted workpiece completion time sum; the constraint (2) represents that each workpiece must be allocated to one machine for processing; the constraint (3) is used for calculating the completion time of each workpiece; the constraint (4) is used for calculating the total resource consumption; the constraint (5) represents that the total resource consumption cannot exceed a given threshold value; Constraint (6) says that x j,k For the decision variable, if workpiece j is assigned to machine k for processing, then x j,k = 1, otherwise 0. S3 comprises the following steps: S31, parameter initialization: initial temperature T max , termination temperature T min , annealing coefficient Markov chain length L, coefficient of variation p; S32, generating initial solution: for each x j ,…,x n} of the solution X={x1,x2,…,x j , x j represents the machine to which the workpiece j is allocated, an integer value is randomly generated in the interval [1, m] to form an initial solution, the objective function value and the total resource consumption of the solution are calculated, and if the total resource consumption of the solution is greater than a given threshold, the solution is optimized by using the precise control strategy to make it meet the resource constraints; a sequence BEST_X with a length of n is created to save the optimal feasible solution that can be found at present; S33, at each temperature, iteratively generating a new solution L times by using a discrete adaptive elitist strategy; S34, update current temperature wherein S35, judging whether to accept the new solution by using the following screening strategy: (1) If the objective function value of the new solution is better than the current best feasible solution and its consumed resource amount is less than a given threshold, accept the new solution and update the system best feasible solution; and reset the current temperature T equal to T max ; (2) If the new solution has a better objective function value than the current best feasible solution but its resource consumption is less than a given threshold, then the probability of accepting the new solution is set to (3) if the objective function value of the new solution is less than that of the current optimal feasible solution and the resource consumption of the new solution is greater than the given threshold value, the solution is re-optimized by using the accurate control strategy in S32; if the objective function value of the optimized solution is still greater than that of the current optimal feasible solution, the solution is discarded, otherwise the solution is accepted; wherein New_Obj is the objective function value of the new solution, and BEST_Obj is the objective function value of the current optimal feasible solution; S36, judging whether the current temperature T is less than the termination temperature T min If yes, terminate and output the current optimal feasible solution to obtain the optimal scheduling scheme after decoding; otherwise, jump to S33 to continue generating a new solution.

2. The resource-constrained job shop scheduling method of claim 1, wherein, the accurate control strategy in S32 is used to optimize the solution, and the method comprises the following steps: S10, calculating the unit speed consumption cost of each machine and sorting according to non-increasing order of the unit speed consumption cost; S20, selecting a workpiece moving-out party: selecting a machine pro with the highest unit speed consumption cost and the number of allocated workpieces greater than or equal to 1 as the workpiece moving-out party, moving out the first workpiece of the machine, and reducing the number of jobs on the machine by 1, as shown in the following formula: pro = min{k | n k ≥ 1, 1 < j < m - 1}, 1 < pro < m - 1 (7) TC≤U (8) S30, selecting a workpiece accepting party: selecting a machine acc with the minimum weighted completion time sum as the workpiece accepting party, adding the workpiece of the moving-out party to the machine where the accepting party is located, and inserting the workpiece according to the weighted shortest processing time rule, and increasing the number of workpieces on the accepting party by 1, as shown in the following formula: TC≤U (10) S40, recalculating the total resource consumption cost TC of the new adjustment; if TC≤U, the current solution is accepted as an initial solution; otherwise, jumping to S20.

3. The resource-constrained job shop scheduling method of claim 1, wherein, The discrete adaptive elitist strategy in S33 is that: where, a j is a decimal number between [0, 1] representing the probability that the new solution for job j inherits the machine assigned to the current best feasible solution, τ j is a random integer between [1, m] representing that job j is randomly assigned to any machine with probability 1- p; t is the current iteration number at each temperature.

4. A resource-constrained job-shop scheduling system based on an improved simulated annealing algorithm, characterized in that, The system comprises: an acquisition module, configured to acquire a workpiece set to be processed and workshop scheduling resources; a construction module, configured to construct a resource-constrained workshop scheduling model according to the workpiece set and the workshop scheduling resources; a solving module, configured to solve the workshop scheduling model by using an improved simulated annealing algorithm to acquire an optimal scheduling scheme.

5. A storage medium, characterized by A computer program for resource-constrained job-shop scheduling based on an improved simulated annealing algorithm is stored in the storage, wherein the computer program causes a computer to execute the resource-constrained job-shop scheduling method according to any one of claims 1-3.

6. An electronic device, comprising: comprise: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the resource-constrained job-shop scheduling method according to any one of claims 1-3.

Images