Multi-layer workshop facility layout and logistics configuration optimization method

Abstract

The invention provides a multi-layer workshop facility layout and logistics configuration optimization method, which comprises the following steps of establishing a workshop facility layout and material transportation resource configuration joint optimization model by taking minimization of logistics cost as a target; and solving the workshop facility layout and material transportation resource allocation joint optimization model through a simulation-based heuristic hybrid optimization algorithm to obtain a facility layout and material transportation resource allocation optimization result. Compared with the prior art, the problems of low yield, long completion time, high logistics cost and the like caused by unreasonable workshop facility layout and transportation resource allocation can be solved, the logistics transportation efficiency in the multi-layer workshop is effectively improved, and the logistics cost is saved.

Description

Technical Field

[0001] The present invention is applicable to the field of logistics technology, and in particular relates to a multi-layer workshop facility layout and logistics configuration optimization method. Background Art

[0002] In the logistics and transportation system of multi-layer flexible workshops, the controllable costs in the workshop are mainly reflected in the logistics costs. Automated guided vehicles (AGVs) and elevators jointly undertake the task of transporting materials. The unreasonable layout of workshop equipment leads to low AGV transportation efficiency and frequent cross-floor processing of materials. The unreasonable speed of transport vehicles may lead to too long workshop production cycle or too low utilization rate of transport vehicles, resulting in energy waste. The unreasonable layout of workshop facilities and the allocation of material transportation resources lead to increased transportation costs, affecting the system production cycle and system output rate, and affecting order delivery. In the layout of facilities and the allocation of material transportation resources in multi-layer flexible workshops, each sub-problem will significantly affect the production efficiency and energy consumption of the workshop. Independent optimization between sub-problems will increase the production energy consumption and processing time of the workshop.

[0003] There are few studies on the joint optimization of facility layout and material transportation resource allocation in multi-layer flexible workshops, which focus on problems such as low output rate, long completion time and high logistics costs caused by unreasonable workshop facility layout and transportation resource allocation.

[0004] Therefore, a new method for optimizing the layout and logistics configuration of multi-storey workshop facilities is urgently needed to solve the above technical problems. Summary of the invention

[0005] The present invention proposes a method for optimizing the layout of multi-layer workshop facilities and logistics configuration, aiming to solve the problems of low output rate, long completion time and high logistics cost caused by unreasonable layout of workshop facilities and transportation resource configuration, effectively improve the logistics transportation efficiency in multi-layer workshops and save logistics costs.

[0006] The present invention proposes a multi-layer workshop facility layout and logistics configuration optimization method, comprising the following steps:

[0007] S1. With the goal of minimizing logistics costs, a joint optimization model for workshop facility layout and material transportation resource allocation is established;

[0008] S2. Solving the joint optimization model of the workshop facility layout and material transportation resource allocation by a simulation-based heuristic hybrid optimization algorithm to obtain optimization results of the facility layout and material transportation resource allocation.

[0009] Preferably, the joint optimization model of workshop facility layout and material transportation resource allocation satisfies the following constraints:

[0010] Constrained optimization objectives;

[0011]

[0012] Among them, X * It represents the process arrangement and combination that minimizes the total energy consumption cost; represents the speed of the automatic guided vehicles in each workshop when minimizing the total energy consumption cost; represents the elevator speed when the total energy consumption cost is minimized; Q represents the total transportation cost; X={x i} represents the combination vector of device layout arrangement; V A = {v k} represents the speed configuration vector of the automatic guided vehicle in each workshop; V E represents the elevator speed; r and n represent the number of materials randomly transported by the elevator and the number of materials randomly transported by the automatic guided vehicle, respectively; k and m represent the workshop floor number and the number of automatic guided vehicles, respectively; β r It indicates the load rate when the elevator load is r; is the load rate of the mth AGV in the kth floor workshop when the load is n; represents the speed of the mth AGV in the kth floor workshop; D and They represent the total running distance of the elevator and the total running distance of the mth automatic guided vehicle in the kth floor workshop respectively; G E and G A They represent the weight factor of the elevator and the weight factor of the automatic guided vehicle respectively; ω represents the energy consumption coefficient;

[0013] Constrain one device to correspond to one process;

[0014]

[0015] Among them, S represents the number of processes, z ij represents a binary variable;

[0016] Constrain the speed of the automatic guided vehicle and the elevator speed;

[0017]

[0018] Among them, v min and v max They represent the upper and lower limits of the automatic guided vehicle speed respectively. and Represents the upper and lower limits of the elevator speed;

[0019] Constrain the average output rate and average production cycle of the workshop system to ensure that the workshop system can meet the production capacity and order delivery requirements;

[0020] E{θ(X,V A ,V E :ε)}≥θ min ;

[0021] E{Γ(X,V A ,V E :ε)}≤Γ max ;

[0022] Where E{·} represents the mathematical expectation of the random function; ε represents the random element; θ min represents the lower limit of the average yield; Γ max Indicates the upper limit of the average production cycle.

[0023] Preferably, step S2 includes the following sub-steps:

[0024] S21, setting the maximum number of iterations, the population size, and randomly generating an initial population; establishing a workshop simulation model, the workshop simulation model is used to obtain the average output rate and the average production cycle, simulating according to the workshop simulation model, obtaining the fitness value of each individual, and counting the performance index corresponding to each individual, if the performance index of the individual does not meet the preset performance index constraint, then introducing a penalty factor to penalize the objective function value of the individual that does not meet the preset index;

[0025] S22, select the individual with the best fitness in the population as the parent;

[0026] S23, performing crossover processing and mutation processing;

[0027] S24, perform simulation again to calculate the fitness value of the offspring individual, and if the offspring individual does not meet the preset performance indicator constraint, introduce a penalty factor to penalize the objective function value of the offspring individual;

[0028] S25, setting the initial temperature and the minimum temperature, and performing simulated annealing local optimization processing on the offspring individuals;

[0029] S26, merge the offspring individuals with the initial population, and select half of the individuals with the best fitness as the new population;

[0030] S27. Determine whether the number of iterations is less than the maximum number of iterations. If so, return to step 22; if not, the optimization ends and the current result is output as the optimization result of the facility layout and material transportation resource configuration.

[0031] Preferably, step S25 includes the following sub-steps:

[0032] S251, locally optimize the individuals in the offspring individuals, and set the fitness value of the current individual to the optimal solution;

[0033] S252, performing disturbance to generate a new solution;

[0034] S253, performing simulation to calculate the objective function value of the new solution, and if the preset performance indicator constraint is not satisfied, introducing a penalty factor into the new solution;

[0035] S254, judging whether the new solution is greater than the current solution, if so, accepting the new solution as the new current solution; otherwise, randomly accepting the new solution as the new current solution with a preset probability; judging whether the current solution is greater than the optimal solution, if so, updating the current solution to the new optimal solution;

[0036] S255, perform cooling process. If the current temperature is greater than the minimum temperature, return to step 252; otherwise, proceed to step S256;

[0037] S256, determine whether all the offspring individuals have been locally optimized, if so, proceed to step 26; if not, return to step 251.

[0038] Preferably, the crossover process is based on a partial mapping crossover method.

[0039] Preferably, the heuristic hybrid optimization algorithm is based on a simulated annealing algorithm and a genetic algorithm.

[0040] Compared with the prior art, the present invention establishes a joint optimization model of workshop facility layout and material transportation resource configuration with the goal of minimizing logistics costs; solves the joint optimization model of workshop facility layout and material transportation resource configuration through a simulation-based heuristic hybrid optimization algorithm to obtain the optimization results of facility layout and material transportation resource configuration. The present invention can solve the problems of low output rate, long completion time and high logistics costs caused by unreasonable workshop facility layout and transportation resource configuration, effectively improve the logistics transportation efficiency in multi-layer workshops, and save logistics costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be described in detail below in conjunction with the accompanying drawings. The above and other aspects of the present invention will become clearer and easier to understand through the detailed description made in conjunction with the following drawings. In the accompanying drawings:

[0042] Figure 1 It is a flowchart of a multi-layer workshop facility layout and logistics configuration optimization method provided by an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of a multi-layered workshop provided by an embodiment of the present invention;

[0044] Figure 3 is a schematic diagram of an encoding method provided by an embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram of cross processing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] Please refer to Figure 1-Figure 4 The present invention provides a method for optimizing the layout and logistics configuration of multi-layer workshop facilities, comprising the following steps:

[0048] S1. With the goal of minimizing logistics costs, a joint optimization model for workshop facility layout and material transportation resource allocation is established.

[0049] In the embodiments of the present invention, please refer to Figure 2 The workshop includes a central warehouse, an elevator room, a processing area, an automatic guided vehicle (AGV) and tracks. The central warehouse consists of a raw material warehouse and a finished product storage area. The elevator room contains elevators, elevator waiting areas and elevator unloading areas. The processing area contains several equal parallel manufacturing units, each of which consists of processing equipment, a buffer area to be processed and a finished buffer area. Each processing equipment only processes a specific process, and each corresponds to a buffer area to be processed and a finished buffer area with limited capacity. Different products flow between the manufacturing units according to the load balancing principle according to their process flow. This process is achieved with the help of multiple automatic guided vehicles and elevators. The automatic guided vehicle runs on the workshop track and is only responsible for the handling work on this floor. When the product needs to be processed across floors, the automatic guided vehicle will move the product to the elevator waiting area and transport it across floors by elevator.

[0050] There are three tasks that the automatic guided vehicle performs: going to the raw material warehouse in the central warehouse to pick up materials, and transporting the raw materials to the corresponding processing buffer area according to the process flow of the products to be processed; transporting the semi-finished products in a completed buffer area to the processing buffer area corresponding to the next process. If the next process is on other floors, it will be transported to the elevator waiting area; transporting the finished products in the completed buffer area to the finished product area of ​​the central warehouse.

[0051] In the embodiment of the present invention, the joint optimization model of workshop facility layout and material transportation resource allocation satisfies the following constraints:

[0052] Constrained optimization objectives;

[0053]

[0054] Among them, X * It represents the process arrangement and combination that minimizes the total energy consumption cost; represents the speed of the automatic guided vehicles in each workshop when minimizing the total energy consumption cost; represents the elevator speed when the total energy consumption cost is minimized; Q represents the total transportation cost; X={x i} represents the combination vector of device layout arrangement; V A = {v k} represents the speed configuration vector of the automatic guided vehicle in each workshop; V E represents the elevator speed; r and n represent the number of materials randomly transported by the elevator and the number of materials randomly transported by the automatic guided vehicle, respectively; k and m represent the workshop floor number and the number of automatic guided vehicles, respectively; β r It indicates the load rate when the elevator load is r; is the load rate of the mth AGV in the kth floor workshop when the load is n; represents the speed of the mth AGV in the kth floor workshop; D and They represent the total running distance of the elevator and the total running distance of the mth automatic guided vehicle in the kth floor workshop respectively; G E and G A They represent the weight factor of the elevator and the weight factor of the automatic guided vehicle respectively; ω represents the energy consumption coefficient;

[0055] Constrain one device to correspond to one process;

[0056]

[0057] Among them, S represents the number of processes, z ij represents a binary variable;

[0058] Constrain the speed of the automatic guided vehicle and the elevator speed;

[0059] v min ≤v≤v max ;

[0060]

[0061] Among them, v min and v max They represent the upper and lower limits of the automatic guided vehicle speed respectively. and Represents the upper and lower limits of the elevator speed;

[0062] Constrain the average output rate and average production cycle of the workshop system to ensure that the workshop system can meet the production capacity and order delivery requirements;

[0063] E{θ(X,V A ,V E :ε)}≥θ min ;

[0064] E{Γ(X,V A ,V E :ε)}≤Γ max ;

[0065] Where E{·} represents the mathematical expectation of the random function; ε represents the random element; θ min represents the lower limit of the average yield; Γ max Indicates the upper limit of the average production cycle.

[0066] S2. Solving the joint optimization model of the workshop facility layout and material transportation resource allocation by a simulation-based heuristic hybrid optimization algorithm to obtain optimization results of the facility layout and material transportation resource allocation.

[0067] In the embodiment of the present invention, step S2 includes the following sub-steps:

[0068] S21. Set the maximum number of iterations, the population size, and randomly generate an initial population; establish a workshop simulation model, wherein the workshop simulation model is used to obtain the average output rate and the average production cycle, perform simulation according to the workshop simulation model, obtain the fitness value of each individual, and count the performance indicators corresponding to each individual. If the performance indicator of the individual does not meet the preset performance indicator constraint, a penalty factor is introduced to penalize the objective function value of the individual that does not meet the preset indicator.

[0069] S22, select the individual with the best fitness in the population as the parent;

[0070] S23, performing crossover processing and mutation processing;

[0071] S24, perform simulation again to calculate the fitness value of the offspring individual, and if the offspring individual does not meet the preset performance indicator constraint, introduce a penalty factor to penalize the objective function value of the offspring individual;

[0072] S25, setting the initial temperature and the minimum temperature, and performing simulated annealing local optimization processing on the offspring individuals;

[0073] S26, merge the offspring individuals with the initial population, and select half of the individuals with the best fitness as the new population;

[0074] S27. Determine whether the number of iterations is less than the maximum number of iterations. If so, return to step 22; if not, the optimization ends and the current result is output as the optimization result of the facility layout and material transportation resource configuration.

[0075] In the embodiment of the present invention, step S25 includes the following sub-steps:

[0076] S251, locally optimize the individuals in the offspring individuals, and set the fitness value of the current individual to the optimal solution;

[0077] S252, performing disturbance to generate a new solution;

[0078] S253, performing simulation to calculate the objective function value of the new solution, and if the preset performance indicator constraint is not satisfied, introducing a penalty factor into the new solution;

[0079] S254, judging whether the new solution is greater than the current solution, if so, accepting the new solution as the new current solution; otherwise, randomly accepting the new solution as the new current solution with a preset probability; judging whether the current solution is greater than the optimal solution, if so, updating the current solution to the new optimal solution;

[0080] S255, perform cooling process. If the current temperature is greater than the minimum temperature, return to step 252; otherwise, proceed to step S256;

[0081] S256, determine whether all the offspring individuals have been locally optimized, if so, proceed to step 26; if not, return to step 251.

[0082] In an embodiment of the present invention, the crossover processing is based on a partial mapping crossover method.

[0083] In an embodiment of the present invention, the heuristic hybrid optimization algorithm is based on a simulated annealing algorithm and a genetic algorithm.

[0084] Specifically, in order to accurately express the multi-layer flexible job shop simulated by the simulation model, the variable symbols involved in the shop simulation model are defined. The symbol definitions are shown in Table 1.

[0085] Table 1: Workshop simulation model parameter settings

[0086]

[0087] The workshop simulation model meets the following rules:

[0088] (1) Consider a system with multiple types of workpiece inputs. The arrival of workpieces is independent of each other and obeys a Poisson distribution with mean λ.

[0089] (2) The processing equipment can automatically load and unload workpieces and can only process one workpiece at a time. The processing times are independent of each other and follow the negative exponential distribution with a rate of μ according to the processing route of each material.

[0090] (3) The service rules for all processing units in the workshop are first-come, first-served. The raw material warehouse and finished product storage area are not considered to have capacity restrictions, and other buffer areas have limited capacity and the same capacity.

[0091] (4) Consider multiple AGVs. Each AGV can serve any processing unit in the system, and each operation is independent of each other. The workshop obtains the transportation task based on the load balancing principle between processing stations, and then selects an AGV to receive the task, without considering the loading and unloading time of the AGV. When there are multiple idle AGVs available, the processing unit selects the AGV to assign the task based on the proximity principle.

[0092] (5) When the automatic guided vehicle transports the finished product to the finished product storage area, the finished product leaves the workshop immediately.

[0093] (6) The elevator's service rule for materials on the current floor is "first come, first served". For service requests from other floors, priority is given to requests in the same direction as the elevator's movement.

[0094] According to the rules, materials arrive at a certain time interval, and the automatic guided vehicle performs the handling task on the one-way circular track according to the corresponding process path of the materials. The system first prioritizes the handling task requests of certain processing units based on the load balancing principle, and then uses the current service object as a benchmark to find the nearest non-full-loaded automatic guided vehicle to accept the task. When the automatic guided vehicle has no handling task, it goes to a specific area to wait for the task and does not occupy the main road.

[0095] The batch transport feature of the automatic guided vehicle improves the operation efficiency of the workshop. The destination buffers of the materials loaded by the automatic guided vehicle can be different, and the number of materials carried in each handling task is affected by the service object. In order to reduce the congestion of the system, the minimum value of the remaining capacity of the target buffer, the number of materials of the current service object and the remaining capacity of the automatic guided vehicle itself is taken as the single handling quantity of the automatic guided vehicle, which effectively avoids the waiting of the automatic guided vehicle during the loading and unloading process.

[0096] The equipment layout optimization problem is a typical combinatorial optimization problem, and its search space grows exponentially with the increase in the number of equipment. The present invention is a joint optimization of layout and transportation resource allocation, so the solution space of the problem is often larger, and the efficiency of general traditional algorithms is not high. Due to the complexity of the problem, the present invention proposes a simulated annealing hybrid optimization algorithm based on a genetic algorithm. By combining the global search of the genetic algorithm with the local optimization of the simulated annealing algorithm, it is beneficial to enhance the convergence speed, robustness and ability of the algorithm to jump out of the local optimal solution. Therefore, the present invention embeds simulation into the hybrid optimization algorithm to solve the problem to optimize the process layout and material transportation resource allocation.

[0097] The joint optimization of workshop layout and logistics resources includes three sub-problems: workshop equipment layout optimization, automatic guided vehicle speed configuration optimization on each floor, and elevator speed configuration optimization. Therefore, the chromosome is encoded into three gene chains, namely, equipment layout gene chain X, automatic guided vehicle speed gene chain V, and elevator speed gene chain R. Taking a three-story workshop, 12 processing equipment, and a single elevator as an example, the joint optimization gene chain is as follows: Figure 3 shown.

[0098] The position of each gene in the X gene chain corresponds to the position of the process represented by the gene in the workshop. For example, the first gene in the gene chain represents that equipment 5 is set at position 1 in the workshop; each gene in the V gene chain represents the number of automatic guided vehicles on each floor of the workshop; the gene in the R gene chain represents the speed of the elevator, and it contains only one gene, which means that there is only one elevator in the workshop.

[0099] The simulation is embedded in the hybrid algorithm. Each time a new solution is generated, a simulation needs to be started. In order to avoid increasing the computing time by generating duplicate solutions, when a new solution is generated, the hash table is used to check whether it is repeated. If it is a valid solution, it is stored in the hash table and the iterative optimization process continues. Otherwise, the solution is perturbed until a non-duplicate solution is generated. Since this problem is a joint optimization problem, the X gene chain represents the permutation problem, and the V and R gene chains represent real number coding. Different methods are used to perturb the initial solution for different gene chains.

[0100] In the crossover process, the process arrangement gene chain X is crossovered, requiring all devices to appear and only appear once, so a partial mapping crossover method (PMX) is used. By randomly selecting two intersections of the X gene chain, exchanging the genes of the parent individual in the interval, establishing a mapping relationship for the genes in the crossover interval, and adjusting the genes outside the crossover interval according to the mapping relationship to ensure that the genes are unique, the PMX crossover method used in the present invention is as follows Figure 4 As shown. For the V gene chain, the single-point crossover method is adopted; since there is only one gene in the R gene chain, the average value crossover method is selected, and the average value of the parent gene is taken as the offspring gene.

[0101] In the mutation process, for the X chain, four mutation methods are defined: exchange method, inversion method, insertion method and subsequence scrambling method, and one method is randomly selected for mutation operation. For the V chain, two mutation methods are defined: randomly swapping the positions of two genes; randomly selecting a gene for small adjustments and satisfying the constraints. For the R chain, a small adjustment method is adopted while ensuring that the speed constraint is met.

[0102] For the SA local optimization perturbation to generate new solutions, when performing local optimization on the offspring individuals, the exchange position method and the insertion method are preferred to perturb the equipment layout arrangement and combination. When these two strategies no longer produce non-repeating solutions, the reversal method and the scrambled subsequence method are randomly selected to generate new solutions, because the exchange position strategy can perform detailed searches in the local area of ​​the solution space, which helps to explore the local optimal solution in depth, while the insertion strategy can more flexibly adjust the structure of the solution, improve the diversity of solutions, and make up for the shortcomings of the exchange position strategy. The priority use of these two strategies can ensure that the information of existing solutions is fully utilized in the local search stage and increase the probability of finding a better solution. However, due to the limited diversity of solutions generated by these two methods, when no effective solution can be generated, switch to other strategies. By dynamically adjusting the strategy, the diversity of solutions is increased, the algorithm is prevented from falling into the local optimum too early, and the probability of finding the global optimal solution is increased. The perturbation of the gene chain V and R is consistent with the mutation operation.

[0103] Compared with the prior art, the present invention establishes a joint optimization model of workshop facility layout and material transportation resource configuration with the goal of minimizing logistics costs; solves the joint optimization model of workshop facility layout and material transportation resource configuration through a simulation-based heuristic hybrid optimization algorithm to obtain the optimization results of facility layout and material transportation resource configuration. The present invention can solve the problems of low output rate, long completion time and high logistics costs caused by unreasonable workshop facility layout and transportation resource configuration, effectively improve the logistics transportation efficiency in multi-layer workshops, and save logistics costs.

[0104] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0105] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only the preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation manner. The above-mentioned specific implementation manner is only illustrative rather than restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A multi-layer workshop facility layout and logistics configuration optimization method, characterized in that: The following steps are involved: S1. With the goal of minimizing logistics costs, a joint optimization model for workshop facility layout and material transportation resource allocation is established; S2. Solving the joint optimization model of the workshop facility layout and material transportation resource allocation by a simulation-based heuristic hybrid optimization algorithm to obtain optimization results of the facility layout and material transportation resource allocation.

2. The multi-storey workshop facility layout and logistics configuration optimization method according to claim 1, characterized in that: The joint optimization model of workshop facility layout and material transportation resource allocation satisfies the following constraints: Constrained optimization objectives; st Among them, X * It represents the process arrangement and combination that minimizes the total energy consumption cost; represents the speed of the automatic guided vehicles in each workshop when minimizing the total energy consumption cost; represents the elevator speed when the total energy consumption cost is minimized; Q represents the total transportation cost; X={x i } represents the combination vector of device layout arrangement; V A = {v k } represents the speed configuration vector of the automatic guided vehicle in each workshop; V E represents the elevator speed; r and n represent the number of materials randomly transported by the elevator and the number of materials randomly transported by the automatic guided vehicle, respectively; k and m represent the workshop floor number and the number of automatic guided vehicles, respectively; β r It indicates the load rate when the elevator load is r; is the load rate of the mth AGV in the kth floor workshop when the load is n; represents the speed of the mth AGV in the kth floor workshop; D and They represent the total running distance of the elevator and the total running distance of the mth automatic guided vehicle in the kth floor workshop respectively; G E and G A They represent the weight factor of the elevator and the weight factor of the automatic guided vehicle respectively; ω represents the energy consumption coefficient; Constrain one device to correspond to one process; Among them, S represents the number of processes, z ij represents a binary variable; Constrain the speed of the automatic guided vehicle and the elevator speed; in min ≤v≤v max ; Among them, v min and v max They represent the upper and lower limits of the automatic guided vehicle speed respectively. and Represents the upper and lower limits of the elevator speed; Constrain the average output rate and average production cycle of the workshop system to ensure that the workshop system can meet the production capacity and order delivery requirements; E(θ(X,V A ,V E :e)}≥θ min ; E{Γ(X,V A ,V E :e)}≤Γ max ; Where E{·} represents the mathematical expectation of the random function; ε represents the random element; θ min represents the lower limit of the average yield; Γ max Indicates the upper limit of the average production cycle.

3. The multi-storey workshop facility layout and logistics configuration optimization method according to claim 2, characterized in that: Step S2 includes the following sub-steps: S21, setting the maximum number of iterations, the population size, and randomly generating the initial population; Establishing a workshop simulation model, the workshop simulation model is used to obtain the average output rate and the average production cycle, performing simulation according to the workshop simulation model to obtain the fitness value of each individual, and counting the performance index corresponding to each individual, if the performance index of the individual does not meet the preset performance index constraint, then introducing a penalty factor to penalize the objective function value of the individual that does not meet the preset index; S22, select the individual with the best fitness in the population as the parent; S23, performing crossover processing and mutation processing; S24, perform simulation again to calculate the fitness value of the offspring individual, and if the offspring individual does not meet the preset performance indicator constraint, introduce a penalty factor to penalize the objective function value of the offspring individual; S25, setting the initial temperature and the minimum temperature, and performing simulated annealing local optimization processing on the offspring individuals; S26, merge the offspring individuals with the initial population, and select half of the individuals with the best fitness as the new population; S27. Determine whether the number of iterations is less than the maximum number of iterations. If so, return to step 22; if not, the optimization ends and the current result is output as the optimization result of the facility layout and material transportation resource configuration.

4. The multi-storey workshop facility layout and logistics configuration optimization method according to claim 3, characterized in that: Step S25 includes the following sub-steps: S251, locally optimize the individuals in the offspring individuals, and set the fitness value of the current individual to the optimal solution; S252, performing disturbance to generate a new solution; S253, performing simulation to calculate the objective function value of the new solution, and if the preset performance indicator constraint is not satisfied, introducing a penalty factor into the new solution; S254, judging whether the new solution is greater than the current solution, if so, accepting the new solution as the new current solution; otherwise, randomly accepting the new solution as the new current solution with a preset probability; judging whether the current solution is greater than the optimal solution, if so, updating the current solution to the new optimal solution; S255, perform cooling process. If the current temperature is greater than the minimum temperature, return to step 252; otherwise, proceed to step S256; S256, determine whether all the offspring individuals have been locally optimized, if so, proceed to step 26; if not, return to step 251.

5. The multi-storey workshop facility layout and logistics configuration optimization method according to claim 3, characterized in that: The crossover process is based on a partial mapping crossover method.

6. The multi-storey workshop facility layout and logistics configuration optimization method according to claim 1, characterized in that: The heuristic hybrid optimization algorithm is based on simulated annealing algorithm and genetic algorithm.

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