CN116755393A - Large-scale flexible job shop scheduling method, system, equipment and medium - Google Patents
Large-scale flexible job shop scheduling method, system, equipment and medium Download PDFInfo
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Abstract
The application relates to the technical field of flexible workshop scheduling, in particular to a large-scale flexible job workshop scheduling method, system, equipment and medium; firstly, comprehensively crossing and independently mutating operators in a three-layer real number coding mode of a genetic algorithm to solve a scheduling problem of a flexible job shop, and obtaining an initial scheduling scheme; then, on the basis of an initial scheduling scheme, identifying a key workpiece, a key procedure, a key machine tool and a key cutter by taking the minimum delay time as a target to obtain a new scheduling scheme; finally, on the basis of a new scheduling scheme, the key workpiece, the key working procedure, the key machine tool and the key tool are identified again by taking the minimum processing cost as a target, so that the solving efficiency of a genetic algorithm is improved, the optimal scheduling scheme of the production cost is obtained, and the high-quality and high-efficiency solving of the large-scale scheduling of the flexible job shop is realized.
Description
Technical Field
The application relates to the technical field of flexible job shop scheduling, in particular to a large-scale flexible job shop scheduling method, system, equipment and medium.
Background
The task of shop scheduling is to reasonably allocate limited resources to optimize one or more targets, so how to effectively arrange the shop manufacturing resources is a key to improving the shop operation efficiency. In the conventional job shop scheduling problem, the available machines for each process are limited, while in the flexible job shop scheduling problem, each process can be processed on any one machine in a feasible machine set, which is an extension of the conventional job shop scheduling, and the goal is to allocate one machine for each process and arrange all the processes on each machine so that the preset goal is optimal.
In order to solve the flexible job shop scheduling problem, an accurate solution and a heuristic algorithm are commonly used methods, but the accurate solution is generally applicable to the small-scale scheduling problem, and the solution time is too long, so that the heuristic algorithm is widely applied. However, in the solving process of the existing heuristic algorithms, such as genetic algorithm, simulated annealing algorithm and the like, the solution space of each iteration is kept unchanged all the time, so that when the problem of large-scale scheduling with high complexity and huge solution space is faced, the calculation efficiency is low, the solution quality is poor, and the stable, accurate and quick actual requirements are difficult to meet. The existing flexible manufacturing mode has the characteristics of multiple varieties and small batches, the flexible job shops are required to schedule manufacturing resources with various types and large quantity, in addition, the flexible job shops are required to schedule the various manufacturing resources with large quantity of parts, the working procedures of each part are large, the performance difference of equipment is large, and the optimal production scheduling scheme is required to be quickly obtained from a large solution space, so that new challenges are provided for the scheduling problem solving efficiency and quality of the flexible job shops. Therefore, it is necessary to provide an efficient and high-quality solving method for the scheduling problem of a large-scale flexible job shop.
Disclosure of Invention
Aiming at the problems that the existing scheduling method is low in calculation efficiency and poor in solving quality when facing the large-scale scheduling problem with high complexity and huge solving space, and is difficult to meet the actual requirements of stability, accuracy and rapidness, the application provides a large-scale flexible job shop scheduling method, system, equipment and medium, wherein the three-layer real number coding mode of a genetic algorithm is designed to comprehensively cross and independently mutate operators to solve the flexible job shop scheduling problem to obtain an initial scheduling scheme; then, on the basis of an initial scheduling scheme, identifying a key workpiece, a key procedure, a key machine tool and a key cutter by taking the minimum delay time as a target to obtain a new scheduling scheme; finally, on the basis of a new scheduling scheme, the key workpiece, the key working procedure, the key machine tool and the key tool are identified again by taking the minimum processing cost as a target, so that the solving efficiency of a genetic algorithm is improved, the optimal scheduling scheme of the production cost is obtained, and the high-quality and high-efficiency solving of the large-scale scheduling of the flexible job shop is realized.
The application has the following specific implementation contents:
a large-scale flexible job shop scheduling method comprises the following steps:
step S1: according to the workpiece information, the machine tool information and the cutter information, a flexible job shop scheduling model is established, and constraint conditions of the flexible job shop scheduling model are set;
step S2: solving the flexible job shop scheduling model by using a genetic algorithm to obtain an initial scheduling scheme;
step S3: according to the initial scheduling scheme, the minimum delay time is taken as a target to identify a key workpiece, and the solution is carried out according to the genetic algorithm to obtain a new scheduling scheme;
step S4: and according to the new scheduling scheme, the minimum processing cost is taken as a target to identify a key workpiece, and the optimal scheduling scheme is obtained by combining the genetic algorithm solution.
In order to better implement the present application, further, the step S1 specifically includes the following steps:
step S11: calculating the use cost of the machine tool, the use cost of the cutter and the delay delivery cost according to the workpiece information, the machine tool information and the cutter information;
step S12: calculating production cost according to the machine tool use cost, the cutter use cost and the delay delivery cost;
step S13: according to the production cost, a flexible job shop scheduling model is established;
step S14: and setting constraint conditions of the flexible job shop scheduling model.
In order to better implement the present application, further, the step S2 specifically includes the following steps:
step S21: setting a genetic algorithm coding and decoding mode of a machine tool and a cutter;
step S22: setting a crossover operator and a mutation operator of a machine tool and a crossover operator and a mutation operator of a cutter in the genetic algorithm;
step S23: and solving a flexible job shop scheduling model by using a genetic algorithm to obtain an initial scheduling scheme.
In order to better implement the present application, further, the step S21 specifically includes the following steps:
step S211: setting the logic relation of a working procedure, a machine tool and a cutter by using a three-layer real number coding method;
step S212: setting a first layer of the three-layer coding method as a process code; setting a second layer of the three-layer coding method as machine tool coding; setting a third layer of the three-layer coding method as a cutter coding;
the process code is used for determining the processing sequence of the workpiece; the machine tool code is used for determining a machine tool for current machining; the tool code is used to determine the machining tool of the current process.
Step S213: and searching an idle time period of the working procedure, and decoding by using a greedy decoding method.
In order to better implement the present application, further, the step S22 specifically includes the following steps:
step S221: crossing the current working procedures;
step S222: integrating a cross machine tool and a cutter in the cross working procedure;
step S223: randomly selecting variant gene positions from the chromosome and randomly changing the variant gene positions;
step S224: the machine tools and the tools are subjected to individual variation, the machine tools are randomly selected in the machine tool set, and the tools are randomly selected on the selected machine tools.
In order to better implement the present application, further, the step S3 specifically includes the following steps:
step S31: calculating the finishing time of the workpieces according to the initial scheduling scheme, and identifying the workpiece with the shortest delay time as a target according to the delay time;
step S32: calculating the working procedure waiting time of the target identification key workpiece, sorting the working procedure waiting time in a descending order, taking the working procedure with the first working procedure waiting time as a key working procedure, and taking a machine tool and a cutter corresponding to the key working procedure as a key machine tool and a key cutter;
step S33: removing the key machine tool and the key cutter from the current working procedure, and modifying the solving space of the flexible job shop scheduling model;
step S34: and solving the flexible job shop scheduling model according to a genetic algorithm to obtain a scheduling scheme with the shortest delay time, and taking the scheduling scheme with the shortest delay time as a new scheduling scheme.
In order to better implement the present application, further, the step S4 specifically includes the following steps:
step S41: according to the new scheduling scheme, the process waiting time of the current process is obtained, the workpiece with the minimum processing cost is taken as a target to identify a key workpiece, and the processing cost difference is calculated;
step S42: arranging the processing cost differences in a descending order, and taking the working procedure, the machine tool and the cutter corresponding to the first processing cost difference as a key working procedure, a key machine tool and a key cutter;
step S43: modifying a solving space of a scheduling problem of the flexible job shop;
step S44: and solving a flexible job shop scheduling model according to the genetic algorithm to obtain a cost optimal scheduling scheme.
Based on the above-mentioned large-scale flexible job shop scheduling method, in order to better implement the application, further, a large-scale flexible job shop scheduling system is provided, including a scheduling model building unit, an initializing unit, a processing unit, and an optimizing unit;
the scheduling model building unit is used for building a flexible job shop scheduling model according to the workpiece information, the machine tool information and the cutter information, and setting constraint conditions of the flexible job shop scheduling model;
the initialization unit is used for solving the flexible job shop scheduling model by utilizing a genetic algorithm to obtain an initial scheduling scheme;
the processing unit is used for identifying key workpieces by taking the minimum delay time as a target according to the initial scheduling scheme and solving according to the genetic algorithm to obtain a new scheduling scheme;
and the optimizing unit is used for identifying key workpieces by taking the minimum processing cost as a target according to the new scheduling scheme and solving by combining the genetic algorithm to obtain an optimal scheduling scheme.
Based on the above-mentioned large-scale flexible job shop scheduling method, in order to better implement the present application, further, an electronic device is proposed, including a memory and a processor; the memory is used for storing a computer program;
the computer program, when executed on the processor, is adapted to implement the large-scale flexible job shop scheduling method described above.
Based on the above-mentioned large-scale flexible job shop scheduling method, in order to better implement the present application, further, a computer readable storage medium is provided, on which computer instructions are stored; the computer instructions, when executed on the electronic device described above, are operable to implement the large-scale flexible job shop scheduling method described above.
The application has the following beneficial effects:
according to the application, the flexible job shop scheduling model and constraint conditions considering the machine tool and the cutter are established, the three-stage heuristic algorithm is adopted to solve the large-scale scheduling problem, the cost optimal scheduling scheme is obtained in a short time, and the operation efficiency of the flexible job shop is improved.
Drawings
Fig. 1 is a schematic flow chart of a dispatching method for a large-scale flexible job shop according to an embodiment of the present application.
Fig. 2 is a schematic diagram of a three-layer coding scheme according to an embodiment of the present application.
Fig. 3 is a schematic cross view of a machine tool and a cutter according to an embodiment of the present application.
FIG. 4 is a schematic diagram of process waiting time according to an embodiment of the present application.
Fig. 5 is a schematic diagram of a processing cost difference according to an embodiment of the present application.
Fig. 6 is a schematic diagram of an optimization result of the second stage algorithm according to an embodiment of the present application.
Fig. 7 is a schematic diagram of an optimization result of a third stage algorithm according to an embodiment of the present application.
Fig. 8 is a schematic diagram of a production cost optimization result of a three-stage algorithm according to an embodiment of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it should be understood that the described embodiments are only some embodiments of the present application, but not all embodiments, and therefore should not be considered as limiting the scope of protection. All other embodiments, which are obtained by a worker of ordinary skill in the art without creative efforts, are within the protection scope of the present application based on the embodiments of the present application.
In the description of the present application, it should be noted that, unless explicitly stated and limited otherwise, the terms "disposed," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; or may be directly connected, or may be indirectly connected through an intermediate medium, or may be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Example 1:
the embodiment provides a large-scale flexible job shop scheduling method, which comprises the following steps:
step S1: and establishing a flexible job shop scheduling model according to the workpiece information, the machine tool information and the cutter information, and setting constraint conditions of the flexible job shop scheduling model.
Further, the step S1 specifically includes the following steps:
step S11: calculating the use cost of the machine tool, the use cost of the cutter and the delay delivery cost according to the workpiece information, the machine tool information and the cutter information;
step S12: calculating production cost according to the machine tool use cost, the cutter use cost and the delay delivery cost;
step S13: according to the production cost, a flexible job shop scheduling model is established;
step S14: and setting constraint conditions of the flexible job shop scheduling model.
Step S2: and solving the flexible job shop scheduling model by using a genetic algorithm to obtain an initial scheduling scheme.
Further, the step S2 specifically includes the following steps:
step S21: setting a genetic algorithm coding and decoding mode of a machine tool and a cutter.
Further, the step S21 specifically includes the following steps:
step S211: setting the logic relation of a working procedure, a machine tool and a cutter by using a three-layer real number coding method;
step S212: setting a first layer of the three-layer coding method as a process code; setting a second layer of the three-layer coding method as machine tool coding; setting a third layer of the three-layer coding method as a cutter coding;
the process code is used for determining the processing sequence of the workpiece; the machine tool code is used for determining a machine tool for current machining; the tool code is used for determining a processing tool of the current procedure;
step S213: and searching an idle time period of the working procedure, and decoding by using a greedy decoding method.
Step S22: setting a crossover operator and a mutation operator of a machine tool and a crossover operator and a mutation operator of a cutter in the genetic algorithm.
Further, the step S22 specifically includes the following steps:
step S221: crossing the current working procedures;
step S222: integrating a cross machine tool and a cutter on the basis of the crossed working procedures;
step S223: randomly selecting a variant gene position from the chromosome and randomly altering the variant gene position;
step S224: the machine tools and the tools are subjected to individual variation, the machine tools are randomly selected in the machine tool set, and the tools are randomly selected on the selected machine tools.
Step S23: and solving a flexible job shop scheduling model by using a genetic algorithm to obtain an initial scheduling scheme.
Step S3: and according to the initial scheduling scheme, taking the minimum delay time as a target to identify a key workpiece, and solving according to the genetic algorithm to obtain a new scheduling scheme.
Further, the step S3 specifically includes the following steps:
step S31: calculating the finishing time of the workpieces according to the initial scheduling scheme, and identifying the workpiece with the shortest delay time as a target according to the delay time;
step S32: calculating the working procedure waiting time of the target identification key workpiece, sorting the working procedure waiting time in a descending order, taking the working procedure with the first working procedure waiting time as a key working procedure, and taking a machine tool and a cutter corresponding to the key working procedure as a key machine tool and a key cutter;
step S33: removing the key machine tool and the key cutter from the current working procedure, and modifying the solving space of the flexible job shop scheduling model;
step S34: and solving the flexible job shop scheduling model according to a genetic algorithm to obtain a scheduling scheme with the shortest delay time, and taking the scheduling scheme with the shortest delay time as a new scheduling scheme.
Step S4: and according to the new scheduling scheme, the minimum processing cost is taken as a target to identify a key workpiece, and the optimal scheduling scheme is obtained by combining the genetic algorithm solution.
Further, the step S4 specifically includes the following steps:
step S41: according to the new scheduling scheme, the process waiting time of the current process is obtained, the workpiece with the minimum processing cost is taken as a target to identify a key workpiece, and the processing cost difference is calculated;
step S42: arranging the processing cost differences in a descending order, and taking the working procedure, the machine tool and the cutter corresponding to the first processing cost difference as a key working procedure, a key machine tool and a key cutter;
step S43: modifying a solving space of a scheduling problem of the flexible job shop;
step S44: and solving a flexible job shop scheduling model according to the genetic algorithm to obtain a cost optimal scheduling scheme.
Working principle: firstly, designing three-layer real number coding modes of a genetic algorithm to comprehensively cross and independently mutate operators to solve a scheduling problem of a flexible job shop, and obtaining an initial scheduling scheme; then, on the basis of an initial scheduling scheme, identifying a key workpiece, a key procedure, a key machine tool and a key cutter by taking the minimum delay time as a target to obtain a new scheduling scheme; finally, on the basis of a new scheduling scheme, the key workpiece, the key working procedure, the key machine tool and the key tool are identified again by taking the minimum processing cost as a target, so that the solving efficiency of a genetic algorithm is improved, the optimal scheduling scheme of the production cost is obtained, and the high-quality and high-efficiency solving of the large-scale scheduling of the flexible job shop is realized.
Example 2:
this embodiment is described in a specific embodiment based on embodiment 1 described above, as shown in fig. 1,2, 3, 4, 5, 6, 7, and 8.
A large-scale flexible job shop scheduling method specifically comprises the following steps.
And S1, establishing a flexible job shop scheduling model considering a machine tool and a cutter according to the operation of the flexible job shop and the characteristics of part machining.
And S11, establishing a flexible job shop scheduling model considering the production cost of the machine tool and the cutter.
According to the flexible job shop part processing characteristics, the flexible job shop scheduling problem considering the machine tool and the cutter can be described as: there are n workpieces j= [ J ] 1 ,J 2 ,J 3 ,…,J n ]M machine tools m= [ M ] 1 ,M 2 ,M 3 ,…,M m ]V handles tool t= [ T 1 ,T 2 ,T 3 ,…,T v ]All working procedures of each workpiece are subject to technological constraints, machining must be performed according to a certain sequence, each working procedure must be performed by selecting one machine tool from a feasible machine tool set, and selecting one tool from a feasible tool set of the machine tool to perform cutting operation, and the machining time of each working procedure is determined by the machine tool and the tool together. Thus, the FJSPT problem contains three sub-problems: (1) sequencing the working procedures; (2) machining the work procedure by a distribution machine tool; (3) distributing the tool for completing the process on the machine tool. The flexible job shop scheduling problem aims at completing work piece processing tasks by distributing machine tools according to constraint conditions such as resource competition, tool stock quantity and the like through reasonably arranging work piece processing sequences, and selecting tools on the machine tools to process work pieces so as to minimize production cost.
Wherein the workpiece J i The finishing time of (2) is as follows:
work J i Is (are) delayed time DT i The method comprises the following steps:
DT i ＝max(0, C i -DD i ) (3)
wherein:
n: the total number of workpieces;
m: total number of machine tools;
n i : total number of processes for the ith workpiece;
m k : machine tool M k Is a total number of cutters;
i, i': workpiece index, i and i' = (1, 2, …, n);
j, j': process index, j and j' = (1, 2, …, n) i )；
k, k': machine index, k and k' = (1, 2, …, m);
v, v': tool index, v and v' = (1, 2, …, m k )；
O ij : a j-th step of the i-th workpiece;
C ij : procedure O ij Is a time of completion of (a);
C i : finishing time of the ith workpiece;
M ij : a set of possible machine tools for the j-th step of the i-th workpiece;
PT ijkv : procedure O ij In machine tools M k And tool T v Processing time;
PT kv : machine tool M k Upper tool T v Is not limited, and the processing time of the device is not limited;
CD: delay cost per unit time;
CM k : machine tool M k Cost per unit time of (a);
CT v : tool T v Cost per unit time of (a);
DD i : delivery time of the ith workpiece;
x kv : if x kv =1, indicating use of machine tool M k And tool T v Processing; if x kv =0, indicating that the machine tool M is not used k And tool T v Processing;
x ijk : if x ijk =1, indicating that the j-th process of the i-th workpiece uses machine tool M k Processing; if x ijk =0, indicating that the jth process of the ith workpiece does not use machine tool M k Processing;
x ijkv : if x ijkv =1, indicating that the j-th process of the i-th workpiece uses machine tool M k And tool T v Processing; if x ijkv =0, indicating that the jth process of the ith workpiece does not use machine tool M k And tool T v And (5) processing.
And step S12, establishing a scheduling model constraint condition considering the machine tool and the cutter.
According to the use requirements of the machine tool and the cutter, the constraint conditions of the scheduling model of the flexible job shop are as follows:
among the constraints, the formula (4) is a process priority constraint, the formula (5) represents that each machine tool processes only one process at a time, the formula (6) represents that each tool on each machine tool can process only one process at a time, the formula (7) represents that each process can be processed only by one machine tool, the formula (8) represents that each process can be processed only by one tool, and the formulas (9) and (10) are non-negative constraints.
And S2, solving by using a genetic algorithm according to the scheduling model established in the step S1 to obtain an initial scheduling scheme.
In step S21, the coding and decoding methods of the machine tool and the tool are considered in designing the genetic algorithm.
The three-layer real number coding method is used for representing the logical relation among working procedures, machine tools and cutters, wherein the first layer is the working procedure coding for determining the processing sequence of each workpiece, the second layer is the machine tool coding for determining which machine tool is used for processing, and the third layer is the cutter coding for determining which cutter is used for processing in each working procedure.
After the encoding is completed, the decoding is performed by a greedy decoding method, and the idle time period of each procedure is searched and processed as early as possible without delaying other procedures, namely, the space time is reduced.
And S22, designing a genetic algorithm by considering intersection and mutation operators of the machine tool and the cutter.
Crossover operator refers to the generation of new offspring individuals by exchanging the genes of the two parent chromosomes. Because the machine tool and the cutter are matched one by one, when the genetic algorithm is crossed, the working procedures are crossed firstly, then the machine tool and the cutter are comprehensively crossed on the basis of the crossed working procedures, and independent crossing is not carried out, so that the phenomenon of mismatching of the machine tool and the cutter is avoided.
The mutation operator is used for randomly selecting part of gene positions from the chromosome on the premise of ensuring the feasibility of the chromosome, and randomly changing the part of gene positions so as to avoid trapping local optimum and increasing diversity of population and search for a larger solution space. By randomly selecting the variant gene positions, individual variants of the machine tools and the tools are performed, any one of the possible machine tools is selected in the set of machine tools in this process, and then any one of the tools is selected on the selected machine tool.
And S23, solving a flexible job shop scheduling model by using a genetic algorithm to obtain an initial scheduling scheme.
The flexible job shop scheduling problem is solved by utilizing a genetic algorithm, wherein in the genetic algorithm, the population number is set to 40, the maximum iteration number is set to 6000, the mutation probability is set to 0.1, the crossover probability is set to 0.9, the coding mode is a three-layer real number coding mode, the crossover operator is a comprehensive crossover method, and the mutation operator is a single mutation method.
And S3, identifying key workpieces by taking minimum delay time as a target according to the initial scheduling scheme obtained in the step S2, reducing a genetic algorithm solving space, and solving by using the genetic algorithm in the step S2 to obtain a better scheduling scheme, namely a new scheduling scheme.
And S31, identifying key workpieces by taking the shortest delay time as a target according to the initial scheduling scheme obtained in the step S23.
(1) Calculating the finishing time of each workpiece according to the initial scheduling scheme obtained by using the genetic algorithm in the step S23;
(2) Sequencing all the workpieces in descending order according to the delay time;
(3) And taking the workpiece with the first delay time rank as a key workpiece.
Step S32, identifying a key process, a key machine tool and a key tool.
(1) The process waiting time OpWT of each process of the key workpiece is calculated. The process waiting time is the time interval between the previous process end time and the next process start time of the workpiece, and is the main cause of the workpiece delay. The greater the process waiting time, the later the earliest start time of the subsequent process, which ultimately results in the later the work completion time, and even the more the work delivery time, resulting in increased production costs.
Wherein: SPT (SPT) ij : procedure O ij Start time of EPT of (2) ij Procedure O ij End time of (c) op wt ij : wait time interval of the j-th process of the i-th workpiece.
(2) Ordering opwt= { OpWT with sequence latency in descending order of all elements 1 ,OpWT 2 ,…,OpWT n }；
(3) The first process of the process waiting time rank is used as a key process OpWT 1 The machine tools and tools used in this process are used as key machine tools and key tools.
And step S33, modifying a solving space of the flexible job shop scheduling model.
Other available tools and tools in the critical process are searched for to replace the critical tools and tools, and the critical tools are removed in the set of tools in the process.
And step S34, solving a scheduling model by using a genetic algorithm to obtain a preferred scheduling scheme with the shortest delay time.
And (3) solving a flexible job shop scheduling model in the modified solution space by using a genetic algorithm, wherein the parameter setting of the genetic algorithm is the same as that of the step S23, and a better scheduling scheme is obtained. If the delay time of the preferred scheduling scheme is longer than the initial scheduling scheme, the solution space is reset and the process returns to step S32.
And S4, identifying key workpieces by taking the minimum processing cost as a target according to the optimal scheduling scheme obtained in the step S3, reducing the solving space of the genetic algorithm, and solving by using the genetic algorithm in the step S2 to obtain the optimal scheduling scheme.
And S41, obtaining a scheduling scheme with the shortest delay time according to the step S34, and identifying the key workpiece by taking the minimum processing cost as a target.
The scheduling scheme obtained in step S34 is input, and the process waiting time and the processing cost difference of all the processes are calculated.
(1) Calculating the process waiting time OpWT of all the processes;
(2) The processing cost difference Δopmc for all the processes is calculated.
For a certain process there are many possible combinations of machine tools and tools, the machining costs of different machine tools and tools being different. For procedure O ij The difference ΔOpMC between the machining cost corresponding to each machine tool and tool combination and the minimum machining cost for the process can be defined as
ΔOpMC ij ＝OpMC ijkv -min(O p MC ijk'v' ) (12)
Wherein: min (OpMC) ijk’v’ ) Indicating completion of process O ij Machine tool k 'and tool v' combination with minimum machining cost, opMC ijkv Indicated as completed process O ij The machining costs of the currently selected machine tool k and tool v. ΔOpMC ij The larger the description of the replacement of the machine and tool may result in less machining costs.
Step S42, identifying a key process, a key machine tool, and a key tool.
(1) Descending order ranking Δopmc= { Δopmc on the processing cost differences of all the processes 1 ,ΔOpMC 2 ,…,ΔOpMC n }；
(2) Ranking the machining cost difference to a first ΔOpMC 1 The processes, machine tools and tools that are present are considered critical processes, critical machine tools and critical tools. If the process waiting time corresponding to the selected process is zero, the process, machine tool and tool with the second rank of processing cost difference are regarded as the key process, key machine tool and key tool, and so on.
And step S43, modifying a solving space of the flexible job shop scheduling model.
(1) Searching a working procedure corresponding to the non-zero working procedure waiting time of a key workpiece, and searching all machine tool and cutter combinations which reduce the processing cost and meet the requirements of the starting time and the finishing time of the working procedure (formula (13) and formula (14)) in the working procedure;
(2) The machine tool and the tool with the greatest reduction in machining cost are selected to replace the key machine tool and the key tool of step S42, and the key machine tool is removed in the set of machine tools in the process.
And S44, solving the scheduling model by using a genetic algorithm to obtain the optimal scheduling scheme.
And (3) solving a flexible job shop scheduling model in the modified solution space by using a genetic algorithm, wherein the parameter setting of the genetic algorithm is the same as that of the step S23, and an optimal scheduling scheme is obtained. If the processing cost of the preferred scheduling scheme is greater than the initial scheduling scheme, the solution space is reset and step S42 is returned.
In order to verify the effectiveness of the large-scale scheduling solving method of the flexible job shop of the embodiment, taking the scheduling problem of the flexible job shop with 20 workpieces, 10 working procedures of each workpiece, 10 machine tools for each workpiece, and 10 cutters for each machine tool as an example, the cost is optimal as a scheduling target. And the result obtained by solving the genetic algorithm is used for solving the scheduling problem with the solving algorithm of the embodiment, genetic operators of the two algorithms are consistent, namely, the population number is set to 40, the mutation probability is set to 0.1, the crossover probability is set to 0.9, three layers of real numbers are selected as the coding mode, a comprehensive crossover method is selected as the crossover operator, and an independent mutation method is selected as the mutation operator. For the maximum number of iterations, the genetic algorithm was set to 6000 and the three-stage algorithm was set to 500.
The comparison result of the two algorithms shows that the optimization result of the genetic algorithm is as follows: the production cost is 9634229, and the calculation time is 5282 seconds. The optimization result of the three-stage algorithm is as follows: the production cost is 9652568, and the calculation time is 1694 seconds. The three-stage algorithm has small production cost difference from the genetic algorithm, but the calculation time is reduced by 67.83%, and the calculation efficiency is remarkably improved, so that the three-stage algorithm can obtain a high-quality solution in a short time.
Other portions of this embodiment are the same as those of embodiment 1 described above, and thus will not be described again.
Example 3:
the embodiment provides a large-scale flexible job shop scheduling system based on any one of the embodiments 1 to 2, which comprises a scheduling model building unit, an initializing unit, a processing unit and an optimizing unit;
the scheduling model building unit is used for building a flexible job shop scheduling model according to the workpiece information, the machine tool information and the cutter information, and setting constraint conditions of the flexible job shop scheduling model;
the initialization unit is used for solving the flexible job shop scheduling model by utilizing a genetic algorithm to obtain an initial scheduling scheme;
the processing unit is used for identifying key workpieces by taking the minimum delay time as a target according to the initial scheduling scheme and solving according to the genetic algorithm to obtain a new scheduling scheme;
and the optimizing unit is used for identifying key workpieces by taking the minimum processing cost as a target according to the new scheduling scheme and solving by combining the genetic algorithm to obtain an optimal scheduling scheme.
The embodiment also provides electronic equipment, which comprises a memory and a processor; the memory is used for storing a computer program;
the computer program, when executed on the processor, is adapted to implement the large-scale flexible job shop scheduling method described above.
The present embodiment also proposes a computer-readable storage medium having stored thereon computer instructions; the computer instructions, when executed on the electronic device described above, are operable to implement the large-scale flexible job shop scheduling method described above.
Other portions of this embodiment are the same as any of embodiments 1 to 2, and thus will not be described again.
The foregoing description is only a preferred embodiment of the present application, and is not intended to limit the present application in any way, and any simple modification, equivalent variation, etc. of the above embodiment according to the technical matter of the present application fall within the scope of the present application.
Claims (10)
1. A method for scheduling a large-scale flexible job shop, comprising the steps of:
step S1: according to the workpiece information, the machine tool information and the cutter information, a flexible job shop scheduling model is established, and constraint conditions of the flexible job shop scheduling model are set;
step S2: solving the flexible job shop scheduling model by using a genetic algorithm to obtain an initial scheduling scheme;
step S3: according to the initial scheduling scheme, the minimum delay time is taken as a target to identify a key workpiece, and the solution is carried out according to the genetic algorithm to obtain a new scheduling scheme;
step S4: and according to the new scheduling scheme, the minimum processing cost is taken as a target to identify a key workpiece, and the optimal scheduling scheme is obtained by combining the genetic algorithm solution.
2. The method for scheduling a large-scale flexible job shop according to claim 1, wherein the step S1 specifically comprises the steps of:
step S11: calculating the use cost of the machine tool, the use cost of the cutter and the delay delivery cost according to the workpiece information, the machine tool information and the cutter information;
step S12: calculating production cost according to the machine tool use cost, the cutter use cost and the delay delivery cost;
step S13: according to the production cost, a flexible job shop scheduling model is established;
step S14: and setting constraint conditions of the flexible job shop scheduling model.
3. The method for scheduling a large-scale flexible job shop according to claim 2, wherein the step S2 specifically comprises the steps of:
step S21: setting a genetic algorithm coding and decoding mode of a machine tool and a cutter;
step S22: setting a crossover operator and a mutation operator of a machine tool and a crossover operator and a mutation operator of a cutter in the genetic algorithm;
step S23: and solving a flexible job shop scheduling model by using a genetic algorithm to obtain an initial scheduling scheme.
4. A method of scheduling a large-scale flexible job shop according to claim 3, wherein the step S21 specifically comprises the steps of:
step S211: setting the logic relation of a working procedure, a machine tool and a cutter by using a three-layer real number coding method;
step S212: setting a first layer of the three-layer coding method as a process code; setting a second layer of the three-layer coding method as machine tool coding; setting a third layer of the three-layer coding method as a cutter coding;
the process code is used for determining the processing sequence of the workpiece; the machine tool code is used for determining a machine tool for current machining; the tool code is used for determining a processing tool of the current procedure;
step S213: and searching an idle time period of the current procedure, and decoding by using a greedy decoding method.
5. The method for scheduling a large-scale flexible job shop according to claim 4, wherein the step S22 specifically comprises the steps of:
step S221: crossing the current working procedures;
step S222: integrating a cross machine tool and a cutter on the basis of the crossed working procedures;
step S223: randomly selecting a variant gene position from chromosomes of a genetic algorithm, and randomly changing the variant gene position;
step S224: the machine tools and the tools are individually varied, the machine tools are randomly selected in the machine tool set, and the tools are randomly selected on the selected machine tools.
6. A method of scheduling a large-scale flexible job shop according to claim 3, wherein the step S3 specifically comprises the steps of:
step S31: according to the initial scheduling scheme, calculating the finishing time of the workpiece, and identifying the workpiece with the shortest delay time as a target to identify a key workpiece;
step S32: calculating the working procedure waiting time of the target identification key workpiece, sorting the working procedure waiting time in a descending order, taking the working procedure with the first working procedure waiting time as a key working procedure, and taking a machine tool and a cutter corresponding to the key working procedure as a key machine tool and a key cutter;
step S33: removing the key machine tool and the key cutter from the current working procedure, and modifying the solving space of the flexible job shop scheduling model;
step S34: and solving the flexible job shop scheduling model according to a genetic algorithm to obtain a scheduling scheme with the shortest delay time, and taking the scheduling scheme with the shortest delay time as a new scheduling scheme.
7. The method for scheduling a large-scale flexible job shop according to claim 6, wherein the step S4 specifically comprises the steps of:
step S41: according to the new scheduling scheme, the process waiting time of the current process is obtained, the workpiece with the minimum processing cost is taken as a target to identify a key workpiece, and the processing cost difference is calculated;
step S42: arranging the processing cost differences in a descending order, and taking the working procedure, the machine tool and the cutter corresponding to the first processing cost difference as a key working procedure, a key machine tool and a key cutter;
step S43: modifying a solving space of a scheduling problem of the flexible job shop;
step S44: and solving a flexible job shop scheduling model according to the genetic algorithm to obtain a cost optimal scheduling scheme.
8. The large-scale flexible job shop scheduling system is characterized by comprising a scheduling model building unit, an initializing unit, a processing unit and an optimizing unit;
the scheduling model building unit is used for building a flexible job shop scheduling model according to the workpiece information, the machine tool information and the cutter information, and setting constraint conditions of the flexible job shop scheduling model;
the initialization unit is used for solving the flexible job shop scheduling model by utilizing a genetic algorithm to obtain an initial scheduling scheme;
the processing unit is used for identifying key workpieces by taking the minimum delay time as a target according to the initial scheduling scheme and solving according to the genetic algorithm to obtain a new scheduling scheme;
and the optimizing unit is used for identifying key workpieces by taking the minimum processing cost as a target according to the new scheduling scheme and solving by combining the genetic algorithm to obtain an optimal scheduling scheme.
9. An electronic device comprising a memory and a processor; the memory is used for storing a computer program;
when executed on the processor, for implementing a large scale flexible job shop scheduling method according to any one of claims 1-7.
10. A computer-readable storage medium having stored thereon computer instructions;
when executed on an electronic device as claimed in claim 9, for implementing a large-scale flexible job shop scheduling method as claimed in any one of claims 1-7.
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