CN111325487A - Intelligent scheduling optimization method and system for flow production workshop - Google Patents

Intelligent scheduling optimization method and system for flow production workshop Download PDF

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CN111325487A
CN111325487A CN202010352455.5A CN202010352455A CN111325487A CN 111325487 A CN111325487 A CN 111325487A CN 202010352455 A CN202010352455 A CN 202010352455A CN 111325487 A CN111325487 A CN 111325487A
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辜丽川
江余
李健
王梦瑶
赵子豪
李菲
王超
焦俊
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Abstract

The invention discloses an intelligent scheduling optimization method and system for a flow production workshop, which comprises the following steps: establishing a multi-target workshop scheduling model according to the processing mode and characteristics of the flow shop, wherein the multi-target workshop scheduling model takes the minimized maximum completion time, the minimized maximum flow-through time and the minimized delivery delay time as optimization targets to establish the multi-target scheduling optimization model of the flow shop; a multi-target grasshopper algorithm improved by curve self-adaptation is combined to solve a multi-target flowing water workshop model to obtain a workshop scheduling optimization scheme; and scheduling the workpieces and the processing equipment of each factory in the flow shop by using the obtained scheduling optimization scheme. The problem of workshop production efficiency low has been solved through this scheme, the effectual production efficiency and the production raw materials utilization ratio that have improved has reached the effect of flow shop intelligence scheduling in a certain sense.

Description

Intelligent scheduling optimization method and system for flow production workshop
Technical Field
The invention relates to the related technical field of workshop production scheduling control, in particular to an intelligent scheduling optimization method and system for a flow production workshop.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Workshop scheduling is one of the important problems to be solved in the enterprise production management system. The research and development of the workshop Scheduling Problem has been known for more than 60 years, and is one of the most difficult combinatorial optimization problems, and the free flowing workshop Scheduling Problem (FSP) is proposed, which has attracted the attention of a plurality of scholars. In the classic flow shop scheduling problem, all workpieces are produced on one flow line, there are multiple processing stages on one flow line, one machine on each stage, and each workpiece needs to pass through the flow line in sequence. The flow shop is widely applied to actual production, such as food, textile, paper making, steel and other industries. If the optimal scheme can be solved in the water flow workshop scheduling problem, the quality of workpieces can be greatly improved, the production cost is reduced, and the production yield of a workshop is improved.
In the work of economic welfare theory, Italian economists propose a multi-objective optimization problem, introduce an optimization concept and play an important role in the formation of a multi-objective optimization subject. In 1951, the american economics analysis of production and distribution activities considered multi-objective problems, and the concept of optimal solutions to the multi-objective optimization problem was proposed and named "Pareto solutions". Nowadays, the multi-objective optimization problem is more and more widely applied, and relates to various fields. Generally, a scheduling problem refers to a problem in which workpieces are reasonably arranged on a machine to optimize a certain performance index or performance indexes on the premise that constraints are met. Production scheduling can be divided into single-target scheduling and multi-target scheduling according to the number of scheduling targets. Multi-objective production scheduling is gaining more and more attention as a multi-objective decision problem in more and more complex manufacturing environments.
Disclosure of Invention
The invention aims to provide an intelligent scheduling optimization method and system for a flow production workshop of a production workshop, which can help the flow production workshop to provide a scheduling scheme so as to achieve intelligent scheduling.
The invention solves the technical problems through the following technical scheme:
an intelligent scheduling optimization method for a production workshop of a production workshop flowing water comprises the following steps:
s1, obtaining basic information of workshop equipment, procedures and workpieces and a production and processing task instruction sent by a first terminal issuing a production task, wherein the production and processing instruction comprises the processed workpieces, the number of the processed workpieces and the required completion time;
s2, determining a target function and specific constraint conditions of workshop scheduling, and establishing a scheduling model;
s3, determining the algorithm: and (3) a multi-objective grasshopper optimization algorithm (MOGOA) is used for improving parameters in the multi-objective grasshopper algorithm by curve self-adaption.
S4, solving the scheduling model by adopting a curve self-adaptive multi-objective grasshopper optimization algorithm to obtain a pareto optimal solution, and outputting a production scheduling scheme.
As an optimized technical solution, step S2 is specifically as follows:
the method comprises the following steps that n workpieces with the same processing route in the processes are processed in m serial devices, each workpiece comprises m processes, each process needs different machines for processing, namely, the workpieces need to sequentially pass through a device 1, a device 2 and a device m, each workpiece is processed on each machine once, the processing time and the preparation time of each workpiece on each machine are known, and once the processing is started, the workpiece cannot be stopped until the processing is finished; meanwhile, each workpiece can be processed by only one machine at the same time, and each machine can intelligently process one workpiece at the same time.
The parameters and symbols are represented in table 1 as follows:
Figure BDA0002472332870000031
Xijkfor indicating the variable, if the workpiece JiWorkpiece JjAt device MkUpper working, then XijkIs 1, otherwise is 0;
Figure BDA0002472332870000032
Yijhk: if process O is an indicator variableijIs just at OhkFront working rule YijhkIs 1, otherwise is 0;
Figure BDA0002472332870000041
the objective function is:
f1＝maxCi,j＝max{Ci,j-1,Ci-1,j}+ti,ji＝1,2,...,n；j＝2,...,m (1)
Figure BDA0002472332870000042
f3＝max{0,Ci,m-Ri}i＝1,...,n (3)
the constraints can be expressed as:
Cik≥0 i＝1,2,...,n；k＝1,2,...,m (4)
Sik+Pik-Sjk≤M(1-Xijk) i,j＝1,2,...,m,k＝1,2,...,m (5)
Sjk+Pjk-Sik≤M*Xijki,j＝1,2,...,n,k＝1,2,...,m (6)
Figure BDA0002472332870000043
Figure BDA0002472332870000044
in the formula, an objective function (1) represents the maximum completion time, an objective function (2) represents the total flow time, and an objective function (3) represents the delay time; constraint (4) represents that the completion time must be greater than zero, constraint (5) and constraint (6) enable one machine to process only one workpiece in one pass, and constraint (7) and constraint (8) define a variable of 0-1.
As an optimized technical solution, step S3 specifically includes the following steps:
adaptation using curves
Figure BDA0002472332870000045
Mathematical model replacing updating grasshopper positions
Figure BDA0002472332870000051
The parameter c in (1);
as an optimized technical solution, step S4 specifically includes the following steps:
s01, initializing workshop machines and workpiece information;
s02, initializing the initial position of the small-sprout population, the size of the population and various parameters and maximum iteration times involved in the algorithm;
s03, calculating the self-adaptability of each grasshopper search agent, and finding out the current global optimal solution;
s04, obtaining the optimal particles;
s05, updating the parameter c by using a curve adaptive function;
s06, standardizing the distance between every two grasshoppers;
s07, passing
Figure BDA0002472332870000052
Updating the position of the current particle;
s08, trimming an external population, and selecting a global optimal position for each particle;
s09, calculating and selecting the historical optimal position of each case of each particle and comparing the historical optimal position with the population optimal position;
s10, archiving the optimal position particles of each particle, comparing the particles with the optimal position particles, and updating the optimal position particles;
s11, according to the comparison between the new non-dominant solution and the archived non-dominant solution, the external storage archive is updated, if the archive is full, one of the members before the archive is deleted.
And S12, stopping if the maximum iteration number is reached.
The intelligent scheduling optimization system of the flow production workshop, which is applied to the method, comprises the following steps:
a production task receiving module: the system comprises a first terminal, a second terminal, a third terminal and a fourth terminal, wherein the first terminal is used for acquiring basic information of workshop equipment, working procedures and workpieces and a production and processing task instruction sent by the first terminal for issuing a production task, and the production and processing instruction comprises the processed workpieces, the number of the processed workpieces and required completion time;
the multi-target scheduling model establishing module: the system comprises a scheduling model, a target function and specific constraint conditions for determining workshop scheduling, and a scheduling model;
an optimized output module: and solving the scheduling model by using an improved multi-objective grasshopper optimization algorithm to obtain an optimal solution of the scheduling model, and outputting a production scheduling scheme.
Compared with the prior art, the invention has the following advantages: the flow shop is specifically analyzed, the whole workshop production process is processed numerically according to the characteristics of the flow shop, a multi-target flow shop model based on the maximum completion time, the total flow time and the delay delivery time is established, and related constraint conditions are established; meanwhile, a multi-target grasshopper algorithm in artificial intelligence is utilized, the defects that the convergence precision is insufficient and the algorithm is easy to fall into local optimum are analyzed, a curve self-adaption improvement algorithm is utilized, a multi-target flow shop model established before is solved by the curve self-adaption-based multi-target grasshopper algorithm, and a shop scheduling scheme is obtained; after a user logs in the system, multi-objective workshop scheduling optimization can be carried out by improving parameters and inputting corresponding workshop information, and intelligent scheduling of flow workshops can be effectively achieved.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the invention and not to limit the invention.
FIG. 1 is a flow diagram of a method of generating a schedule in accordance with one or more embodiments.
FIG. 2 is a flow chart of a multi-target grasshopper algorithm based on curve adaptation.
Fig. 3 is a schematic diagram of the overall system structure.
Fig. 4 is a system overview flow diagram.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
Example 1
Referring to the drawings, as shown in fig. 1, the method for optimizing the intelligent scheduling of the flow production shop provided by the present invention specifically includes the following steps:
s1, obtaining basic information of workshop equipment, procedures and workpieces and a production and processing task instruction sent by a first terminal issuing a production task, wherein the production and processing instruction comprises the processed workpieces, the number of the processed workpieces and the required completion time;
s2, determining a target function and specific constraint conditions of workshop scheduling, and establishing a scheduling model;
s3, determining the algorithm: a multi-objective grasshopper optimization algorithm (MOGOA) improves parameters in the multi-objective grasshopper algorithm by utilizing curve self-adaptation, and a flow chart of the improved multi-objective grasshopper algorithm based on the curve self-adaptation is shown in FIG. 2;
s4, solving the scheduling model by adopting a curve self-adaptive multi-objective grasshopper optimization algorithm to obtain a pareto optimal solution, and outputting a production scheduling scheme.
As an optimized technical solution, step S2 is specifically as follows:
the method comprises the following steps that n workpieces with the same processing route in the processes are processed in m serial devices, each workpiece comprises m processes, each process needs different machines for processing, namely, the workpieces need to sequentially pass through a device 1, a device 2 and a device m, each workpiece is processed on each machine once, the processing time and the preparation time of each workpiece on each machine are known, and once the processing is started, the workpiece cannot be stopped until the processing is finished; meanwhile, each workpiece can be processed by only one machine at the same time, and each machine can intelligently process one workpiece at the same time.
The parameters and symbols are represented in table 1 as follows:
Figure BDA0002472332870000081
Xijkfor indicating the variable, if the workpiece JiWorkpiece JjAt device MkUpper working, then XijkIs 1, otherwise is 0;
Figure BDA0002472332870000082
Yijhk: if process O is an indicator variableijIs just at OhkFront working rule YijhkIs 1, otherwise is 0;
Figure BDA0002472332870000091
the objective function is:
f1＝maxCi,j＝max{Ci,j-1,Ci-1,j}+ti,ji＝1,2,...,n；j＝2,...,m (1)
Figure BDA0002472332870000092
f3＝max{0,Ci,m-Ri}i＝1,...,n (3)
the constraints can be expressed as:
Cik≥0 i＝1,2,...,n；k＝1,2,...,m (4)
Sik+Pik-Sjk≤M(1-Xijk)i,j＝1,2,...,m,k＝1,2,...,m (5)
Sjk+Pjk-Sik≤M*Xijki,j＝1,2,...,n,k＝1,2,...,m (6)
Figure BDA0002472332870000093
Figure BDA0002472332870000094
in the formula, an objective function (1) represents the maximum completion time, an objective function (2) represents the total flow time, and an objective function (3) represents the delay time; constraint (4) represents that the completion time must be greater than zero, constraint (5) and constraint (6) enable one machine to process only one workpiece in one pass, and constraint (7) and constraint (8) define a variable of 0-1.
As an optimized technical solution, step S3 specifically includes the following steps:
adaptation using curves
Figure BDA0002472332870000095
Mathematical model replacing updating grasshopper positions
Figure BDA0002472332870000096
The parameter c in (1);
as an optimized technical solution, step S4 specifically includes the following steps:
s01, initializing workshop machines and workpiece information;
s02, initializing the initial position of the small-sprout population, the size of the population and various parameters and maximum iteration times involved in the algorithm;
s03, calculating the self-adaptability of each grasshopper search agent, and finding out the current global optimal solution;
s04, obtaining the optimal particles;
s05, updating the parameter c by using a curve adaptive function;
s06, standardizing the distance between every two grasshoppers;
s07, passing
Figure BDA0002472332870000101
Updating the position of the current particle;
s08, trimming an external population, and selecting a global optimal position for each particle;
s09, calculating and selecting the historical optimal position of each case of each particle and comparing the historical optimal position with the population optimal position;
s10, archiving the optimal position particles of each particle, comparing the particles with the optimal position particles, and updating the optimal position particles;
s11, according to the comparison between the new non-dominant solution and the archived non-dominant solution, the external storage archive is updated, if the archive is full, one of the members before the archive is deleted.
And S12, stopping if the maximum iteration number is reached.
The general structure diagram and general flow chart of the whole system are shown in fig. 3 and fig. 4, and the intelligent scheduling optimization system for the flow production workshop comprises:
a production task receiving module: the system comprises a first terminal, a second terminal, a third terminal and a fourth terminal, wherein the first terminal is used for acquiring basic information of workshop equipment, working procedures and workpieces and a production and processing task instruction sent by the first terminal for issuing a production task, and the production and processing instruction comprises the processed workpieces, the number of the processed workpieces and required completion time;
the multi-target scheduling model establishing module: the system comprises a scheduling model, a target function and specific constraint conditions for determining workshop scheduling, and a scheduling model;
an optimized output module: and solving the scheduling model by using an improved multi-objective grasshopper optimization algorithm to obtain an optimal solution of the scheduling model, and outputting a production scheduling scheme.
Compared with the prior art, the invention has the following advantages: the flow shop is specifically analyzed, the whole workshop production process is processed numerically according to the characteristics of the flow shop, a multi-target flow shop model based on the maximum completion time, the total flow time and the delay delivery time is established, and related constraint conditions are established; meanwhile, a multi-target grasshopper algorithm in artificial intelligence is utilized, the defects that the convergence precision is insufficient and the algorithm is easy to fall into local optimum are analyzed, a curve self-adaption improvement algorithm is utilized, a multi-target flow shop model established before is solved by the curve self-adaption-based multi-target grasshopper algorithm, and a shop scheduling scheme is obtained; after a user logs in the system, multi-objective workshop scheduling optimization can be carried out by improving parameters and inputting corresponding workshop information, and intelligent scheduling of flow workshops can be effectively achieved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (6)

1. An intelligent scheduling optimization method for a flow production workshop is characterized by comprising the following steps: the method specifically comprises the following steps:
s1, acquiring basic information of workshop equipment and procedures and a production processing task instruction sent by a first terminal issuing a production task; the production processing instruction comprises workpieces to be processed, the number of the workpieces to be processed and the required completion time;
s2, determining specific targets and constraint conditions of workshop scheduling, and establishing a flow workshop scheduling model;
s3, determining the algorithm: a multi-objective grasshopper optimization algorithm (MOGOA) is used for improving parameters in the multi-objective grasshopper algorithm by curve self-adaption;
and S4, solving the multi-target flow shop model established in S2 by using the improved algorithm in S3 to obtain the optimal solution of the scheduling model, and outputting a production scheduling scheme.
2. The flow production workshop intelligent scheduling optimization method according to claim 1, wherein the method comprises the following steps: the optimization target of the objective function of the flow shop scheduling model is as follows: the maximum completion time, the total flow time and the delivery delay time are minimum.
3. The flow production workshop intelligent scheduling optimization method according to claim 1, wherein the method comprises the following steps: each workpiece can be processed by only one machine at the same time; each machine can only process one workpiece at the same time; the processing technology of each workpiece is invariable and the working procedures are invariable; each workpiece must pass through a processing stage that completes the processing of the workpiece.
4. The flow production workshop intelligent scheduling optimization method according to claim 1, wherein the method comprises the following steps: improving multi-target grasshopper algorithm and utilizing curve self-adaption
Figure FDA0002472332860000011
Mathematical model replacing updating grasshopper positions
Figure FDA0002472332860000021
And c, comparing the MOGOA with the improved MOGOA and solving a solution set of the multi-target test function by using a multi-target particle swarm algorithm MOPSO.
5. The flow production workshop intelligent scheduling optimization method according to claim 4, wherein the flow production workshop intelligent scheduling optimization method comprises the following steps: solving the model by using an improved grasshopper optimization algorithm comprises the following steps:
step 51: initializing workshop machine and workpiece information;
step 52: initializing an initial position of a rice-raising population, a population scale, various parameters involved in an algorithm and a maximum iteration number;
step 53: calculating the self-adaptability of each grasshopper search agent, and finding out the current global optimal solution;
step 54: obtaining optimal particles;
step 55: updating the parameter c by using a curve adaptive function;
step 56: standardizing the distance between every two grasshoppers;
and 57: by passing
Figure FDA0002472332860000022
Updating the position of the current particle;
step 58: pruning an external population, and selecting a global optimal position for each particle;
step 59: calculating and selecting the historical optimal position of each particle and the optimal position of the population for comparison;
step 510: archiving the optimal position particles of each particle, comparing the particles with the optimal particles, and updating the optimal particles;
step 511: the externally stored archive is updated based on a comparison of the new non-dominant solution with the archived non-dominant solution, and if the archive is full, one of the members prior to archive is deleted.
6. The flow production workshop intelligent scheduling optimization system applied to the claim 1 is characterized in that: the intelligent scheduling optimization system of the flow production workshop comprises:
a production task receiving module: the system comprises a first terminal, a second terminal, a third terminal and a fourth terminal, wherein the first terminal is used for acquiring basic information of workshop equipment, working procedures and workpieces and a production and processing task instruction sent by the first terminal for issuing a production task, and the production and processing instruction comprises the processed workpieces, the number of the processed workpieces and required completion time;
the multi-target scheduling model establishing module: the system comprises a scheduling model, a target function and specific constraint conditions for determining workshop scheduling, and a scheduling model;
an optimized output module: and solving the scheduling model by using an improved multi-objective grasshopper optimization algorithm to obtain an optimal solution of the scheduling model, and outputting a production scheduling scheme.
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