Genetic programming algorithm based on local search for dynamic job shop scheduling

A local search and job shop technology, applied in program control, electrical program control, control/regulation system, etc., can solve the problems of large program scale, static scheduling constraints, falling into local optimum, etc., to achieve strong search and development capabilities, reduce Calculation time, avoiding the effect of repeated searches

Inactive Publication Date: 2017-05-03
SICHUAN YONGLIAN INFORMATION TECH CO LTD
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Problems solved by technology

[0007] The problems solved by the present invention are: first. solve the problem that many advanced optimization algorithms are subject to static scheduling constraints; second. for a specific problem, there may be no suitable rules to choose from, or there may be but not necessarily optimal problems ;Third. Solve the problem of insufficient development ability of genetic programming when dealing with multiple conflicting goals and feature requirements; Fourth. Solve the problem that local search is easy to fall into local optimum; Fifth. Solve the problem of genetic programming expressed in the form of a tree The program requires a large storage space, and the program scale is huge when dealing with dynamic problems, resulting in high computing costs

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  • Genetic programming algorithm based on local search for dynamic job shop scheduling
  • Genetic programming algorithm based on local search for dynamic job shop scheduling
  • Genetic programming algorithm based on local search for dynamic job shop scheduling

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Embodiment Construction

[0017] 1. Dynamic workshop scheduling problem

[0018] Scheduling problems in actual production usually have to face more workpieces, more diverse machines, and other unexpected working conditions, such as machine failures, arrival of new workpieces, etc. Therefore, the workshop environment is mostly dynamic, and some System information is uncertain and will change with time, and workpieces are often affected by these random disturbances, which is the dynamic job shop scheduling problem.

[0019] 2. Determine the objective function

[0020] Average flow time:

[0021] Maximum flow time: F max =max j∈J {f j} (2)

[0022] Delay Artifact Percentage:

[0023] Average Latency:

[0024] Maximum delay time: T max =max j∈Q {C j -d j} (5)

[0025] Among them, J is the set of workpieces, Q={j∈J:C j -d j >0} is the set of delayed artifacts, C j 、d j , f j Respectively represent the completion time, cut-off time and flow time of job j.

[0026] 2. Parameter setting...

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Abstract

A genetic programming algorithm based on local search for dynamic job shop scheduling is applicable to the field of job shop scheduling. According to the technical scheme adopted by the invention, the algorithm comprises the following steps: first, solving the scheduling optimization problem by use of scheduling rules; second, automatically designing scheduling rules by use of a genetic programming method; third, using a heuristic method of local search; fourth, perturbing the current optimal solution of local search; and fifth, introducing a taboo search strategy in the process of local search. The search mechanism of the algorithm achieves balance between development and exploration. Compared with the existing algorithm, compact enough and better scheduling rules can be obtained in a smaller computing time range.

Description

[0001] Technical field [0002] The invention belongs to the field of job shop technology, and is especially suitable for the optimization problem of dynamic job shop production scheduling. Background technique [0003] Manufacturing systems in the real world often involve uncertain or dynamic changes, especially in order-oriented job shop environments. Scheduling in this situation is a challenge because timely and reliable scheduling decisions are made based on changes in the shop floor. In this case, scheduling rules have been commonly used due to their simplicity of implementation and ability to deal with dynamic environmental problems. However, due to the specialization of each manufacturing system, there is still no general scheduling rule that can work in all environments. As a result, practitioners have had to manually alter their scheduling rules to account for their unique operating environment. This also implies the need for an automated method to aid in the select...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418
CPCG05B19/41865G05B2219/32252
Inventor 龚晓慧胡成华
Owner SICHUAN YONGLIAN INFORMATION TECH CO LTD
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