Flexible job shop scheduling method based on improved wolf pack algorithm

Abstract

The invention discloses a flexible job shop scheduling method based on an improved wolf pack algorithm, and belongs to the technical field of flexible job shop scheduling. The method comprises the following steps: initializing algorithm parameters, initializing a scheduling scheme solution population by adopting an individual double-layer coding mode, calculating target function values of all initialized individuals, introducing a Levy flight mechanism into a walking behavior of the algorithm, fusing a simulated annealing algorithm in a determination process of a first wolf individual, using a Hamming distance to define a purse attack discrimination distance, and accurately measuring a similarity distance between a fierce wolf individual and the first wolf individual in a flexible job shop scheduling problem. The problem that the global search capability is not strong enough due to the fixed step length when an existing algorithm executes a walk behavior can be solved; the problem that an existing algorithm is prone to falling into local optimum due to excessive dependence on a first wolf in the execution process is solved; and the problem that an existing algorithm is difficult to construct a flexible job shop production scheduling model is solved.

Description

technical field [0001] The invention relates to the technical field of flexible job shop scheduling, in particular to a flexible job shop scheduling method based on an improved wolf pack algorithm. Background technique [0002] With the development of social economy, the traditional manufacturing industry has faced new challenges in many aspects, and some problems that restrict the efficiency and long-term development of enterprises also need to be solved urgently. For example, in the production workshop, the scale of production continues to expand, and the types of processed products are gradually increasing. It is difficult for ordinary workshops to meet actual needs. Compared with ordinary job shops, flexible job shops are more flexible and changeable, and the manufacturing process of products is more diversified, which can greatly improve production efficiency. Therefore, the research on Flexible Job Shop Scheduling Problem (FJSP) Compared with the common Job Shop Sched...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved in the present invention is to provide a flexible job shop scheduling method based on the improved wolf pack algorithm (IWPA), solve the discrete flexible job shop scheduling problem by improving the traditional wolf pack algorithm, and solve the existing wolf group When the algorithm performs the walking behavior, the global search ability is not strong enough due to the fixed step size; solve the problem that the existing wolf pack algorithm is easy to fall into local optimum due to over-reliance on the head wolf during the execution process; solve the existing Wolf Pack Algorithm Difficult to Build a Production Scheduling Model for Flexible Job Shops; Solved the Problems of Using Manhattan Distance to Accurately Measure the Similarity Distance of Individual Codes in Discrete Flexible Job Shops

[0006] A flexible job shop scheduling method based on the improved wolf pack algorithm. By introducing the Levy flight mechanism into the walking behavior of the existing wolf pack algorithm, the simulated annealing algorithm is integrated in the process of determining the head wolf, and the Hamming distance is used to measure the flexibility. The Similarity Distance of Job Shop Individual Codes to Solve the Flexible Job Shop Scheduling Problem

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