[1]ZHAO Wenchao,GUO Peng,WANG Haibo,et al.Improved slap swarm algorithm for scheduling of flexible job shop[J].CAAI Transactions on Intelligent Systems,2022,17(2):376-386.[doi:10.11992/tis.202103036]
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Improved slap swarm algorithm for scheduling of flexible job shop

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 2 Page number: 376-386 Column: 学术论文—智能系统 Public date: 2022-03-05
Title:
Improved slap swarm algorithm for scheduling of flexible job shop
Author(s):
ZHAO Wenchao1;  GUO Peng1; 2;  WANG Haibo1; 2;  LEI Kun1
1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China;
2. Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Chengdu 610031, China
Keywords:
flexible job shop; salp swarm algorithm; Lévy flight; discretization; inertia weight; critical path; simulated annealing; local search
CLC:
TP18
DOI:
10.11992/tis.202103036
Abstract:
To deal with the scheduling of a flexible job shop and minimize the maximum completion time, an improved salp swarm algorithm (SSA), based on the standard SSA, is proposed in this paper. Two-dimensional vectors based on the process and equipment are encoded, and the population is initialized with the consideration of the machine workload. Based on a Lévy flight, the leader position update method is discretely improved. The adaptive inertial weight is introduced into the follower position update formula to balance the global search and local search performance of the algorithm. To improve the search efficiency, the proposed SSA adopts crossover and mutation operators based on the critical path to guarantee population diversity. Moreover, simulated annealing is applied to improve the local search capacity of the algorithm. Standard examples are compared and calculated, and the results verify the effectiveness of the proposed algorithm.
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