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A Hybrid Algorithm for Job Shop Scheduling Problem

  • Xinyu LiEmail author
  • Liang Gao
Chapter
  • 19 Downloads
Part of the Engineering Applications of Computational Methods book series (EACM, volume 2)

Abstract

Job shop Scheduling Problem (JSP) which is widespread in the real-world production system is one of the most general and important problems in various scheduling problems. Nowadays, the effective method for JSP is a hot topic in the research area of the manufacturing system. JSP is a typical NP-hard combinatorial optimization problem and has a broad engineering application background. Due to the large and complicated solution space and process constraints, JSP is very difficult to find an optimal solution within a reasonable time even for small instances. In this chapter, a hybrid Particle Swarm Optimization algorithm (PSO) based on Variable Neighborhood Search (VNS) has been proposed to solve this problem. In order to overcome the blind selection of neighborhood structures during the hybrid algorithm design, a new neighborhood structure evaluation method based on logistic model has been developed to guide the neighborhood structures selection. This method is utilized to evaluate the performance of different neighborhood structures. Then the neighborhood structures which have good performance are selected as the main neighborhood structures in VNS. Finally, a set of benchmark instances has been conducted to evaluate the performance of the proposed hybrid algorithm and the comparisons among some other state-of-the-art reported algorithms are also presented. The experimental results show that the proposed hybrid algorithm has achieved good improvement in the optimization of JSP, which also verifies the effectiveness and efficiency of the proposed neighborhood structure evaluation method.

Keywords

Job shop scheduling problem Neighborhood structure evaluation Logistic model Hybrid algorithm 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature and Science Press, Beijing 2020

Authors and Affiliations

  1. 1.School of Mechanical Science and Engineering, HUSTState Key Laboratory of Digital Manufacturing and Equipment TechnologyWuhanChina
  2. 2.School of Mechanical Science and Engineering, HUSTState Key Laboratory of Digital Manufacturing and Equipment TechnologyWuhanChina

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