A hybrid algorithm combining genetic algorithm and variable neighborhood search for process sequencing optimization of large-size problem. Issue 10 (1st November 2020)

• Record Type: Journal Article
• Authors: Luo, Yabo; Pan, Yuling; Li, Cunrong; Tang, Hongtao
• Is Part Of: International journal of computer integrated manufacturing. Volume 33:Issue 10/11(2020)
• Page Start: 962
• Page End: 981
• Publication Date: 2020-11-01
• Subjects: Genetic algorithms -- machining processes -- CAPP -- combinatorial optimization -- process planning; Computer integrated manufacturing systems -- Periodicals; 670.427
• DOI: 10.1080/0951192X.2020.1780318
• Languages: English
• ISSNs: 0951-192X

Abstract

ABSTRACT: On the premise of satisfying the process priority relationship, there are many kinds of feasible sequencing schemes. How to obtain the optimal process sequencing meeting the process priority relationship has always been a popular research area in the field of CAPP (Computer-Aided Process Planning). Currently, some achievements have been made in the field of small and medium-size problems. For large-size problems, due to the explosion of solution space, the existing bionic algorithms are easy to fall into local optimum or even non-convergence. In this paper, a hybrid algorithm combining genetic algorithm and variable neighborhood search is proposed to solve the above problems. The basic idea is to decompose the complex and huge solution space into relatively simple multi-neighborhood spaces, and then search in each neighborhood space by genetic algorithm in turn. The global optimal solution is obtained when a solution is the best solution through all neighborhood spaces. Based on this idea, the hybrid algorithm framework and neighborhood construction rules are developed, and the implementation steps of the hybrid algorithm are detailed. Taking a real-world case as the case study, the feasibility and superiority of the proposed hybrid algorithm are demonstrated by algorithm comparison tests.