An improved multi-objective grey wolf optimization algorithm for fuzzy blocking flow shop scheduling problem

This paper formulates a bi-criteria fuzzy blocking flow shop scheduling problem with fuzzy processing time and fuzzy due date. An improved multi-objective grey wolf optimization (MOGWO) algorithm is proposed to solve this combinational optimization problem. The proposed MOGWO utilizes the ranked-ord...

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Bibliographic Details
Published in:2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) pp. 661 - 667
Main Authors: Zhi Yang, Cungen Liu, Weixin Qian
Format: Conference Proceeding
Language:English
Published: IEEE 01.03.2017
Subjects:
Artificial intelligence
blocking flow shop
fuzzy scheduling problem
grey wolf optimization
Job shop scheduling
multi-objective optimization
Optimization
Silicon
Online Access:Get full text
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Summary:This paper formulates a bi-criteria fuzzy blocking flow shop scheduling problem with fuzzy processing time and fuzzy due date. An improved multi-objective grey wolf optimization (MOGWO) algorithm is proposed to solve this combinational optimization problem. The proposed MOGWO utilizes the ranked-order-value (ROV) rule for solution representation, employs a dynamic maintenance strategy to maintain archive, and develops a thorough mechanism for leader selection. In addition, to improve the performance of the neighborhood search, a VNS structure with three randomly ranked neighborhood search operators is introduced and implemented on the members of archive that may become the selected leaders. The proposed MOGWO is tested on a fuzzy blocking flow shop scheduling problem of panel block construction, and is compared with general MOGWO and multi-objective particle swarm optimization (MOPSO). Computational results suggest that the proposed MOGWO is superior to the compared algorithms in terms of the convergence, spread and coverage of the optimal solutions. This demonstrates the feasibility and effectiveness of the proposed MOGWO.
DOI:10.1109/IAEAC.2017.8054099