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Multi-objective Particle Swarm Optimization and Its Application Research in Shop Scheduling Problem

Author: ChenLiLi
Tutor: WangWanLiang；XuXinLi
School: Zhejiang University of Technology
Course: Control Theory and Control Engineering
Keywords: shop scheduling multi-objective optimization particle swarm optimization Baldwinian learning strategy
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 102
Quote: 1
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Abstract


Shop scheduling is the key of business management, whose main task is to allocate the available human and material resource reasonably and to satisfy the financial or performance objectives. Currently, most of the scheduling problem is solved as a single objective problem, while it often contains multiple conflicting objectives during the actual manufacture, so the study of multi-objective scheduling problem has greater significance in practice. The shop scheduling problem is studied systematically in the paper, then a multi-objective particle swarm optimization is proposed to solve multi-objective problem. The aim of this paper is to find an effective way to solve the shop scheduling problem from the theory and actual manufacture, and to provide a theoretical basis and practical methods for the further improvement of the technology.The main work of this paper is summarized as follows:(1) An learning strategy named Baldwininan is introduced into the particle swarm optimization to protect this algorithm from being trapped in local minima. The self-study characteristics of biological populations is used that can improve the global search and convergence ability of particle swarm. And the experiment shows that this algorithm has good search performance.(2) An improved multi-objective particle swarm optimization algorithm is applied to solve the multi-objective flow shop and job shop scheduling problems. The Baldwinian learning strategy is added to this algorithm to improve the local search performance. In order to ensure the diversity of non-dominated solution set, the external elite solution set is generated during the iteration, then the crowding distance strategy is applied to update the non-dominated set. The results show that this algorithm is effective to solve the multi-objective scheduling problems and achieves better non-dominated solutions.(3) An effective hybrid algorithm based on discrete particle swarm optimization and simulated annealing algorithm is proposed, the simulated annealing algorithm is introduced in order to improve the local search ability. Then the algorithm is applied to solve the multi-objective flexible job shop scheduling problem, and the results shows that this algorithm has good search performance.Finally, this paper summarizes the total work and makes a certain prospect of the future work.

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