期刊
COMPUTERS & OPERATIONS RESEARCH
卷 79, 期 -, 页码 279-290出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cor.2016.04.024
关键词
Dynamic scheduling; Dynamic evolutionary multi-objective optimization; Directed search strategy; Historical information; Prediction
类别
资金
- National Natural Science Foundation of China [71420107028, 71501024, 71502026, 71533001]
- Fundamental Research Funds for the Central Universities [DUT15QY32]
- Joint Research Fund for Overseas Chinese, Hong Kong and Macao Scholars of the National Natural Science Foundation of China [61428302]
This paper considers dynamic multi-objective machine scheduling problems in response to continuous arrival of new jobs, under the assumption that jobs can be rejected and job processing time is controllable. The operational cost and the cost of deviation from the baseline schedule need to be optimized simultaneously. To solve these dynamic scheduling problems, a directed search strategy (DSS) is introduced into the elitist non-dominated sorting genetic algorithm (NSGA-II) to enhance its capability of tracking changing optimums while maintaining fast convergence. The DSS consists of a population re initialization mechanism (PRM) to be adopted upon the arrival of new jobs and an offspring generation mechanism (OGM) during evolutionary optimization. PRM re-initializes the population by repairing the non-dominated solutions obtained before the disturbances occur, modifying randomly generated solutions according to the structural properties, as well as randomly generating solutions. OGM generates offspring individuals by fine-tuning a few randomly selected individuals in the parent population, employing intermediate crossover in combination with Gaussian mutations to generate offspring, and using intermediate crossover together with a differential evolution based mutation operator. Both PRM and OGM aim to strike a good balance between exploration and exploitation in solving the dynamic multi objective scheduling problem. Comparative studies are performed on a variety of problem instances of different sizes and with different changing dynamics. Experimental results demonstrate that the proposed DSS is effective in handling the dynamic scheduling problems under investigation. (C) 2016 Elsevier Ltd. All rights reserved.
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