An effective artificial bee colony algorithm for multi-objective flexible job-shop scheduling problem

Authors:
Gang Zhou;Ling Wang;Ye Xu;Shengyao Wang
Affiliations:
Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, P.R. China;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, P.R. China;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, P.R. China;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, P.R. China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Year:
2011

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Abstract

In this paper, an effective artificial bee colony (ABC) algorithm is proposed to solve the multi-objective flexible job-shop scheduling problem with the criteria to minimize the maximum completion time, the total workload of machines and the workload of the critical machine simultaneously. By using the effective decoding scheme, hybrid initialization strategy, crossover and mutation operators for machine assignment and operation sequence, local search based on critical path and population updating strategy, the exploration and exploitation abilities of ABC algorithm are stressed and well balanced. Simulation results based on some widely used benchmark instances and comparisons with some existing algorithms demonstrate the effectiveness of the proposed ABC algorithm.