A two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints |
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| Affiliation: | 1. Integration and Collaboration Laboratory, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan;2. Department of Statistics, Feng Chia University, Taichung, Taiwan;1. School of Management, Hefei University of Technology, Hefei, China;2. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA;3. Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education, Hefei, China;4. Department of Computer and Information Science and Engineering, University of Florida, Gainesville, USA |
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| Abstract: | In this paper, an unrelated parallel machine scheduling problem with additional resource constraints (UPMSP_RC) from the real world manufacturing systems is studied. With the objective of minimizing the makespan, a mixed integer linear programming model is presented and several properties are analyzed. Furthermore, a two-stage adaptive fruit fly optimization algorithm (TAFOA) is proposed to solve the UPMSP_RC. At the first stage, a heuristic is proposed to generate an initial solution with high quality. At the second stage, the initial solution is adopted as the initial swarm center for further evolution. During the evolution, the search manners are selected adaptively with the guidance of the problem-specific knowledge, which is a sufficient condition of the best schedule under a given job-to-machine assignment. Moreover, the effect of parameters on the performance of the TAFOA is investigated by using the two-factor analysis of variance (ANOVA). Finally, extensive numerical comparisons are carried out to show the effectiveness of the TAFOA in solving the UPMSP_RC. |
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