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      A Multi-Objective Cellular Memetic Optimization Algorithm for Green Scheduling in Flexible Job Shops

      , , ,
      Symmetry
      MDPI AG

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          Abstract

          In the last 30 years, a flexible job shop scheduling problem (FJSP) has been extensively explored. Production efficiency is a widely utilized objective. With the rise in environmental awareness, green objectives (e.g., energy consumption) have received a lot of attention. Nevertheless, energy consumption has received little attention. Furthermore, controllable processing times (CPT) should be considered in the field of scheduling, because they are closer to some real production. Therefore, this work investigates a FJSP with CPT (i.e., FJSP-CPT) where asymmetrical conditions and symmetrical constraints increase the difficulty of problem solving. The objectives of FJSP-CPT are to minimize simultaneously the maximum completion time (i.e., makespan) and total energy consumption (TEC). First of all, a mathematical model of this multi-objective FJSP-CPT was formulated. To optimize this problem, a novel multi-objective cellular memetic optimization algorithm (MOCMOA) was presented. The proposed MOMOA combined the advantages of cellular structure for global exploration and variable neighborhood search (VNS) for local exploitation. At last, MOCMOA was compared against other multi-objective optimization approaches by performing experiments. Numerical experiments reveal that the presented MOCMOA is superior to its competitors in 15 instances regarding three commonly used performance metrics.

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          Most cited references38

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          A fast and elitist multiobjective genetic algorithm: NSGA-II

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            Comparison of multiobjective evolutionary algorithms: empirical results.

            In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.
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              Variable neighborhood search

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                Author and article information

                Contributors
                Journal
                SYMMAM
                Symmetry
                Symmetry
                MDPI AG
                2073-8994
                April 2022
                April 18 2022
                : 14
                : 4
                : 832
                Article
                10.3390/sym14040832
                53b48564-b47c-4c85-8ff5-e449ee03cb37
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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