Metaheuristics for Energy-Efficient No-Wait Flowshops: A Trade-off Between Makespan and Total Energy Consumption

Yüksel, Damla; Taşgetiren, M. Fatih; Kandiller, Levent; Pan, Quan-Ke

doi:10.1109/cec48606.2020.9185554

Cited by 7 publications

(1 citation statement)

References 35 publications

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“…Apart from that, the probability values used more were the higher ones (i.e., in the Q4 range), as expected. EDA GA [52] Own algorithm -- [53] 2-echelon iMOEA/D MOEA/D NSGA-II; MOGLS [90] HEA -NSGA-II; NNIA [59] iIMOALO ALO NSGA-II; MOPSO [60] NMA MA MA [73] NSGA-II NSGA-II SPEA2 [74] NSGA-II NSGA-II - [75] EE-VBIH; EE-IG; IG-ALL [76] MDSS-MOGA-DE MOABC; MOACO; MOCS [77] NSGA-II; SPEA-2 NSGA-II; SPEA-II NSGA-II; SPEA-2 [78] NSGA-II NSGA-II [81] PSO_SWS; PSO_LWR PSO PSO_SWS; PSO_LWR; PSO [72] HPSO PSO i NSGA-II; HPSO-LS…”

Section: Multi-objective Optimizermentioning

confidence: 99%

Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives

2022

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In recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried out, but considering the reduction of energy consumption as a key objective to fulfill. Finding the best combination of machines and jobs to be performed is not a trivial problem and becomes even more involved when several objectives are taken into account. Among them, the improvement of energy savings may conflict with other objectives, such as the minimization of the makespan. In this paper, we provide an in-depth review of the existing literature on multi-objective job shop scheduling optimization with metaheuristics, in which one of the objectives is the minimization of energy consumption. We systematically reviewed and critically analyzed the most relevant features of both problem formulations and algorithms to solve them effectively. The manuscript also informs with empirical results the main findings of our bibliographic critique with a performance comparison among representative multi-objective evolutionary solvers applied to a diversity of synthetic test instances. The ultimate goal of this article is to carry out a critical analysis, finding good practices and opportunities for further improvement that stem from current knowledge in this vibrant research area.

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