Yuanzhen LiQuanke PanKaizhou GaoM. Fatih TasgetirenBiao ZhangJunqing LiTasgetiren, M. FatihLi, Jun-QingLi, Yuan-ZhenPan, Quan-KeGao, Kai-ZhouZhang, Biao2025-10-062021156849461568-49461872-968110.1016/j.asoc.2021.1075262-s2.0-85107089301https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107089301&doi=10.1016%2Fj.asoc.2021.107526&partnerID=40&md5=2ec605f02def1279c6c24ab28c1e5919https://gcris.yasar.edu.tr/handle/123456789/8922https://doi.org/10.1016/j.asoc.2021.107526In recent years sustainable development and green manufacturing have attracted widespread attention to environmental problems becoming increasingly serious. Meanwhile affected by the intensification of market competition and economic globalization distributed manufacturing systems have become increasingly common. This paper addresses the energy-efficient scheduling of the distributed permutation flowshop (EEDPFSP) with the criteria of minimizing both total flow time and total energy consumption. Considering the distributed and multi-objective optimization complexity an improved NSGAII algorithm (INSGAII) is proposed. First we analyze the problem-specific characteristics and designed new operators based on the knowledge of the problem. Second four constructive heuristic algorithms are proposed to produce high-quality initial solutions. Third inspired by the artificial bee colony algorithm we propose a new colony generation method using the operators designed. Fourth a local intensification is designed for exploiting better non-dominated solutions. The influence of parameter settings is investigated by experiments to determine the optimal parameter configuration of the INSGAII. Finally a large number of computational tests and comparisons have been carried out to verify the effectiveness of the proposed INSGAII in solving EEDPFSP. © 2021 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessDistributed Permutation Flowshop Scheduling, Energy Efficient, Multi-objective Optimization, Nsga-ii, Total Energy Consumption, Total Flowtime, Competition, Energy Efficiency, Energy Utilization, Heuristic Algorithms, Multiobjective Optimization, Sustainable Development, Artificial Bee Colony Algorithms, Constructive Heuristic Algorithm, Distributed Manufacturing Systems, Economic Globalization, Energy-efficient Scheduling, Environmental Problems, Permutation Flow Shops, Total Energy Consumption, Green ManufacturingCompetition, Energy efficiency, Energy utilization, Heuristic algorithms, Multiobjective optimization, Sustainable development, Artificial bee colony algorithms, Constructive heuristic algorithm, Distributed manufacturing systems, Economic globalization, Energy-Efficient Scheduling, Environmental problems, Permutation flow shops, Total energy consumption, Green manufacturingDistributed Permutation FlowshopTotal FlowtimeDistributed Permutation Flowshop SchedulingSchedulingEnergy EfficientMulti-Objective OptimizationTotal Energy ConsumptionNSGA-IIArticle