Hande OztopM. Fatih TasgetirenD. T. EliiyiQuanke PanLevent KandillerTasgetiren, M. FatihÖztop, HandePan, Quan-KeKandiller, LeventEliiyi, Deniz Türsel2025-10-062020095741740957-41741873-679310.1016/j.eswa.2020.1132792-s2.0-85079326964https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079326964&doi=10.1016%2Fj.eswa.2020.113279&partnerID=40&md5=dcdf549b92c399f08b6860f42d226cf9https://gcris.yasar.edu.tr/handle/123456789/9181https://doi.org/10.1016/j.eswa.2020.113279The permutation flowshop scheduling problem (PFSP) has been extensively explored in scheduling literature because it has many real-world industrial implementations. In some studies multiple objectives related to production efficiency have been considered simultaneously. However studies that consider energy consumption and environmental impacts are very rare in a multi-objective setting. In this work we studied two contradictory objectives namely total flowtime and total energy consumption (TEC) in a green permutation flowshop environment in which the machines can be operated at varying speed levels corresponding to different energy consumption values. A bi-objective mixed-integer programming model formulation was developed for the problem using a speed-scaling framework. To address the conflicting objectives of minimizing TEC and total flowtime the augmented epsilon-constraint approach was employed to obtain Pareto-optimal solutions. We obtained near approximations for the Pareto-optimal frontiers of small-scale problems using a very small epsilon level. Furthermore the mathematical model was run with a time limit to find sets of non-dominated solutions for large instances. As the problem was NP-hard two effective multi-objective iterated greedy algorithms and a multi-objective variable block insertion heuristic were also proposed for the problem as well as a novel construction heuristic for initial solution generation. The performance of the developed heuristic algorithms was assessed on well-known benchmark problems in terms of various quality measures. Initially the performance of the algorithms was evaluated on small-scale instances using Pareto-optimal solutions. Then it was shown that the developed algorithms are tremendously effective for solving large instances in comparison to time-limited model. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessEnergy-efficient Scheduling, Heuristic Algorithms, Multi-objective Optimization, Permutation Flowshop Scheduling Problem, Benchmarking, Energy Efficiency, Energy Utilization, Environmental Impact, Integer Programming, Multiobjective Optimization, Optimal Systems, Pareto Principle, Scheduling, Scheduling Algorithms, Energy-efficient Scheduling, Industrial Implementation, Iterated Greedy Algorithm, Mixed Integer Programming Model, Pareto Optimal Solutions, Pareto-optimal Frontiers, Permutation Flowshop Scheduling Problems, Total Energy Consumption (tec), Heuristic AlgorithmsBenchmarking, Energy efficiency, Energy utilization, Environmental impact, Integer programming, Multiobjective optimization, Optimal systems, Pareto principle, Scheduling, Scheduling algorithms, Energy-Efficient Scheduling, Industrial implementation, Iterated greedy algorithm, Mixed integer programming model, Pareto optimal solutions, Pareto-optimal frontiers, Permutation flowshop scheduling problems, Total energy consumption (TEC), Heuristic algorithmsEnergy-Efficient SchedulingHeuristic AlgorithmsMulti-Objective OptimizationPermutation Flowshop Scheduling ProblemArticle