An Ensemble of Meta-Heuristics for the Energy-Efficient Blocking Flowshop Scheduling Problem

This study presents an energy-efficient multi-objective ensemble of meta-heuristics (MO-EMH) including three different strategies, multi-objective genetic algorithm (MOGA) and multi-objective genetic algorithm with local search (MOGALs) for the Blocking Permutation Flowshop Scheduling Problem (BPFSP). The current research in the literature on BPFSP considers only the traditional production efficiency measures such as makespan, total flowtime or total tardiness. To the best of our knowledge, there exists no study considering the energy consumption in BPFSP. We study the bi-objective energy-efficient BPFSP (EE-BPFSP) and develop a bi-objective mixed-integer linear programming (MILP) model in order to analyze the trade-off between the makespan (Cmax) and total energy consumption (TEC). The augmented-epsilon constraint method is used for generating the Pareto optimal solution sets for the Taillard's well-known benchmark suite. As a metaheuristic method, MO-EMH, MOGA, and MOGALs are employed to solve this complex problem. Through extensive computational experiments, we show that the MO-EMH algorithm is extremely effective for solving the benchmark instances when compared to both MOGA algorithms. (C) 2019 The Authors. Published by Elsevier Ltd.