A multi-objective iterated local search algorithm for comprehensive energy-aware hybrid flow shop scheduling. (1st July 2019)

• Record Type: Journal Article
• Authors: Schulz, Sven; Neufeld, Janis S.; Buscher, Udo
• Is Part Of: Journal of cleaner production. Volume 224(2019)
• Page Start: 421
• Page End: 434
• Publication Date: 2019-07-01
• Subjects: Scheduling -- Energy awareness -- Hybrid flow shop -- Multi-objective optimization -- Iterated local search; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.03.155
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Growing environmental awareness and the relevance of energy costs in many industries has led to the need of improving energy efficiency in operations management; hence, energy-aware scheduling (EAS) has grown in importance. In EAS three basic strategies can be identified. First, a large part of research activities is aimed at reducing energy consumption ; second, energy costs can be reduced by making use of varying energy prices ; third, a rarely-examined aspect is load curve leveling, used to reduce demand charges or grid utilization charges. In this paper, all three strategies are integrated into one model for the first time in order to solve a multi-objective hybrid flow shop scheduling problem. A new multiphase iterated local search algorithm (ILS) is developed to determine a three-dimensional Pareto front regarding three objectives: makespan, total energy costs and peak load. Tabu lists, several time- and energy-dependent list scheduling algorithms, a right-shifting procedure and a reference point based fitness function enable high-quality solutions. A computational study is presented that analyzes the interdependencies of objectives and compare the proposed algorithm to well-known NSGA2 heuristic. The ILS is proven to be suitable in purposeful search in the solution space, which allows practical decision support.