Multi-Objective Optimization in a Job Shop with Energy Costs through Hybrid Evolutionary Techniques

Book One

Volume

Proceedings of the International Conference on Automated Planning and Scheduling, 27

Main Track

Energy costs are an increasingly important issue in real-world scheduling, for both economic and environmental reasons. This paper deals with a variant of the well-known job shop scheduling problem, where we consider a bi-objective optimization of both the weighted tardiness and the energy costs. To this end, we design a hybrid metaheuristic that combines a genetic algorithm with a novel local search method and a linear programming approach. We also propose an efficient procedure for improving the energy cost of a given schedule. In the experimental study we analyse our proposal and compare it with the state of the art and also with a constraint programming approach, obtaining competitive results.

10.1609/icaps.v27i1.13809