Decision support for multi-objective flow shop scheduling by the Pareto Iterated Local Search methodology

The article describes the proposition and application of a local search metaheuristic for multi-objective optimization problems. It is based on two main principles of heuristic search, intensification through variable neighborhoods, and diversification through perturbations and successive iterations in favorable regions of the search space. The concept is successfully tested on permutation flow shop scheduling problems under multiple objectives and compared to other local search approaches. While the obtained results are encouraging in terms of their quality, another positive attribute of the approach is its simplicity as it does require the setting of only very few parameters.The metaheuristic is a key element of the Multi-Objective Optimization and Production Planning Solver MOOPPS. The software has been awarded the European Academic Software Award in Ronneby, Sweden (http://www.bth.se/llab/easa_2002.nsf), and has since been used for research and higher education in the mentioned problem domain (Geiger, 2006).

► A multi-objective flow shop scheduling problem is studied.
► An improved solution is possible by means of a novel metaheuristic: Pareto Iterated Local Search.
► Besides, the distribution of solutions in outcome space is investigated.
► Additionally, a decision support system for multi-objective scheduling is presented.