Improvements for multi-objective flow shop scheduling by Pareto Iterated Local Search

TL;DR

This paper introduces a simple yet effective Pareto Iterated Local Search metaheuristic for multi-objective flow shop scheduling, demonstrating promising results and ease of parameter tuning.

Contribution

It proposes a novel Pareto Iterated Local Search method that combines intensification and diversification for multi-objective scheduling problems.

Findings

Encouraging solution quality compared to other local search methods

Effective in handling permutation flow shop scheduling with multiple objectives

Requires only a few parameters to tune

Abstract

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.