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

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

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

The flow shop scheduling problem consists in the assignment of a set of jobs J = {J1,...,Jn}, each of which consists of a set of operations Jj = {Oj1,...,Ojoj} onto a set of machines M = {M1,...,Mm} [5, 18]. Each operation Ojk is processed by at most one machine at a time, involving a non-negative processing time pjk. The result of the problem resolution is a schedule x, defining fo