Geoffrey Vilcot

A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem

This paper deals with a general job shop scheduling problem with multiple constraints, coming from printing and boarding industry. The objective is the minimization of two criteria, the makespan and the maximum lateness, and we are interested in finding an approximation of the Pareto frontier. We propose a fast and elitist genetic algorithm based on NSGA-II for solving the problem. The initial population of this algorithm is either randomly generated or partially generated by using a tabu search algorithm, that minimizes a linear combination of the two criteria. Both the genetic and the tabu search algorithms are tested on benchmark instances from flexible job shop literature and computational results show the interest of both methods to obtain an efficient and effective resolution method.

A tabu search algorithm for solving a multicriteria flexible job shop scheduling problem

The problem that we consider in this article is a flexible job shop scheduling problem issued from the printing and boarding industry. Two criteria have to be minimised, the makespan and the maximum lateness. Two tabu search algorithms are proposed for finding a set of non-dominated solutions: the first is based on the minimisation of one criterion subject to a bound on the second criterion (ε-constraint approach) and the second is based on the minimisation of a linear combination of criteria. These algorithms are tested on benchmark instances from the literature and the results are discussed. The total tardiness is considered as a third criterion for the second tabu search and results are presented and discussed.

A Genetic Algorithm For A Bicriteria Flexible Job Shop Scheduling Problem

The problem we consider in this paper is a flexible job shop scheduling problem. The aim is to minimize two criteria: the makespan and the maximum lateness and we are interested in finding an approximation of the Pareto front. The method that we propose is a genetic algorithm. The initial population is partially generated by using a tabu search algorithm which minimizes a linear combination of the two criteria. The method is tested on benchmark instances from literature

A TABU SEARCH ALGORITHM FOR SOLVING A BICRITERIA FLEXIBLE JOB SHOP SCHEDULING PROBLEM

Abstract We consider in this paper a scheduling problem issued from an industrial context. This problem can be seen as a flexible job shop scheduling problem. Solutions are evaluated using two criteria: the makespan and the maximum lateness. We propose a Tabu search algorithm that finds a solution with minimum makespan, respecting a given bound for the maximum lateness. This approach is called the epsilon-constraint approach in multicriteria literature and it can be used for finding the whole set of dominant criteria vectors. The Tabu search algorithm is tested on benchmark instances of the literature and results are discussed.