An hybrid algorithm for the industrial car sequecing problem

In most research papers, the industrial car sequencing problem, as defined during the ROADEF 2005 Challenge, has been tackled by organizing objectives in a hierarchy. However from a decision-making viewpoint it would be interesting to tackle this problem in a Pareto sense. Indeed, tackling the problem in Pareto sense can offer greater latitude to a manager by presenting him several alternative solutions. In this paper, we suggest to adapt the GISMOO algorithm to solve the industrial car sequencing problem. A comparison of the performance is carried out using well-known published algorithms and proves an advantage for GISMOO. As well, we aim to demonstrate the relevance of handling applied problems such as the industrial car sequencing problem using a Pareto multi-objective approach.

[1]  Gilbert Laporte,et al.  Iterated tabu search for the car sequencing problem , 2008, Eur. J. Oper. Res..

[2]  José Rui Figueira,et al.  Solving the Car Sequencing Problem from a Multiobjective Perspective , 2007 .

[3]  Celso C. Ribeiro,et al.  An efficient implementation of a VNS/ILS heuristic for a real-life car sequencing problem , 2008, Eur. J. Oper. Res..

[4]  Marc Gravel,et al.  Design of an Efficient Genetic Algorithm to Solve the Industrial Car Sequencing Problem , 2008 .

[5]  Marc Gravel,et al.  GISMOO: A new hybrid genetic/immune strategy for multiple-objective optimization , 2012, Comput. Oper. Res..

[6]  Bertrand Estellon,et al.  Two local search approaches for solving real-life car sequencing problems , 2008, Eur. J. Oper. Res..

[7]  Marc Gravel,et al.  Solving the industrial car sequencing problem in a Pareto sense , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[8]  Tapabrata Ray,et al.  A Memetic Algorithm for Dynamic Multiobjective Optimization , 2009 .

[9]  Van-Dat Cung,et al.  Le problème du Car Sequencing RENAULT et le Challenge ROADEF'2005 , 2005 .

[10]  Marc Gravel,et al.  Solving multiple-objective optimization problems using GISMOO algorithm , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[11]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[12]  Martin J. Oates,et al.  The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[13]  Marc Gravel,et al.  Solving real car sequencing problems with ant colony optimization , 2006, Eur. J. Oper. Res..

[14]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[15]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[16]  Christine Solnon,et al.  The car sequencing problem: Overview of state-of-the-art methods and industrial case-study of the ROADEF'2005 challenge problem , 2008, Eur. J. Oper. Res..

[17]  Marc Gravel,et al.  Crossover Operators for the Car Sequencing Problem , 2007, EvoCOP.

[18]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[19]  Caroline Gagné,et al.  Tackling the Industrial Car Sequencing Problem Using GISMOO Algorithm , 2011 .

[20]  Grégory Mounié,et al.  Greedy approach and multi-criteria simulated annealing for the car sequencing problem , 2008, Eur. J. Oper. Res..

[21]  F. Azuaje Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[22]  Jason R. Schott Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .

[23]  Thierry Benoist,et al.  Soft car sequencing with colors: Lower bounds and optimality proofs , 2008, Eur. J. Oper. Res..