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Multi-objective approach for production line equipment selection

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A novel problem dealing with design of reconfigurable automated machining lines is considered. Such lines are composed of workstations disposed sequentially. Each workstation needs the most suitable equipment. Each available piece of equipment is characterized by its cost, can perform a set of operations and requires skills of a given level for its maintenance. A multiobjective approach is proposed to assign tasks, choose and allocate pieces of equipment to workstations taking into account all the problem parameters and constraints. The techniques developed are based on a genetic algorithm of type NSGA-II. The NSGA-II suggested is also combined with a local search. These two genetic algorithms (with and without local search) are tested for several line examples and for two versions of the considered problem: bi-objective and four-objective cases. The results of numerical tests are reported. What is the most interesting is that the assessment of these algorithms is accomplished by using three measuring criteria: the direct measures of gaps, the measures proposed by Zitzler and Thiele in 1999 and the distances suggested by Riise in 2002.
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  • Institut Charles Delaunay, UMR CNRS 6279 STMR, Laboratoire d'Optimisation des Systémes Industriels (LOSI), Université de Technologie de Troyes, 12, rue Marie Curie, BP2006 - 10010 Troyes cedex, France, phone: 0033 3 25 71 84 55, Hicham.chehade@utt.fr
Bibliografia
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  • [11] Rekiek B., Dolgui A., Delchambre A., Bratcu A., State of art of assembly lines design optimization, Annual Reviews in Control, 26(2), 163-174, 2002.
  • [12] Scholl A., Becker C., State-of-the-art exact and heuristic solution procedures for simple assembly line balancing, European Journal of Operational Research, 168, 666-693, 2006.
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  • [17] Dolgui A., Ihnatsenka I., Branch and bound algorithm for a transfer line design problem: Stations with sequentially activated multi-spindle heads, European Journal of Operational Research, 197, 1119-1132, 2009.
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  • [21] Ishibushi H., Yoshida T., Murata T., Balance between genetic search and local search in memetic algorithms for multi-objective permutation flowshop scheduling, IEEE Transactions on Evolutionary Computation, 7(2), 204-223, 2003.
  • [22] Sysoev V., Dolgui A., A Pareto optimization approach for manufacturing system design, Proceedings of the International Conference on Industrial Engineering and Production Management (IEPM’99), book 1, 75-83, 1999.
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  • [24] Srinivas N., Deb K., Multi-objective function optimization using non-dominated sorting genetic algorithms, Evolutionary Computation Journal, 2(3), 221-248, 1994.
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  • [29] Zitzler E., Thiele L., Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation, 3, 257-271, 1999.
  • [30] Ponnambalam S.G., Aravindan P., Mogileeswar Nadiu G., A multi-objective genetic algorithm for solving assembly line balancing problem, International Journal of Advanced Manufacturing Technology, 16, 341-352, 2000.
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  • [34] Makdessian L., Yalaoui F., Dolgui A., Optimisation de lignes de production, Partie II: une approche multicrit`ere, Journal of Decision Systems, 17, 337-368, 2008.
  • [35] Riise A., Comparing genetic algorithms and tabu search for multiobjective optimization, Proceedings of the IFORS conference, Edinburgh, July 8-12, 2002.
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  • [37] Deb K., Agrawal S., Pratap A., Meyarivan T., A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, Proceedings of Parallel Problem Solving from Nature VI, 849-858, 2000.
  • [38] Deb K., Multi-objective genetic algorithms: Problem difficulties and construction of test problems, Evolutionary Computation Journal, 7(3), 205-230, 1999.
  • [39] Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multi-objective genetic algorithm: NSGAII, IEEE Transactions on Evolutionary Computation, 6(2), 182-197, 2002.
  • [40] Chehade H., Amodeo L., Yalaoui F., A new hybrid multiobjective algorithm for assembly lines design, Proceedings of the World Congress in Computer Science, Computer Engineering and Applied Computing, Las Vegas, USA, July 13-16, 2009.
  • [41] Dugardin F., Amodeo L., Yalaoui F., Méthodes multi-objectif pour l’ordonnancement de lignes réentrantes, Journal of Decision Systems, 18(2), 233-257, 2009.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAR0-0066-0001
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