high computational time and wrong priority output in some problem instances. In this paper, a
novel permutation method called adjusted permutation method (APM) is proposed to
compensate deficiencies of conventional permutation method. We propose Tabu search (TS)
and particle swarm optimization (PSO) to find suitable solutions at a reasonable computational
time for large problem instances. The proposed method is examined using some numerical
examples to evaluate the performance of the proposed method. The preliminary results show
that both approaches provide competent solutions in relatively reasonable amounts of time
while TS performs better to solve APM.
How to cite this paper
Rezaeinia, A & Karimi, H. (2011). Adjusted permutation method for multiple attribute decision making with meta-heuristic solution approaches.International Journal of Industrial Engineering Computations , 2(2), 369-384.
Refrences
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Turskis, Z. (2008). Multi-Attribute Contractors Ranking Method by Applying Ordering of Feasible Alternatives of Solutions in Terms of Preferability Technique. Technologic and Economic Developement, 14, 224–239.
Yoon, K., & Hwang, C. (1981). Multiple Attribute Decision Making Method and Applications. Berlin: Springer.
Blair, R., & Karnisky, W. (1994). Distribution-free statistical analysis of surface and volumetric maps. Brain Topography, 6, 19–28.
Chen, T., & Wang, J. (2009). Interval-valued fuzzy permutation method and experimental analysis on cardinal and ordinal evaluations. Journal of Computer and System Sciences, 75, 371–387.
Chin, L., & Haughton, D. (1996). Analysis of student evaluation of teaching scores using bootstrap and permutation methods. journal of Computing in Higher Education, 8, 69–84.
Chu, T. (2002). Facility Location Selection Using Fuzzy TOPSIS under group decisions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10, 687-701.
Clerc, M.(2006). Particle Swarm Optimization. London: ISTE Ltd.
Figueira, J., Salvatore, G., Matthias, E. (2005). Multiple Criteria Decision Analysis: State of the Art Surveys. New York: Springer Science.
Glover, F. (1989). Tabu search: Part I. ORSA Journal on Computing, 1, 190–206.
Glover, F. (1990). Tabu search: Part II. ORSA Journal on Computing, 2, 4-32.
Grabowski, J., & Pempera, J. (2007). The permutation flow shop problem with blocking. A tabu search approach. Omega, 35, 302-311
Grabowski, J., & Wodecki, M. (2004). A very fast tabu search algorithm for the permutation flow shop problem with makespan criterion. Computers and Operations Research, 31, 1891-1909.
Hugonnard, J., & Roy, B. (1982). Le plan d’extension du métro en banlieue parisienne, un cas type d’application de l’analyse multicritère. Les Cahiers Scientifiques de la Revue Transports, 6, 77–108.
Jacquet-Lagreze, E. (1969). L’agrégation des opinions individuelles. en Informatiques et Sciences Humaines, 4, 1-21.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. in: Proceedings of IEEE International Conference on Neural Networks, Piscataway, 1942–1948.
Korhonen, P., Moskowitz, H., & Wallenius, J. (1992). Multiple Criteria Decision Support: A review. European Journal of Operational Research, 63, 361-375.
Liao, L., & Huang, C. (2010). Tabu search for non-permutation flowshop scheduling problem with minimizing total tardiness. Applied Mathematics and Computation, 217(2), 557-567.
Nowicki, E., & Smutnicki, C. (1996). A fast tabu search algorithm for the permutation flow-shop problem. European Journal of Operational Research, 91, 160-175.
Olson, D. L. (2004). Comparison of Weights in TOPSIS Models. Mathematical and Computer Modeling. 40(7-8), 721-727.
Peng, Y. (2000). Management Decision Analysis. Peking: Science Publications.
Paelinck, J. (1977). Qualitative multiple criteria analysis: an application to airport location. En'liironment and Planning, 9, 893–695.
Pantazis, D., Nichols, T., Baillet, S., & Leahy, R. (2003). Spatiotemporal localization of significant activation in MEG using permutation tests. 18th Conference on Information Processing in Medical Imaging, 512– 523.
Rinnooy K. (1976). Machine Scheduling Problems: Classification, Complexity, and Computations, Nijhoff, The Hague.
Roy, B. (1968). Classement et choix en présence de critères multiples (la method ELECTRE), RIRO, 8, 57-75.
Roy, B., & Bertier, B. (1971). Le methods ELECTRE II: Une methode de classement en presence de criteres multiples, note de travail no. 142. Direction Scientifique, Groupe Metra.
Roy, B., (1978). ELECTRE III: Un algorithme de classements fondé sur une représentation floue des préférences en présence de critères multiples. Cahiers du CERO, 20, 3–24.
Saaty, T. L. (1990). The Analytic Hierarchy Process, McGraw-Hill, RWS Publications, Pittsburgh, PA.
Tasgetiren, M. F., Liang, Y. C., Sevkli, M. & Gencyilmaz, G. (2007). A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. European Journal of Operational Research,177, 1930-1947.
Turskis, Z. (2008). Multi-Attribute Contractors Ranking Method by Applying Ordering of Feasible Alternatives of Solutions in Terms of Preferability Technique. Technologic and Economic Developement, 14, 224–239.
Yoon, K., & Hwang, C. (1981). Multiple Attribute Decision Making Method and Applications. Berlin: Springer.