A hybrid genetic algorithm-TOPSIS-computer simulation approach for optimum operator assignment in cellular manufacturing systems

Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A

Volumn 34, Issue 1, 2011, Pages 57-74

  Azadeh, Ali ^a   Kor, Hamrah ^a   Hatefi, Seyed Morteza ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

Author keywords

CMS; Decision making; Entropy; Genetic algorithm; Hybrid; Optimization; Simulation; TOPSIS

Indexed keywords

CURIUM; DATA ENVELOPMENT ANALYSIS; DECISION MAKING; ENTROPY; GENETIC ALGORITHMS; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS;

AVERAGE WAITING-TIME; FITNESS FUNCTIONS; HYBRID; HYBRID GENETIC ALGORITHMS; MACHINE UTILIZATION; OPTIMUM PERFORMANCE; SIMULATION; TOPSIS;

CELLULAR MANUFACTURING;

EID: 81355139223     PISSN: 02533839     EISSN: 21587299     Source Type: Journal    
DOI: 10.1080/02533839.2011.552966     Document Type: Conference Paper

References (44)

1. Anderson, P. and Peterson, N.C., 1993. A procedure for ranking efficient units in data envelopment analysis. Management Science, 39 (10), 1261-1264.
2. Askin, R. and Huang, Y., 2001. Forming effective worker teams for cellular manufacturing. International journal of production research, 39 (11), 2431-2451. (Pubitemid 35165579)
3. Ayag, Z. and Ozdemir, R.G., 2006. A fuzzy AHP approach to evaluating machine tool alternatives. Journal of intelligent manufacturing, 17 (2), 179-190.
4. Azadeh, A. and Anvari, M., 2006. Implementation of multivariate methods as decision making models for optimization of operator allocation by computer simulation in CMS. In: Proceedings of the 2006 summer computer simulation conference, Calgary, Canada, 34-41.
5. Banker, R.D., Charnes, A., and Cooper, W.W., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30 (9), 1078-1092. (Pubitemid 14651320)
6. Bidanda, B., et al., 2005. Human related issues in manufacturing cell design, implementation, and operation: a review and survey. Computers and industrial engineering, 48 (3), 507-523. (Pubitemid 40308645)
7. Camanho, A.S. and Dyson, R.G., 2005. Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments. European journal of operational research, 161 (2), 432-446.
8. Cesani, V.I. and Steudel, H.J., 2005. A study of labour assignment flexibility in cellular manufacturing systems. Computers and industrial engineering, 48 (3), 571-591. (Pubitemid 40308649)
9. Charnes, A., Cooper, W.W., and Rhodes, E., 1978. Measuring the efficiency of decision making units. European journal of operational research, 2 (6), 429-444. (Pubitemid 9402758)
10. Chauvet, F., Proth, J.M., and Soumare, A., 2000. The simple and multiple job assignment problems. International journal of production research, 38 (14), 3165-3179. (Pubitemid 35401957)
11. Doyle, J. and Green, R., 1994. Efficiency and cross-efficiency in DEA: derivations, meanings and uses. Journal of the operational research society, 45 (5), 567-578.
12. Ebrahimipour, V., et al., 2007. A GA-PCA approach for power sector performance ranking based on machine productivity. Applied mathematics and computation, 186 (2), 1205-1215. (Pubitemid 46497328)
13. Edirisinghe, N.C.P. and Zhang, X., 2007. Generalized DEA model of fundamental analysis and its application to portfolio optimization. Journal of banking & finance, 31 (11), 3311-3335.
14. Ertay, T., 2002. An analytical approach to technology selection problem: a case study in plastic mold production. International journal of operations and quantitative management, 8 (3), 165-179.
15. Ertay, T. and Ruan, D., 2005. Data envelopment analysis based decision model for optimal operator allocation in CMS. European journal of operational research, 164 (3), 800-810. (Pubitemid 40114599)
16. Fitzpatrick, E. and Askin, R., 2005. Forming effective worker teams with multi-functional skill requirements. Computers and industrial engineering, 48 (3), 593-608. (Pubitemid 40308650)
17. Haupt, S.E. and Haupt, R.L., 1998. Practical genetic algorithm. 2nd ed. New York, NY: John Wiley.
18. Hsu-Shih, S., 2008. Incremental analysis for MCDM with an application to group TOPSIS. European journal of operational research, 186 (2), 720-734.
19. Hwang, C.L. and Yoon, K., 1981. Multiple attributes decision making methods and applications. Berlin, Germany: Springer.
20. Jeff, K.S., Aleda, V.R., and Wendell, G.G., 2001. The deployment of temporary production workers in assembly operations: a case study of the hidden costs of learning and forgetting. Journal of operation management, 21 (6), 689-707.
21. Liu, S.T., 2008. A fuzzy DEA/AR approach to the selection of flexible manufacturing systems. Computers and industrial engineering, 54 (1), 66-76.
22. Mannino, M., Hong, S.N., and Choi, I.J., 2008. Efficiency evaluation of data warehouse operations. Decision support systems, 44 (4), 883-898. (Pubitemid 351172476)
23. Molleman, E. and Slomp, J., 1999. Functional flexibility and team performance. International journal of production research, 37 (8), 1837-1858.
24. Nembhard, D.A., 2001a. An empirical comparison of forgetting model. IEEE transactions on engineering management, 48 (3), 283-291.
25. Nembhard, D.A., 2001b. Heuristic approach for assigning workers to tasks based on individual learning rates. International journal of production research, 39 (9), 1955-1968. (Pubitemid 35165602)
26. Nembhard, D.A. and Mustafa, V.U., 2000. Experiential learning and forgetting for manual and cognitive tasks. International journal of industrial ergonomics, 25 (4), 315-326.
27. Olson, D.L., 2004. Comparison of weights in TOPSIS models. Mathematical and computer modeling, 40 (7-8), 721-727. (Pubitemid 40359304)
28. Önüt, S., Soner Kara, S., and Efendigil, T., 2008. A hybrid fuzzy MCDM approach to machine tool selection. Journal of intelligent manufacturing, 19 (4), 443-453.
29. Pritsker, A.A.B., 1995. Introduction to simulation and SLAM II. 4th ed. New York, NY: John Wiley and System Publishing Corporation.
30. Rao, R.V., 2006. A material selection model using graph theory and matrix approach. Material science engineering, 43 (1-2), 248-255. (Pubitemid 44177286)
31. Roy, B., 1991. The outranking approach and the foundations of ELECTRE methods. Theory and decision, 31 (1), 49-73.
32. Roy, B., 1996. Multicriteria methodology for decision aiding. Vol. 12 of non-convex optimization and its applications. Dordrecht, Netherlands: Kluwer Academic Publishers.
33. Saaty, T.L., 1990. Decision making for leaders, the analytical hierarchy process for decision in a complex world. Pittsburgh, PA: RWS Publications.
34. Saaty, T.L., 2000. Fundamentals of decision making and priority theory with the analytic hierarchy process. University of Pittsburgh, Pittsburgh, PA: RWS Publications.
35. Sarkis, J. and Talluri, S., 1998.Adecision model for evaluation of flexible manufacturing systems in the presence of both cardinal and ordinal factors. International journal of production research, 37 (13), 2927-2938.
36. Scott, M.S., Nembhard, D.A., and Mustafa, V.U., 2001. The effects of worker learning, forgetting, and heterogeneity on assembly line productivity. Management science, 47 (12), 1639-1653. (Pubitemid 34126292)
37. Shanian, A. and Savadogo, O., 2006. TOPSIS multiplecriteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell. Journal of power sources, 159 (2), 1095-1104. (Pubitemid 44314070)
38. Simos, J. and Gestion, J., 1990. Dechets solides urbains Genevois: les faits, le traitement, l'analyse. Presses Polytechniques et Universitaires Romandes, Lausanne, Switzerland.
39. Slomp, J., Bokhorst, J., and Molleman, E., 2005. Cross training in a cellular manufacturing environment. Computer and industrial engineering, 48 (3), 609-624. (Pubitemid 40308651)
40. Slomp, J. and Molleman, E., 2000. Cross training policies and performance of teams. Proceedings of the group technology/cellular manufacturing world symposium 2000. San Juan, Puerto Rico, 107-112.
41. Süer, G.A. and Tummaluri, R.R., 2008. Multi-period operator assignment considering skills, learning and forgetting in labour-intensive cells. International journal of production research, 46 (2), 469-493. (Pubitemid 350136919)
42. Wittrock, R.J., 1992. Operator assignment and the parametric preflow algorithm. Management science, 38 (9), 1354-1359.
43. Yang, T., Chen, M.-C., and Hung, C.-C., 2007. Multiple attribute decision-making methods for the dynamic operator allocation problem. Mathematics and computers in simulation, 73 (5), 285-299. (Pubitemid 44918247)
44. Zhang, H., Gen, M., and Seo, Y., 2006. An effective coding approach for multiobjective integrated resource selection and operation sequences problem. Journal of intelligent manufacturing, 17 (4), 385-397. (Pubitemid 44273763)