Presenting a Fuzzy Model to evaluate the rate of Applying Lean Thinking from the Manufacturer's Viewpoint
(Case Study: Shoe-Manufacturing Companies of Tabriz)
Subject Areas :
Industrial Management
Sayyed Jamal Ashrafi Saadat
1
,
Houshang Taghi Zadeh
2
1 - M.A Student of Industrial Management - Production & Operations, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 - Assistant Professor, Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Received: 2016-02-02
Accepted : 2016-07-18
Published : 2016-08-25
Keywords:
Abstract :
Lean thinking is a method of managing organizations in order to improve the productivity, efficiency, and quality of the products and services. Accordingly, the main purpose of the present research is to examine and present a model for the evaluation of the rate of applying lean thinking from the viewpoint of the manufacturer through the use of fuzzy mathematics. The statistical population includes all the shoe manufacturers of Tabriz. In order to measure each of the indices of lean thinking, the researchers used a questionnaire. The validity of the questionnaire was confirmed by content validity, and its reliability was assessed by using Cronbach's Alpha coefficient.To specify the score of lean thinking and to present the model, fuzzy logic was used in MATLAB software. The results indicate that the index "value" is in the best state, and the index "pull" is in the worst state. Finally, after drawing the conclusion, the researchers presented the necessary guidelines to the managers.
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pplegate, D. L.; Bixby, R. M.; Chvátal, V.; Cook, W. J. (2006), the Traveling Salesman Problem, ISBN 0-691-12993-2.
Bektas, T. (2006). The multiple traveling salesman problem: An overview of formulations and solution procedures. Omega, 34(3), 209–219.
Chan, D. and Mercier, D. (1989). IC insertion: An application of the traveling salesman Problem, International Journal of Production Research, 27, 1837–1841.
Cordeau,J.F.,Ghiani,G.,& Guerriero, E. (2014). Analysis and branch and-cut algorithm for the time dependent travelling salesman problem. Transportation Science, 48(1), 46–58.
Gromicho J., Paixao J. and Branco I. (1992). Exact solution of multiple traveling salesman problems, In: MustafaAkgül, et al., editors. Combinatorial optimization. NATO ASI Series, Berlin: Springer, F82, 291–292.
Hougardy Stefan, Mirko Wilde. (2014). On the nearest neighbor rule for the metric traveling salesman problem, Discrete Applied Mathematics.
Li. D, and H.X. Sun. (2009) “An Application Research of TSP Based on Genetic Algorithm,” Science Technology of Heilongjiang Province, (13), 27.
Nemati, K., Shamsuddin, S.M. and Saberi Kamarposhti, M. (2011). Using Imperial Competitive Algorithm for Solving Traveling Salesman Problem and Comparing the Efficiency of the Proposed Algorithm with Methods in Use, Australian Journal of Basic and Applied Sciences, 5(10), 540-543.
Peng. D.P, Z.Y. Lin, and J.Q. Wang. (2002).An Improved Genetic Algorithm for TSP Problem, Computer Engineering and Applications, (13), 91-93.
Roberti, R., & Toth, P. (2012). Models and algorithms for the asymmetric traveling sales man problem: an experimental comparison. EURO Journal on Transportation and Logistics,1,113–133.
Salari M., Z. Naji Azimi. (2012). An integer programming-based local search for the covering salesman problem, Comput. Oper. Res. 39 (11), 2594–2602.
Salari Majid, Mohammad H. Shaelaiea, Zahra Naji-Azimib. (2014). The generalized covering traveling salesman problem, Applied Soft Computing.
Soylu Banu. (2015). A general variable neighborhood search heuristic for multiple traveling salesmen problem, Computers & Industrial Engineering.
Tas Duygu, Michel Gendreaub, Ola Jabali, Gilbert Laporte. (2015). The traveling salesman problem with time-dependent service times, European Journal of Operational Research2.
Venkatesh, P., & Singh, A. (2015). Two metaheuristic approaches for the multiple traveling salesperson problem. Applied Soft Computing, 26, 74–89.
Wong, L.P., Low, M.Y.H. and Chong, C.S. (2008). A bee colony optimization algorithm for traveling salesman problem, Modeling & Simulation, AICMS 08. Second Asia International Conference on, 818– 823.
Yang, X.S. (2008). Nature-inspired metaheuristic algorithms, 1stEdition, Luniver Press.
Yang, X.S. (2010). a new metaheuristic bat-inspiredalgorithm, in: Nature Inspired Cooperative Strategiesfor Optimization, NISCO 2010, Studies in Computational Intelligence, Springer Berlin. Available from: http://arxiv.org/
Yang, X.S. (2011). Bat algorithm for multi-objectiveoptimization, International Journal Bio-Inspired Computation. Available from: http://arxiv.org/
Zhang, X. and Tang, L. (2009). A new hybrid ant colony optimization algorithm for the vehicle routing problem, Pattern Recognition Letters, 30, 848–855.
