ارائه مدل فازی جهت ارزیابی میزان بکارگیری تفکر ناب از دیدگاه تولید کننده (مطالعه موردی: شرکتهای تولید کننده کفش در تبریز)
الموضوعات :
Sayyed Jamal Ashrafi Saadat
1
(M.A Student of Industrial Management - Production & Operations, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
Houshang Taghi Zadeh
2
(Assistant Professor, Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
تاريخ الإرسال : 23 الثلاثاء , ربيع الثاني, 1437
تاريخ التأكيد : 13 الإثنين , شوال, 1437
تاريخ الإصدار : 22 الخميس , ذو القعدة, 1437
الکلمات المفتاحية:
fuzzy logic,
منطق فازی,
تفکر ناب,
تولیدکننده کفش,
Lean thinking,
Shoe-manufacturer,
ملخص المقالة :
تفکر ناب یک روش مدیریت سازمان ها برای بهبود بهره وری، کارایی و کیفیت محصولات و خدمات می باشد. بر همین اساس هدف اصلی این تحقیق، بررسی و ارائه مدلی جهت ارزیابی میزان بکارگیری تفکر ناب از دیدگاه تولید کننده با استفاده از ریاضیات فازی می باشد. جامعه آماری این تحقیق کلیه تولیدکنندگان کفش در شهر تبریز می باشد. برای سنجش هر یک از شاخص های تفکر ناب از ابزار پرسشنامه استفاده شده است. روایی پرسشنامه به صورت روایی محتوایی و پایایی آن با استفاده از ضریب آلفای کرونباخ بررسی شده است. برای تعیین میزان نمره تفکر ناب و ارائه مدل، از منطق فازی در نرم افزار متلب استفاده شده است. نتایج تحقیق نشان می دهد که شاخص ارزش در شرکت های مورد مطالعه بهترین و شاخص کشش بدترین وضعیت را دارا می باشد. در نهایت نیز پس از نتیجه گیری راهکارهای لازم برای مدیران ارائه شده است.
المصادر:
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.
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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.
