نوع مقاله : مقاله پژوهشی
نویسندگان
1 استاد، دانشکده مدیریت وحسابداری ، دانشگاه علامه طباطبایی
2 کارشناسی ارشد، مدیریت صنعتی ، تولید صنعتی دانشگاه علامه طباطبایی
چکیده
بارانداز متقاطع یکی از ابزارهای ناب سازی لجستیک بوده که برای یکی کردن بارها درطول حلقه های
جایگزینی از آن استفاده می شود. بارانداز متقاطع، فرایند حرکت محصول از طریق مراکز توزیع، بدون انبارش
می باشد. یکی از مواردی که تاثیر زیادی بر هزینه های بارانداز متقاطع دارد، مساله تعیین مسیر حرکت
V ( خودروها RP (در محیط بیرونی بارانداز متقاطع میباشد. هدف از این مقاله ارائه مدلی جهت کمینه کردن
مجموع مسافت طی توسط خودروها در محیط بیرونی بارانداز متقاطع میباشد. در این مقاله، مسیر حرکت
V خودروها توسط روش RP C ( با محدودیت ظرفیت VRP ( در شرکت ایران خودرو مدل سازی گردید و
استفاده گردید. جهت بررسی اعتبار پاسخ بدست آمده توسط )GA( جهت حل مدل از روش الگوریتم ژنتیک
استفاده گردید. همچنین جهت بررسی )SA( از الگوریتم دیگری به نام الگوریتم شبیه سازی تبریدی GA
C کارایی دو الگوریتم در مسائل مختلف VRP در بارانداز متقاطع به بررسی 01 مساله با ابعاد متفاوت پرداخته
در مسائلی با حجم SA در مسائلی با حجم کوچکتر و کارایی بیشتر GA شد. نتایج حاکی از کارایی بیشتر
بزرگتر میباشد
کلیدواژه‌ها
عنوان مقاله [English]
Vehicle routing problem in cross-dock using genetic algorithm, Case: Iran Khodro company.
نویسندگان [English]
- Laya Olfat 1
- Maghsod Amiri 1
- Ahmad Jafarian 2
چکیده [English]
Cross-docking is one of the lean logistics tools that is used for uniting the shipments during the loops replacement. Cross-docking is the process of product movement form distribution centers without storage function. Vehicle routing problem in Cross-Dock external environment has much influence on cross-dock costs. This paper provides a model for minimizing total distance traveled by vehicles in the external environment of a cross-dock. In this paper, Vehicles routes was modeled with capacitated vehicle routing problem (CVRP) and genetic algorithm (GA) was used to solve the model. To validate responses obtained by GA, simulated annealing (SA) was used. Also, to evaluate the efficacy of two algorithms (SA & GA) in different CVRP problems in cross-dock, 10 problems with different dimensions are evaluated. The results show that in problems with smaller size GA is more efficient, whereas in large size problems SA is more efficient
کلیدواژه‌ها [English]
- [cross-dock
- Vehicle routing problem
- Genetic Algorithm
- Meta-Heuristics
Apte, U. M., Viswanathan, S. )2000(, Effective Cross Docking for Improving Distribution Effciencies, International Journal of Logistics: Research and Applications, Vol. 3, No. 3, 291-302.
Ballou, R.H. (1999) Business Logistics Management, 4th Edn (New Jersey, Prentice Hall).
Barbarosoglu, G., Ozgur, D. (1999). A Tabu search algorithm for the vehicle routing problem. Computer & Operational Research, Vol. 26, pp. 255–270.
Bartholdi, J. J., Gue, K. R.(2004) The Best Shape for a Cross-dock, Transportation Science, Vol. 38, No. 2, pp. 235–244.
Brockmann, T. (1999), 21 warehousing Trends in the 21st century, IIE Solutions, Vol.31, No.7, pp.36-40.
Buzzell, R.D. & Ortmeyer, G. (1995) Channel partnerships streamline distribution, Sloan Management Review, spring, pp. 85-96.
Caric, T., Gold, H. (2008). Vehicle Routing Problem. Tonci Caric and Hrvoje Gold. Published by In-The.
Chiang A.W., Russell R., (1997).A reactive tabu search metaheuristic for the vehicle routing problem with time windows, Informs Journal on Computing, Vol. 9, No. 4.
Cook, R.L., Gibson B., MacCurdy, D., (2005). A lean approach to cross-docking, Supply Chain Management, Vol. 9, pp.54-59.
Cordeau, J. F., Gendreau, M., Laporte, G., Potvin, J. Y., & Semet, F. (2002). A guide to vehicle routing heuristics. Journal of the Operational Research Society, Vol. 53, pp. 512–522.
Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, Vol. 6, No.1. pp. 80–91.
Duhamel, C., Lacomme, P., Quilliot, A., Toussaint, H. (2011). A multi-start evolutionary local search for the two-dimensional loading capacitated vehicle routing problem. Computers & Operations Research.Vol 38 .pp. 617–640
Feitzinger, W. & Lee, H.L. (1997) Mass customization at Hewlett-Packard: the power of postponement, Harvard Business Review, p. 116-121.
Golberg, D.E., (1989). Genetic Algorithms in Search, Optimisation and Machine Learning. Addison Wesley, Reading, MA
Golden, B., Raghavan, S., Wasil, E. 2008. The Vehicle Routing Problem: Latest Advances and New Challenges. Springer Science+Business Media, LLC.
Gue, Kevin R. (2001), Cross-docking: Just-In-Time for Distribution8
Teaching Notes-Naval Postgraduate School, Monterey, CA.
Gumus, M., Bookbinder, J.H. (2004) Cross-Docking and its implication in location-distribution systems, Journal of Businuss Logistics, Vol. 25, No.2.pp 199-228.
Hammond, J.H. & Kelly, M.G. (1991) Quick response in the apparel industry, Harvard Business School Publication, No. 9, pp 690- 038.
Holland, J.H., (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI.
Jin, M., Liu, K., Burak, E. (2008) Acolumn generation approach for the split delivery vehicle routing problem.Operations Research Letters. Vol.36,pp.265–270
Knill, B. (1997) Information pulls food distribution, Supply Chain Management and Warehousing Supplement, Materials Handling Engineering, Vol. 52, No.7, pp.4-8.
Lau, H.C., Sim, M., Teo, K.M. (2003). Vehicle routing problem with time windows and a limited number of vehicles.European Journal of Operational Research, Vol. 148, pp.559-569
Laurent, B., Hao, J.K.(2009). Iterated local search for the multiple depot vehicle scheduling problem. Computers & Industrial Engineering, Vol. 57, pp. 277–286
Lee, H.Y., Jung, W., Lee K.M.(2006). Vehicle routing scheduling for cross-docking in the supply chain. Computers & Industrial Engineering. 51, 247–256.
Lin, S.W., Lee, Z.J., Ying, K.C., Lee, C.Y.(2009). Applying hybrid meta-heuristics for capacitated vehicle routing problem. Expert Systems with Applications. Vol. 36. pp..1505–1512
Lysgaard, J. (2010). The pyramidal capacitated vehicle routing problem. European Journal of Operational Research. Vol. 205. pp. 59–64
Mosheiov, G. (1998). Vehicle routing with pick-up and delivery: tour – partitioning heuristics.Computers & Industrial Engineering,Vol. 34, pp.669–684
Napolitano, M. (2000). Making the move to cross-docking. The WERC sheet-Warehousing education and research council.
Novoa, C., Storer, R. (2009). An approximate dynamic programming approach for the vehicle routing problem with stochastic demands. European Journal of Operational Research. Vol. 196. pp. 509–515
Pereira, F.B., Tavares, J. (2009). Bio-inspired Algorithms for the Vehicle Routing Problem. Springer-Verlag Berlin Heidelberg. Studies in Computational Intelligence, Vol. 161.
Prins, C. (2004). A simple and effective evolutionary algorithm for the vehicle routing problem. Computers & Operations Research, Vol. 31, pp 1985–2002.
Quinn, F.J. (1997). The Payoff. Logistic Management, Vol.36, No.12, pp.37-41.
Ratliff, H.D., Vate, J.V., Zhang, M., (1999). Network design for load-driven dross-docking systems, Technical Report, The Logistics Institute, Georgia Institute of Technology, Atlanta.
Sheffi, Y. (1990) Third party logistics: present and future prospects, Journal of Business Logistics, Vol. 11, No. 2, pp. 27- 39.
Sivanandam, S.N. Deepa, S.N. (2008). Introduction to Genetic Algorithms Springer-Verlag Berlin Heidelberg
Stalk, G., Evans, P. Shulman, L.E. (1992) .Competing on capabilities: the new rules of corporate strategy, Harvard Business Review, Vol. 70, No.2, pp. 57- 69.
Sung, C.S., Song, S.H., 2003. Integrated service network design for a cross-docking supply chain network, Journal of the Operational Research Society, Vol. 54, pp. 1283-1295.
Tavakkoli, M.R, Saremi, A.R., Ziaee, M.S. (2006). A memetic algorithm for a vehicle routing problem with backhauls. Applied Mathematics and Computation. Vol.181, pp. 1049–1060
Toth P, Vigo D, editors. (2002). the vehicle routing problem. SIAM Monographs on Discrete Mathematics and Applications, vol. 9. Philadelphia, PA: SIAM; pp. 129–54.
Tütüncüa, G. Y., Carretob, C.A., Bakerc, B.M. (2009). Avisual interactive approach to classical and mixed vehicle routing problems with backhauls. Omega.Vol. 37. pp. 138-154
)