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
1
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
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