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A Multi-item multi-depot inventory routing problem |
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| รหัสดีโอไอ | |
| Title | A Multi-item multi-depot inventory routing problem |
| Creator | Chayathuch Phuaksaman |
| Contributor | Paveena Chaovalitwongse |
| Publisher | Chulalongkorn University |
| Publication Year | 2551 |
| Keyword | Vehicle routing problem, Physical distribution of goods, Commercial products -- Transportation, Heuristic algorithms, ปัญหาการจัดเส้นทางเดินรถ, การกระจายสินค้าจากผู้ผลิตสู่ผู้บริโภค, การขนส่งสินค้า, ฮิวริสติกอัลกอริทึม |
| Abstract | This thesis studies a multi-retailer multi-depot distribution system with multi-item. In this system, the products must be spread from a group of depot to several retailers in a finite time horizon. Shipments from the depots to retailers are made by a set of homogeneous vehicle with limited carrying capacity. Customer demand of items at each retailer is dynamic with time period. Decisions in this system include replenishment quantities of each item at each retailer during considered periods and its corresponding delivery route with minimum total inventory and routing cost during considered time horizon. For the solution, this dissertation uses the concept of problem decomposition by decomposing the main problem into many sub problems. Each sub problem is the lot-sizing problem for an item in an outlet or a set of items in outlets. The lot-sizing problem considered in this dissertation differs from the basic lot-sizing problem in which the setup cost is dynamic. The setup costs are provided by the calculation of the difference on route cost. Three heuristic algorithms, SIOH, AIOOH and AIOVH, are proposed to solve this complicated problem. The performances of the heuristics are tested by comparing to solution obtained by CPLEX 8.0 for small-sized problem and Lot-for-Lot policy for medium and large-sized problem. The computational results show that the proposed heuristics can achieve the optimal solution in small-sized problem. Moreover the heuristics AIOOH and AIOVH show improvement from Lot-for-Lot policy between 15-25% in average for the medium and large-sized problem. |
| URL Website | cuir.car.chula.ac.th |
