Applying genetic algorithm for can-order policies in the joint replenishment problem  

Oleh:

Nagasawa, Keisuke ; Irohara, Takashi ; Matoba, Yosuke ; Liu, Shuling

Jenis: Article from Proceeding
Dalam koleksi: The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS), 3-6 December 2013 Cebu, Philippines, page 1-9.
Topik: Inventory Modeling & Management; Logistics & Supply Chain Management (L/SCM); Supply Chain Management (SCM); Evolutionary Algorithms; Warehouse Operation & Management
Fulltext: 1117.pdf (688.55KB)

Isi artikel

Effective inventory management has played an important role in the success of supply chain management. Now, we consider a multi-item inventory. For managing multi-item inventory, generally we apply ordering policy for each item or coordinate replenishment of items. This problem is called Joint Replenishment Problem (JRP). For the JRP, many researchers apply can-order policy. Under the can-order policy, some items are re-ordered when the inventory level drops to or below re-order level and any items with inventory level at or below its can-order level can be included in the order. Many studies assume that for JRP under stochastic demand, two main policies have been compared: applying can-order policy or applying periodic replenishment policy for each item. Many studies have shown that the benefit of employing joint replenishment for correlated items is less than the benefit of using single-item replenishment. But, few researches consider how to set parameters of can-order policy of each item groups, as can-order level of each items. In this paper, we propose to find the optimal parameters of can-order policy, can-order level, for each item in lost-sales model. The main objectives in our model are minimizing items storages, stock-out and ordering cost. In order to optimize multi-objective optimization problem, we apply genetic algorithm to solve this problem. In our numerical experiment, from actual shipment data, we simulate the proposed model. Results of our proposed model are compared with the results of other methods.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali