Batch size and stocking levels in multi-echelon repairable systems
Management Science
Estimating the performance of multi-level inventory systems
Operations Research
Simple solution procedures for a class of two-echelon inventory problems
Operations Research
Installation vs. echelon stock policies for multilevel inventory control
Management Science
Information distortion in a supply chain: the bullwhip effect
Management Science - Special issue on frontier research in manufacturing and logistics
The production size and inventory policy for a manufacturer in a two-echelon inventory model
Computers and Operations Research
Computers and Industrial Engineering - Special issue: Logistics and supply chain management
A study on inventory replenishment policies in a two-echelon supply chain system
Computers and Industrial Engineering - Special issue: Logistics and supply chain management
On conflict and cooperation in a two-echelon inventory model for deteriorating items
Computers and Industrial Engineering
A parameter-tuned genetic algorithm to optimize two-echelon continuous review inventory systems
Expert Systems with Applications: An International Journal
Reorder decision system based on the concept of the order risk using neural networks
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
| Hi-index | 0.02 |
The objective of this paper is to develop an optimal reorder policy for a two-echelon distribution system with one central warehouse and multiple retailers. We assume the warehouse has centralized stock information and each facility uses continuous-review batch ordering policy. Since echelon stock policies may show poor performance for distribution systems, we propose a new type of policy that utilizes the centralized stock information more effectively. We define the order risk policy, which decides reorder time based on the order risk which represents the relative cost increase due to immediate order compared to delayed order. We formulate the order risk and prove the optimality of the order risk policy under the system assumption that the warehouse guarantees delivery within the fixed lead time. The order risk is derived from the marginal analysis. Since exact calculation of the order risk is complex, an approximation method is provided. Computational experiment that compares our policy with existing policies shows that a significant cost savings is obtained. The concept of the order risk can be extended to the other models.