Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Modelling emergency lateral transshipments in inventory systems
Management Science
A two-echelon inventory system with priority shipments
Management Science
An optimal policy for a two depot inventory problem with stock transfer
Management Science
Modeling Emergency Supply Flexibility in a Two-Echelon Inventory System
Management Science
Single-Period Multiproduct Inventory Models with Substitution
Operations Research
A Two-Location Inventory Model with Transshipment and Local Decision Making
Management Science
Optimal Policies for Transshipping Inventory in a Retail Network
Management Science
Meta-heuristic Algorithm for the Transshipment Problem with Fixed Transportation Schedules
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Dynamic Capacity Management with Substitution
Operations Research
On the Interaction Between Demand Substitution and Production Changeovers
Manufacturing & Service Operations Management
Computers & Mathematics with Applications
Computers and Operations Research
An innovation approach for achieving cost optimization in supply chain management
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper deals with a single-echelon inventory system consisting of a number of parallel local warehouses facing compound Poisson demand. There are standard holding and backorder costs as well as ordering costs at all warehouses. Normally, the warehouses replenish from an outside supplier. However, lateral transshipments between the warehouses are also possible. Such transshipments take no time but incur additional costs. When a demand occurs at a warehouse, the question is whether the whole demand or part of it should be covered by a lateral transshipment from another warehouse. Given a set of alternative decisions, our decision rule minimizes the expected costs under the assumption that no further transshipments will take place. This rule is then used repeatedly as a heuristic. A simulation study illustrates how the suggested technique performs under different conditions.