Minimizing the Total Cost in an Integrated Vendor--Managed Inventory System
Journal of Heuristics
A simulation framework for real-time management and control of inventory routing decisions
Proceedings of the 38th conference on Winter simulation
Performance Measurement for Inventory Routing
Transportation Science
A Price-Directed Approach to Stochastic Inventory/Routing
Operations Research
Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching
Transportation Science
An optimization algorithm for the inventory routing problem with continuous moves
Computers and Operations Research
Scenario Tree-Based Heuristics for Stochastic Inventory-Routing Problems
INFORMS Journal on Computing
Using scenario trees and progressive hedging for stochastic inventory routing problems
Journal of Heuristics
Invited Review: Industrial aspects and literature survey: Combined inventory management and routing
Computers and Operations Research
Expert Systems with Applications: An International Journal
Coordination of split deliveries in one-warehouse multi-retailer distribution systems
Computers and Industrial Engineering
The inventory-routing problem with transshipment
Computers and Operations Research
Mathematical and Computer Modelling: An International Journal
Robust Inventory Routing Under Demand Uncertainty
Transportation Science
Proceedings of the Winter Simulation Conference
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This work is motivated by the need to solve the inventory routing problem when implementing a business practice called vendor managed inventory replenishment (VMI). With VMI, vendors monitor their customers' inventories and decide when and how much inventory should be replenished at each customer. The inventory routing problem attempts to coordinate inventory replenishment and transportation in such a way that the cost is minimized over the long run. We formulate a Markov decision process model of the stochastic inventory routing problem and propose approximation methods to find good solutions with reasonable computational effort. We indicate how the proposed approach can be used for other Markov decision processes involving the control of multiple resources.