Minimizing the Total Cost in an Integrated Vendor--Managed Inventory System
Journal of Heuristics
Performance Measurement for Inventory Routing
Transportation Science
A coordinated approach to hedge the risks in stochastic inventory-routing problem
Computers and Industrial Engineering
Heuristics for a multiperiod inventory routing problem with production decisions
Computers and Industrial Engineering
Invited Review: Industrial aspects and literature survey: Combined inventory management and routing
Computers and Operations Research
The biobjective inventory routing problem: problem solution and decision support
INOC'11 Proceedings of the 5th international conference on Network optimization
The inventory-routing problem with transshipment
Computers and Operations Research
A single supplier-single retailer system with an order-up-to level inventory policy
Operations Research Letters
A Column-Generation Based Tactical Planning Method for Inventory Routing
Operations Research
Robust Inventory Routing Under Demand Uncertainty
Transportation Science
The exact solution of several classes of inventory-routing problems
Computers and Operations Research
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We consider a distribution problem in which a set of products has to be shipped from a supplier to several retailers in a given time horizon. Shipments from the supplier to the retailers are performed by a vehicle of given capacity and cost. Each retailer determines a minimum and a maximum level of the inventory of each product, and each must be visited before its inventory reaches the minimum level. Every time a retailer is visited, the quantity of each product delivered by the supplier is such that the maximum level of the inventory is reached at the retailer. The problem is to determine for each discrete time instant the retailers to be visited and the route of the vehicle. Various objective functions corresponding to different decision policies, and possibly to different decision makers, are considered. We present a heuristic algorithm and compare the solutions obtained with the different objective functions on a set of randomly generated problem instances.