A Multi-agent Based Approach to the Inventory Routing Problem
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Integrating local search and network flow to solve the inventory routing problem
Eighteenth national conference on Artificial intelligence
Discrete Applied Mathematics - The fourth international colloquium on graphs and optimisation (GO-IV)
Journal of Global Optimization
The Period Vehicle Routing Problem with Service Choice
Transportation Science
Performance Measurement for Inventory Routing
Transportation Science
An Efficient Heuristic Algorithm for a Two-Echelon Joint Inventory and Routing Problem
Transportation Science
The Period Vehicle Routing Problem with Service Choice
Transportation Science
An optimization algorithm for the inventory routing problem with continuous moves
Computers and Operations Research
A coordinated approach to hedge the risks in stochastic inventory-routing problem
Computers and Industrial Engineering
Coordination of split deliveries in one-warehouse multi-retailer distribution systems
Computers and Industrial Engineering
Mathematical and Computer Modelling: An International Journal
Robust Inventory Routing Under Demand Uncertainty
Transportation Science
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We consider a distribution system consisting of a single warehouse and many geographically dispersed retailers. Each retailer faces demands for a single item which arise at a deterministic, retailer specific rate. The retailers? stock is replenished by a fleet of vehicles of limited capacity, departing and returning to the warehouse and combining deliveries into efficient routes. The cost of any given route consists of a fixed component and a component which is proportional with the total distance driven. Inventory costs are proportional with the stock levels. The objective is to identify a combined inventory policy and a routing strategy minimizing system-wide infinite horizon costs. We characterize the asymptotic effectiveness of the class of so-called Fixed Partition policies and those employing Zero Inventory Ordering. We provide worst case as well as probabilistic bounds under a variety of probabilistic assumptions. This insight is used to construct a very effective algorithm resulting in a Fixed Partition policy which is asymptotically optimal within its class. Computational results show that the algorithm is very effective on a set of randomly generated problems.