Optimization Approach for Multi-Stork Inventory Routing Problem

2011 ◽  
Vol 268-270 ◽  
pp. 1637-1640
Author(s):  
Jun Cheng Lei ◽  
Yan Peng Wu ◽  
Wen Fei Zeng

Inventory routing problem is one of the key issues to achieve integrated management of logistics. Solving this problem effectively, we can improve vehicle utilization, and reduce distribution costs. This paper, concerning the problem in inventory routing of multi-variety, multi-vendor to multi-customers, proposed heuristic algorithm based on greedy rules. The core strategy of the algorithm is to choose circularly the current lowest unit cost routine ---Hamilton delivery routes. Simulation shows that the algorithm reduces the unloaded ratio of truck, raises the efficiency of truck delivery and saves transport costs.

2012 ◽  
Vol 487 ◽  
pp. 312-316
Author(s):  
Hong Peng Zhu ◽  
Yan Peng Wu ◽  
Xiao Hong Li

The inventory-routing problem is an important approach for the enterprises to save energy and improve efficiency. The improvement of the utilization rate of vehicles and the reduction of the dispatching cost can be realized by solving this problem effectively. This thesis focuses on the inventory-routing problem and put forward a heuristic algorithm based on the greedy rules whose key strategy is to cycle select the Hamilton transportation route with the current lowest unit cost. The simulation example indicates that this algorithm can effectively improve the trucking efficiency and save the transporting cost.


Author(s):  
Ali Diabat ◽  
Claudia Archetti ◽  
Waleed Najy

In this paper, we formally introduce a variant of the inventory routing problem (IRP) that we call the fixed-partition policy IRP (FPP-IRP). In contrast to the classical IRP in which delivery routes are arbitrary, the FPP-IRP partitions customers into mutually exclusive clusters that are fixed throughout the optimization horizon, and distribution is performed separately for each cluster. By restricting the flexibility inherent in the classical IRP, the FPP-IRP attains many potential advantages. First, partitioning reduces the operational complexity of the system and allows a simpler organization of the distribution service. Second, it improves the robustness of the system by isolating disruptions to affected clusters. Third, it can fit the needs and requirements of specific applications in which consistency in the distribution policy, such as familiarity between customers and drivers and route invariance, is required. We present two fixed-partition policies for the IRP together with mathematical formulations and valid inequalities. We also present a worst-case analysis on the performance of these policies. Extensive computational results are presented to show the behavior of these policies and glean insights into their potential benefits.


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