DocumentCode
498905
Title
Modeling and approach for VMI cyclic inventory routing problem
Author
Zheng, Hong-Zhen ; Guo, Hao-yan ; Zhang, Xiao-dong
Author_Institution
Dept. of Comput. Sci. & Technol., Harbin Inst. of Technol. at Weihai, Harbin, China
Volume
3
fYear
2009
fDate
12-15 July 2009
Firstpage
1393
Lastpage
1398
Abstract
It presents a practical modeling and solution approach for VMI cyclic inventory routing problem (IRP) under constant customer demand rates. In VMI, the supplier takes the responsibility for replenishing his customers´ inventories based on their consumption data, with the aim of optimizing the overall distribution and inventory costs throughout the supply chain. The objective of this IRP problem is to determine a distribution plan that minimizes average distribution and inventory costs without causing any stockout at the customers. Deterministic constant customer demand rates are assumed and therefore, a long-term cyclical approach is adopted, integrating fleet sizing, vehicle routing, and inventory management. Further, realistic side-constraints such as limited storage capacities, driving time restrictions and constant replenishment intervals are taken into account. A heuristic solution approach is proposed, analyzed and evaluated against a comparable heuristic. The heuristic solution approach is well capable of finding the appropriate cost trade-off under varying circumstances. Furthermore, when fleet sizing is not considered, the heuristic outperforms an existing heuristic in finding the two-way trade-off between distribution and inventory costs.
Keywords
inventory management; supply chains; VMI cyclic inventory routing problem; constant customer demand rates; customer inventory; fleet sizing; inventory management; supply chain; vehicle routing; Collaboration; Computer science; Costs; Cybernetics; Frequency; Inventory management; Machine learning; Routing; Supply chains; Vehicle driving; Inventory routing problem (IRP); Supply chain; VMI;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location
Baoding
Print_ISBN
978-1-4244-3702-3
Electronic_ISBN
978-1-4244-3703-0
Type
conf
DOI
10.1109/ICMLC.2009.5212292
Filename
5212292
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