An aborescent network formulation and dual ascent based procedure for the two-stage multi-item dynamic demand lotsize problem

Traditional approaches for modeling and solving dynamic demand lotsize problems are based on Zangwill's single-source network and dynamic programming algorithms. An arborescent fixed-charge network (ARBNET) programming model and dual ascent based branch-and-bound procedure for the 2-stage multi...

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Bibliographic Details
Published inDecision sciences Vol. 25; no. 1; p. 103
Main Authors Gao, Li-Lian, Robinson, E Powell
Format Journal Article
LanguageEnglish
Published Atlanta American Institute for Decision Sciences 01.01.1994
Subjects
Algorithms
Dynamic programming
Inventory management
Mathematical models
Statistical analysis
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Summary:Traditional approaches for modeling and solving dynamic demand lotsize problems are based on Zangwill's single-source network and dynamic programming algorithms. An arborescent fixed-charge network (ARBNET) programming model and dual ascent based branch-and-bound procedure for the 2-stage multi-item dynamic demand lotsize problem is presented. Computational results show that the new approach is significantly more efficient than earlier solution strategies. The largest set of problems that could be solved using dynamic programming contained 4 end items and 12 time periods, and required 475.38 CPU seconds per problem. Similar results verify the superiority of the new approach for handling backlogged demand. An additional advantage of the algorithm is the availability of a feasible solution, with a known worst-case optimality gap, throughout the problem-solving process.
ISSN:0011-7315
1540-5915