Dynamic programming approximation algorithms for the capacitated lot-sizing problem

This paper provides a new idea for approximating the inventory cost function to be used in a truncated dynamic program for solving the capacitated lot-sizing problem. The proposed method combines dynamic programming with regression, data fitting, and approximation techniques to estimate the inventor...

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Veröffentlicht in:Journal of global optimization Jg. 65; H. 2; S. 231 - 259
Hauptverfasser: Büyüktahtakın, İ. Esra, Liu, Ning
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2016
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Approximation
Computation
Computer Science
Costs
Dynamic programming
Dynamics
Integer programming
Inventories
Inventory control
Mathematical analysis
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Production capacity
Production planning
Real Functions
Stockpiling
Studies
Supply chain management
Approximate dynamic programming
Approximation algorithms
Production and inventory control
Data fitting
Mixed-integer programming
Capacitated lot-sizing
ISSN:0925-5001, 1573-2916
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Beschreibung
Zusammenfassung:This paper provides a new idea for approximating the inventory cost function to be used in a truncated dynamic program for solving the capacitated lot-sizing problem. The proposed method combines dynamic programming with regression, data fitting, and approximation techniques to estimate the inventory cost function at each stage of the dynamic program. The effectiveness of the proposed method is analyzed on various types of the capacitated lot-sizing problem instances with different cost and capacity characteristics. Computational results show that approximation approaches could significantly decrease the computational time required by the dynamic program and the integer program for solving different types of the capacitated lot-sizing problem instances. Furthermore, in most cases, the proposed approximate dynamic programming approaches can accurately capture the optimal solution of the problem with consistent computational performance over different instances.
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ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-015-0349-5