Stochastic lot-sizing problem with inventory-bounds and constant order-capacities

In this paper, we study the stochastic version of lot-sizing problems with inventory bounds and order capacities. Customer demands, inventory bounds, and costs are subject to uncertainty and dependent with each other throughout the finite planning horizon. Two models in stochastic programming are de...

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Bibliographic Details
Published inEuropean journal of operational research Vol. 207; no. 3; pp. 1398 - 1409
Main Authors Guan, Yongpei, Liu, Tieming
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 16.12.2010
Elsevier
Elsevier Sequoia S.A
SeriesEuropean Journal of Operational Research
Subjects
Algorithms
Applied sciences
Dynamic programming
Exact sciences and technology
Inventories
Inventory bounds
Inventory control, production control. Distribution
Inventory management
Lagerhaltungsmodell
Lagerzyklus
Losgröße
Lot sizing
Lot sizing Stochastic programming Inventory bounds
Marketing
Mathematical analysis
Mathematical programming
Operational research
Operational research and scientific management
Operational research. Management science
Optimization algorithms
Programming
Stochastic models
Stochastic programming
Stochasticity
Stochastischer Prozess
Stockpiling
Studies
Lot sizing
Inventory bounds
Stochastic programming
Finite horizon
Stochastic model
Uncertain system
Inventory control
Capacity constraint
Planning
Dynamic programming
Modeling
Deterministic algorithms
Online AccessGet full text
ISSN0377-2217
1872-6860
DOI10.1016/j.ejor.2010.07.003

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Summary:In this paper, we study the stochastic version of lot-sizing problems with inventory bounds and order capacities. Customer demands, inventory bounds, and costs are subject to uncertainty and dependent with each other throughout the finite planning horizon. Two models in stochastic programming are developed: the first one has inventory-bound constraints, and the second one has both inventory-bound and order-capacity constraints. We explore structural properties of the two models and develop O(n2) and O(n2Tlogn) dynamic programming algorithms for them, respectively. Our model also generalizes the deterministic lot-sizing problem with inventory bounds. For some cases, when applied to the deterministic versions, our algorithms outperform existing deterministic algorithms.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2010.07.003