A Simulation-Based Optimization Approach to a Lost Sale Stochastic Inventory Model

This paper describes a stochastic inventory model where the control review system is periodic; demand contains auto-correlated components; and categorized as a lost sale case. The authors propose a simulation-based optimization based on using a combination of simulated annealing, pattern search, and...

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Bibliographic Details
Published inInternational journal of operations research and information systems Vol. 3; no. 2; pp. 46 - 63
Main Authors Diaz, Rafael, Ezell, Barry Charles
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.04.2012
Subjects
Algorithms
Autocorrelation
Computer simulation
Control systems
Inventories
Inventory control
Mathematical models
Optimization
Pattern search
Ranking
Rankings
Sales
Simulated annealing
Simulation
Stochastic models
Stochasticity
Stockpiling
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Summary:This paper describes a stochastic inventory model where the control review system is periodic; demand contains auto-correlated components; and categorized as a lost sale case. The authors propose a simulation-based optimization based on using a combination of simulated annealing, pattern search, and ranking and selection methods to search and approximate solutions to this problem. Simulated annealing is employed to stochastically nominate and pre-select solutions in a decision space. Pattern search is used to systematically define a grid of competitive neighbors around pre-selected solutions. Ranking and selection is used to evaluate the performance of such competing pre-selected alternatives. On one hand, results show that service level in terms of filling rates deteriorate as the autocorrelation grows and is ignored. In contrast, service levels were kept almost invariable to the effects of the serially correlated components for solutions suggested using the proposed algorithm.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1947-9328
1947-9336
DOI:10.4018/joris.2012040103