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Supply Chain Inventory Coordination under Uncertain Demand via Combining Monte Carlo Simulation and Fitness Inheritance PSO

Supply Chain Inventory Coordination under Uncertain Demand via Combining Monte Carlo Simulation and Fitness Inheritance PSO

Heting Cao, Xingquan Zuo
Copyright: © 2015 |Volume: 6 |Issue: 1 |Pages: 22
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781466678279|DOI: 10.4018/ijsir.2015010101
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MLA

Cao, Heting, and Xingquan Zuo. "Supply Chain Inventory Coordination under Uncertain Demand via Combining Monte Carlo Simulation and Fitness Inheritance PSO." IJSIR vol.6, no.1 2015: pp.1-22. http://doi.org/10.4018/ijsir.2015010101

APA

Cao, H. & Zuo, X. (2015). Supply Chain Inventory Coordination under Uncertain Demand via Combining Monte Carlo Simulation and Fitness Inheritance PSO. International Journal of Swarm Intelligence Research (IJSIR), 6(1), 1-22. http://doi.org/10.4018/ijsir.2015010101

Chicago

Cao, Heting, and Xingquan Zuo. "Supply Chain Inventory Coordination under Uncertain Demand via Combining Monte Carlo Simulation and Fitness Inheritance PSO," International Journal of Swarm Intelligence Research (IJSIR) 6, no.1: 1-22. http://doi.org/10.4018/ijsir.2015010101

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Abstract

Supply chain coordination consists of multiple aspects, among which inventory coordination is the most widely used in practice. Inventory coordination is challenging due to the uncertainty of customers' demand. Existing researches typically assume that the demand is either a deterministic constant or a stochastic variable following a known distribution function. However, the former cannot reflect the practical costumers' demand, and the later make the model inaccurate when the demand distribution is ambiguous or highly variable. In this paper, the authors propose a Monte Carlo simulation model of such problem, which can mimic the inventory changing procedure of a supply chain with uncertain demand following an arbitrary distribution function. Then, a PSO is combined with the simulation model to achieve a coordination decision scheme to minimize the total inventory cost. Experiments show that their approach is able to produce a high quality solution within a short computational time and outperforms comparative approaches.

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