Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.cor.2008.04.006
Title: Differentiated service inventory optimization using nested partitions and MOCBA
Authors: Chew, E.P. 
Hay Lee, L. 
Teng, S. 
Hwee Koh, C.
Keywords: Differentiated service
Inventory
Multi-objective computing budget allocation
Nested partitions method
Pareto optimality
Issue Date: May-2009
Source: Chew, E.P., Hay Lee, L., Teng, S., Hwee Koh, C. (2009-05). Differentiated service inventory optimization using nested partitions and MOCBA. Computers and Operations Research 36 (5) : 1703-1710. ScholarBank@NUS Repository. https://doi.org/10.1016/j.cor.2008.04.006
Abstract: In this paper, we consider a differentiated service inventory problem with multiple demand classes. Given that the demand from each class is stochastic, we apply a continuous review policy with dynamic threshold curves to provide differentiated services to the demand classes in order to optimize both the cost and the service level. The difficult features associated with the problem are the huge search space, the multi-objective problem which requires finding a non-dominated set of solutions and the accuracy in estimating the parameters. To address the above issues, we propose an approach that uses simulation to estimate the performance, nested partitions (NP) method to search for promising solutions, and multi-objective optimal computing budget allocation (MOCBA) algorithm to identify the non-dominated solutions and to efficiently allocate the simulation budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework. © 2008 Elsevier Ltd. All rights reserved.
Source Title: Computers and Operations Research
URI: http://scholarbank.nus.edu.sg/handle/10635/63096
ISSN: 03050548
DOI: 10.1016/j.cor.2008.04.006
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