TY - JOUR
T1 - A simulation-based decision support system for a multi-echelon inventory problem with service level constraints
AU - Tsai, Shing Chih
AU - Liu, Chung Hung
N1 - Funding Information:
The authors thank the editor and two anonymous reviewers for their helpful comments. This research was supported by Taiwan National Science Council under Grant No. NSC 102-2628-H-006-002-MY3 .
Publisher Copyright:
© 2014 Elsevier Ltd.
PY - 2015/1
Y1 - 2015/1
N2 - In this paper, we present a simulation-based decision support system for solving the multi-echelon constrained inventory problem. The goal is to determine the optimal setting of stocking levels to minimize the total inventory investment costs while satisfying the expected response time targets for each field depot. We derive new decision support algorithms to be applied in different scenarios, including small-sample and large-sample cases. The first case requires that the set of alternative solutions is known at the beginning of the experiment, and the number of evaluated solutions may depend on the simulation budget (i.e.; the time available to solve the problem). In the second case, the alternative solutions are generated sequentially during the searching process, and we may terminate the algorithm when the specified sampling budget is exhausted. Empirical studies are conducted to compare the performance of the proposed algorithms with other conventional optimization approaches.
AB - In this paper, we present a simulation-based decision support system for solving the multi-echelon constrained inventory problem. The goal is to determine the optimal setting of stocking levels to minimize the total inventory investment costs while satisfying the expected response time targets for each field depot. We derive new decision support algorithms to be applied in different scenarios, including small-sample and large-sample cases. The first case requires that the set of alternative solutions is known at the beginning of the experiment, and the number of evaluated solutions may depend on the simulation budget (i.e.; the time available to solve the problem). In the second case, the alternative solutions are generated sequentially during the searching process, and we may terminate the algorithm when the specified sampling budget is exhausted. Empirical studies are conducted to compare the performance of the proposed algorithms with other conventional optimization approaches.
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U2 - 10.1016/j.cor.2014.07.018
DO - 10.1016/j.cor.2014.07.018
M3 - Article
AN - SCOPUS:84907718933
SN - 0305-0548
VL - 53
SP - 118
EP - 127
JO - Computers and Operations Research
JF - Computers and Operations Research
ER -