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Bayesian Optimization Methods for Inventory Control with Agent-Based Supply-Chain Simulator
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Summary :
We propose a penalty-based and constraint Bayesian optimization methods with an agent-based supply-chain (SC) simulator as a new Monte Carlo optimization approach for multi-echelon inventory management to improve key performance indicators such as inventory cost and sales opportunity loss. First, we formulate the multi-echelon inventory problem and introduce an agent-based SC simulator architecture for the optimization. Second, we define the optimization framework for the formulation. Finally, we discuss the evaluation of the effectiveness of the proposed methods by benchmarking it against the most commonly used genetic algorithm (GA) in simulation-based inventory optimization. Our results indicate that the constraint Bayesian optimization can minimize SC inventory cost with lower sales opportunity loss rates and converge to the optimal solution 22 times faster than GA in the best case.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E105-A No.9 pp.1348-1357
- Publication Date
- 2022/09/01
- Publicized
- 2022/02/25
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2021EAP1110
- Type of Manuscript
- PAPER
- Category
- Mathematical Systems Science
Authors
Takahiro OGURA
Hitachi, Ltd.,Japan Advanced Institute of Science and Technology (JAIST)
Haiyan WANG
Hitachi America, Ltd.
Qiyao WANG
Hitachi America, Ltd.
Atsuki KIUCHI
Hitachi America, Ltd.
Chetan GUPTA
Hitachi America, Ltd.
Naoshi UCHIHIRA
Japan Advanced Institute of Science and Technology (JAIST)
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Takahiro OGURA, Haiyan WANG, Qiyao WANG, Atsuki KIUCHI, Chetan GUPTA, Naoshi UCHIHIRA, "Bayesian Optimization Methods for Inventory Control with Agent-Based Supply-Chain Simulator" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 9, pp. 1348-1357, September 2022, doi: 10.1587/transfun.2021EAP1110.
Abstract: We propose a penalty-based and constraint Bayesian optimization methods with an agent-based supply-chain (SC) simulator as a new Monte Carlo optimization approach for multi-echelon inventory management to improve key performance indicators such as inventory cost and sales opportunity loss. First, we formulate the multi-echelon inventory problem and introduce an agent-based SC simulator architecture for the optimization. Second, we define the optimization framework for the formulation. Finally, we discuss the evaluation of the effectiveness of the proposed methods by benchmarking it against the most commonly used genetic algorithm (GA) in simulation-based inventory optimization. Our results indicate that the constraint Bayesian optimization can minimize SC inventory cost with lower sales opportunity loss rates and converge to the optimal solution 22 times faster than GA in the best case.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAP1110/_p
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@ARTICLE{e105-a_9_1348,
author={Takahiro OGURA, Haiyan WANG, Qiyao WANG, Atsuki KIUCHI, Chetan GUPTA, Naoshi UCHIHIRA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Bayesian Optimization Methods for Inventory Control with Agent-Based Supply-Chain Simulator},
year={2022},
volume={E105-A},
number={9},
pages={1348-1357},
abstract={We propose a penalty-based and constraint Bayesian optimization methods with an agent-based supply-chain (SC) simulator as a new Monte Carlo optimization approach for multi-echelon inventory management to improve key performance indicators such as inventory cost and sales opportunity loss. First, we formulate the multi-echelon inventory problem and introduce an agent-based SC simulator architecture for the optimization. Second, we define the optimization framework for the formulation. Finally, we discuss the evaluation of the effectiveness of the proposed methods by benchmarking it against the most commonly used genetic algorithm (GA) in simulation-based inventory optimization. Our results indicate that the constraint Bayesian optimization can minimize SC inventory cost with lower sales opportunity loss rates and converge to the optimal solution 22 times faster than GA in the best case.},
keywords={},
doi={10.1587/transfun.2021EAP1110},
ISSN={1745-1337},
month={September},}
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TY - JOUR
TI - Bayesian Optimization Methods for Inventory Control with Agent-Based Supply-Chain Simulator
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1348
EP - 1357
AU - Takahiro OGURA
AU - Haiyan WANG
AU - Qiyao WANG
AU - Atsuki KIUCHI
AU - Chetan GUPTA
AU - Naoshi UCHIHIRA
PY - 2022
DO - 10.1587/transfun.2021EAP1110
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2022
AB - We propose a penalty-based and constraint Bayesian optimization methods with an agent-based supply-chain (SC) simulator as a new Monte Carlo optimization approach for multi-echelon inventory management to improve key performance indicators such as inventory cost and sales opportunity loss. First, we formulate the multi-echelon inventory problem and introduce an agent-based SC simulator architecture for the optimization. Second, we define the optimization framework for the formulation. Finally, we discuss the evaluation of the effectiveness of the proposed methods by benchmarking it against the most commonly used genetic algorithm (GA) in simulation-based inventory optimization. Our results indicate that the constraint Bayesian optimization can minimize SC inventory cost with lower sales opportunity loss rates and converge to the optimal solution 22 times faster than GA in the best case.
ER -
English
