Development of Agent-based Supply Chain Management Simulator

Kiuchi, Atsuki; Ogura, Takahiro; Ishii, Toshinari; Tsujibe, Akihisa; Nomoto, Tazu; Taguchi, Kentaro

doi:10.1299/jsmemecj.2016.s1420102

Cited by 1 publication

(8 citation statements)

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“…By running simulations and what-if scenarios within the digital twin, a SC manager can make optimized proactive decisions to improve SC efficiency and reduce risks. For example, Kiuchi et al [8] developed an agent-based SC simulator that has the flexibility to accommodate situations when the SC structure changes, when the SC managers change the inventory-policy structure (e.g., from a single-echelon to a multi-echelon inventory-control policy), when the market-demand pattern changes, etc. However, Kiuchi et al [8] has proposed just simulation model and has not proposed simulation-based-optimization methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, Kiuchi et al [8] developed an agent-based SC simulator that has the flexibility to accommodate situations when the SC structure changes, when the SC managers change the inventory-policy structure (e.g., from a single-echelon to a multi-echelon inventory-control policy), when the market-demand pattern changes, etc. However, Kiuchi et al [8] has proposed just simulation model and has not proposed simulation-based-optimization methods. In this paper, we use the simulator developed by Kiuchi et al [8] as the DSCT in our proposed simulation-based optimization methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, Kiuchi et al [8] has proposed just simulation model and has not proposed simulation-based-optimization methods. In this paper, we use the simulator developed by Kiuchi et al [8] as the DSCT in our proposed simulation-based optimization methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For the base-stock policy, depending on whether the relevant information is centrally available, and whether the decisions can be centrally made, there are independent single-echelon and multi-echelon inventory policies. Note that the agent-based SC simulator we used [8] has the flexibility to model all these inventory policies.…”

Section: Problem Setting and Formulationmentioning

confidence: 99%

“…That is, in addition to expensive evaluations of the objective function f (α), we have an additional constraint function g(α). Note that both f (α) and g(α) can be evaluated in the same run of the simulator [8]. One of our proposed methods for dealing with the constrained optimization problem is to impose a large penalty to the objective function values in the parameter region where the constraint is violated, then applying the general Bayesian optimization framework in Fig.…”

Section: Bayesian Optimization With Constraintsmentioning

confidence: 99%

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Bayesian Optimization Methods for Inventory Control with Agent-Based Supply-Chain Simulator

Ogura

Wang

et al. 2022

IEICE Trans. Fundamentals

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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.

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Section: Literature Reviewmentioning

confidence: 99%