A simulation–optimization approach for a service-constrained multi-echelon distribution network

Noordhoek, Marije; Dullaert, Wout; Lai, David S.W.; Leeuw, Sander de

doi:10.1016/j.tre.2018.02.006

Cited by 27 publications

(6 citation statements)

References 56 publications

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“…The simulation-based optimization approach has also been widely used to solve the problems of congestion pricing (Chen et al, 2016;He et al, 2017), traffic signal control (Chong and Osorio, 2015;Osorio and Bierlaire, 2013;Nanduri, 2015a, 2015b), transit scheduling (Zhang and Xu, 2017), vehicle sharing (Cardin et al, 2017), supply chain management (Noordhoek et al, 2018), liner shipping (Dong and Song, 2009), etc. In general, there are three classes of methods for the simulation-based optimization, including the directsearch method, the stochastic gradient method, and the metamodel (or surrogate) method (Osorio and Bierlaire, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Surrogate-based simulation optimization approach for day-to-day dynamics model calibration with real data

Cheng

Wang

Liu

et al. 2019

Transportation Research Part C: Emerging Technologies

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Section: Literature Reviewmentioning

confidence: 99%

Surrogate-based simulation optimization approach for day-to-day dynamics model calibration with real data

Cheng

Wang

Liu

et al. 2019

Transportation Research Part C: Emerging Technologies

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“…Thus, combining simulation and optimisation is an effective way to find a feasible optimal or nearly optimal solution. Such a strategy deploys the optimisation technique to identify the optimal variables and uses simulation to see the impact of random parameters on the model [37]. We solve each scenario several times by generating random parameters and computing the moving average of the variables.…”

Section: 𝜕𝜋 Dnmentioning

confidence: 99%

Developing a resilient supply chain in complex product systems through investment in reliability and cooperative contracts

Gheidar-kheljani,

Halat

2024

RAIRO-Oper. Res.

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In recent years, finding mitigation strategies for supply chain disruptions has become one of the most critical challenges for businesses. This issue is crucial for complex product industries because of their role in the modern economy, few suppliers, and their need for high investment in research and development (R&D). This paper studies a resilient supply chain in complex product systems to overcome its specific challenges through supplier reliability enhancement and cooperative contracts. Utilising a game theoretic approach and analytical models, this paper aims to improve the supply chain performance from the resilience perspective while considering R&D investment, supplier learning effect, buyer fairness concern, and market sensitivity to the product’s technology. Investment in supplier reliability enhancement with different contracts is proposed to mitigate disruption risks for a two-echelon supply chain. Analytical mathematical models have been developed, and a simulation approach has been used in optimisation. The results show how proposed contracts effectively increase supply chain performance from financial and resilience perspectives. Moreover, the market sensitivity to the product’s technological level and the sensitivity to the price could adversely affect performance. The buyer’s fairness concern also improves the profit loss while decreasing the service level slightly.

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“…e agent-based model can be combined with optimization techniques in two ways: (1) embedding an optimizing method into the physical behavior of agents (e.g., by translating optimization algorithms like route-search into agents' behavior) [86]; (2) utilizing the agent-based model in a simulation-based optimization framework to obtain the simulation results, which are designed as an intermediate component to provide input data iteratively [87].…”

Section: Integrating Agent-based Approaches In Optimizationmentioning

confidence: 99%

An Overview of Agent-Based Models for Transport Simulation and Analysis

Huang

Cui

Zhang

et al. 2022

Journal of Advanced Transportation

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This article presents an overview of the agent-based modeling and simulation approach and its recent developments in transport fields, with the purpose of discovering the advantages and gaps and encouraging more valuable investigations and applications of agent-based models. We clarify the agent-based model from agents, the background of development, and the basic structure applied in transport systems. Then, the agent-based transport modeling toolkits are discussed. The applications of agent-based models in transport systems are reviewed in three time scale models followed by an additional discussion of hybrid modeling approaches. The extensive modeling of the beliefs, desires, learning, and adaptability of individuals and the optimization problems using agent-based models are explored. Besides, we point out some limitations in terms of calibration and validation procedure, agents’ behavior modeling, and computing efficiency. In conclusion, some recommendations are given and suggest potential and insightful directions such as Big Data and Digital Twin for future research.

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A simulation–optimization approach for a service-constrained multi-echelon distribution network

Cited by 27 publications

References 56 publications

Surrogate-based simulation optimization approach for day-to-day dynamics model calibration with real data

Surrogate-based simulation optimization approach for day-to-day dynamics model calibration with real data

Developing a resilient supply chain in complex product systems through investment in reliability and cooperative contracts

An Overview of Agent-Based Models for Transport Simulation and Analysis

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