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System reliability for a multistate intermodal logistics network with time windows

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  • Yi-Kuei Lin
  • Cheng-Fu Huang
  • Yi-Chieh Liao
  • Chih-Ching Yeh

Abstract

Network structures have been diffusely adopted in logistics systems, where the most critical target is completing the delivery within the promised timeframe. This paper focuses on a single commodity in a multistate intermodal logistics network (MILN) with transit stations and routes to involve three parameters: a route’s capacity, delivery time and time window. There is a carrier along each route whose number of available containers is multistate because the containers can be occupied by other customers. The delivery time consisting of the service time, travel time and waiting time varies with the number of containers and vehicle type. The arrival time at the transit station should be within the time window, the interval between the earliest and latest acceptable arrival times. This paper evaluates the system reliability, the probability that the MILN can successfully deliver sufficient amount of the commodity to meet market demand via several transit stations under the delivery time threshold and time windows. The system reliability can be treated as a delivery performance index and is evaluated with a proposed algorithm in terms of minimal paths. A practical case of scooter parts distribution between Taiwan and China is presented to emphasise the management implications of system reliability.

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  • Yi-Kuei Lin & Cheng-Fu Huang & Yi-Chieh Liao & Chih-Ching Yeh, 2017. "System reliability for a multistate intermodal logistics network with time windows," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1957-1969, April.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:7:p:1957-1969
    DOI: 10.1080/00207543.2016.1247997
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    Cited by:

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    2. Dixit, Vijaya & Verma, Priyanka & Tiwari, Manoj Kumar, 2020. "Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure," International Journal of Production Economics, Elsevier, vol. 227(C).
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    5. Niu, Yi-Feng & Zhao, Xia & Xu, Xiu-Zhen & Zhang, Shi-Yun, 2023. "Reliability assessment of a stochastic-flow distribution network with carbon emission constraint," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Zarghami, Seyed Ashkan & Dumrak, Jantanee, 2021. "Unearthing vulnerability of supply provision in logistics networks to the black swan events: Applications of entropy theory and network analysis," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    7. Wang, Wenzhuo & He, Yihai & Liao, Ruoyu & Cai, Yuqi & Zheng, Xin & Zhao, Yu, 2022. "Mission reliability driven functional healthy state modeling approach considering production rhythm and workpiece quality for manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    8. Riccardo Aldrighetti & Ilenia Zennaro & Serena Finco & Daria Battini, 2019. "Healthcare Supply Chain Simulation with Disruption Considerations: A Case Study from Northern Italy," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(1), pages 81-102, December.
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