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In-transit perishable product inspection

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  • White, Chelsea C.
  • Cheong, Taesu

Abstract

We determine the value of monitoring perishable freight in-transit for a single vehicle traveling from an origin to a destination. We develop a computationally practical approach for determining the optimal expected cost function and an optimal policy, based on an infinite horizon partially observed Markov decision process model. Structural properties of the optimal expected cost function and optimal policy are determined. These results can lend insight when deciding whether to acquire the capacity to monitor freight status in transit and what actions to take, based on the data from the in-transit monitoring, that optimally increase expected supply chain productivity.

Suggested Citation

  • White, Chelsea C. & Cheong, Taesu, 2012. "In-transit perishable product inspection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 310-330.
    • Handle: RePEc:eee:transe:v:48:y:2012:i:1:p:310-330
    DOI: 10.1016/j.tre.2011.08.006
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    References listed on IDEAS

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    8. Zong-Zhi Lin & James C. Bean & Chelsea C. White, 2004. "A Hybrid Genetic/Optimization Algorithm for Finite-Horizon, Partially Observed Markov Decision Processes," INFORMS Journal on Computing, INFORMS, vol. 16(1), pages 27-38, February.
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    Cited by:

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    3. Lee, In & Lee, Kyoochun, 2015. "The Internet of Things (IoT): Applications, investments, and challenges for enterprises," Business Horizons, Elsevier, vol. 58(4), pages 431-440.
    4. Dettenbach, Marcus & Thonemann, Ulrich W., 2015. "The value of real time yield information in multi-stage inventory systems – Exact and heuristic approaches," European Journal of Operational Research, Elsevier, vol. 240(1), pages 72-83.

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