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The relative impact of consignee behaviour and road traffic congestion on distribution costs

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  • Sankaran, Jayaram K.
  • Wood, Lincoln

Abstract

A theme that emerges from the empirical literature into the impact of road traffic congestion on supply chains is the compounding effect of consignee behaviour on distribution costs: even as congestion levels rise, customers of manufacturers/distributors replenish product on a just-in-time (JIT) basis, which further drives up distribution costs. The reluctance of customers to receive shipments outside business hours exacerbates the distribution task. Since the isolation of the impact of congestion on logistics costs is not always easy, organisations may be tempted to unjustifiably impute rising logistics costs to congestion. Through continuous approximation models, the present research aims to clarify the relative impact of relevant dimensions of consignee behaviour, particularly, JIT replenishment and the length of the workday, and traffic congestion on distribution costs. A critical element of such modelling is the estimation of the required number of vehicles, which in turn depends on the average daily number of commercial trip chains per vehicle. We identify two polar cases. In one case, the estimation of this number is trivial and therefore enables further analysis, including the quantification of the relative impact of congestion on distribution costs and the deduction of empirically testable hypotheses. The other case is considerably less tractable; we consider a specific instance of this case that was broadly relevant to one of the companies that participated in the research. A simulation experiment using real-world data from a NZ manufacturer-distributor serves to validate our analysis.

Suggested Citation

  • Sankaran, Jayaram K. & Wood, Lincoln, 2007. "The relative impact of consignee behaviour and road traffic congestion on distribution costs," Transportation Research Part B: Methodological, Elsevier, vol. 41(9), pages 1033-1049, November.
  • Handle: RePEc:eee:transb:v:41:y:2007:i:9:p:1033-1049
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    2. Carlos F. Daganzo, 1984. "The Distance Traveled to Visit N Points with a Maximum of C Stops per Vehicle: An Analytic Model and an Application," Transportation Science, INFORMS, vol. 18(4), pages 331-350, November.
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    1. Ansari, Sina & Başdere, Mehmet & Li, Xiaopeng & Ouyang, Yanfeng & Smilowitz, Karen, 2018. "Advancements in continuous approximation models for logistics and transportation systems: 1996–2016," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 229-252.
    2. Anna Franceschetti & Ola Jabali & Gilbert Laporte, 2017. "Continuous approximation models in freight distribution management," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 413-433, October.
    3. Santos, Luís & Coutinho-Rodrigues, João & Current, John R., 2010. "An improved ant colony optimization based algorithm for the capacitated arc routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 246-266, February.
    4. Ellegood, William A. & Campbell, James F. & North, Jeremy, 2015. "Continuous approximation models for mixed load school bus routing," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 182-198.

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