Cold chain distribution: How to deal with node and arc time windows?

Yi Zhang, Guowei Hua, T. C.E. Cheng, Juliang Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)


Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (called node time windows) and (2) each vehicle route is available only in a predefined time interval prescribed by the government (called arc time windows). We study the effects of the retailer time window type (i.e., density of the node time-window constraints) and other cost-related factors on a distribution firm’s legitimacy choice (i.e., the firm chooses to either comply with or violate the governmental time-window policy), food quality, and pollutant emissions in the urban environment. We model the problem as an intractable vehicle routing problem with node and arc time windows and develop a genetic algorithm to tackle it. We conduct a case study to generate the managerial insights on dealing with time windows. We find that the governmental time windows will increase the distribution cost. The governmental time windows has a negative effect on pollutant emissions while showing a positive effect on food safety. Given governmental time windows, a higher demand for node time windows will result in more governmental time-window violations or lower vehicle load factor, which depends on the vehicle fixed cost, fuel price, and government penalty.

Original languageEnglish
Pages (from-to)1127-1151
Number of pages25
JournalAnnals of Operations Research
Issue number1-2
Publication statusPublished - 1 Aug 2020


  • Arc time windows
  • Cold chain distribution
  • Node time windows
  • Urban freight transport
  • Vehicle routing

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Management Science and Operations Research


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