Low carbon logistics : Reducing shipment frequency to cut carbon emissions

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

28 Scopus Citations
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Author(s)

  • Shaolong Tang
  • Wenjie Wang
  • Hong Yan
  • Gang Hao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)339-350
Journal / PublicationInternational Journal of Production Economics
Volume164
Early online date15 Dec 2014
Publication statusPublished - Jun 2015

Abstract

This study examines the issue of cutting emissions by reducing shipment frequency within the framework of periodic inventory review system, which is one of the most popular inventory control systems in practice. We first formulate a benchmark model to represent the Business-As-Usual scenario then further develop this model into a carbon emission reduction model by adding a constraint that represents the emission reduction percentage target. Because of the non-convexity of general models, we simplify our two general models into special cases and develop their propositions analytically. We then conduct comprehensive numerical experiments on the general models. Our findings suggest that by reducing shipment frequency and adjusting inventory control decisions in a periodic review system, a firm could meet a moderate emission reduction target with limited impact on total cost. Our results also show that cost associated with emission reduction is related to unit backorder cost and leadtime, while the common belief that a higher unit holding cost would lead to higher cost for emission reduction is not supported. To the best of our knowledge, no prior research has yet addressed carbon reduction considerations within the framework of periodic review inventory systems. This study provides insights in understanding in what ways the adjustment of inventory control decisions could lead to reducing carbon emissions in logistics.

Research Area(s)

  • Logistics, Low carbon, Periodic review system, Stochastic inventory model