On the mathematical modeling of green one-to-one pickup and delivery problem with road segmentation

Creative Commons License


JOURNAL OF CLEANER PRODUCTION, vol.174, pp.1664-1678, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 174
  • Publication Date: 2018
  • Doi Number: 10.1016/j.jclepro.2017.11.040
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1664-1678
  • Keywords: Pickup and delivery problem, Road segmentation, Greenhouse gas emissions, Energy consumption, Sustainable logistics management, POLLUTION-ROUTING PROBLEM, A-RIDE PROBLEM, LARGE NEIGHBORHOOD SEARCH, FREIGHT TRANSPORTATION, TIME WINDOWS, SPLIT LOADS, LOGISTICS, ALGORITHM, PERFORMANCE, NETWORKS
  • Hacettepe University Affiliated: Yes


This paper presents a green one-to-one pickup and delivery problem including a set of new features in the domain of green vehicle routing. The objective here is to enhance the traditional models for the one-to-one pickup and delivery problem by considering several important factors, such as explicit fuel consumption (which can be translated into emissions), variable vehicle speed and road categorization (i.e., urban, non-urban). Accordingly, the paper proposes a mixed integer programming model for the problem. A case study from the Netherlands shows the applicability of the model in practice. The numerical analyses show that the investigated factors has a significant impact on operational-level logistics decisions and the selected key performance indicators. The results suggest that the proposed green model can achieve significant savings in terms of total transportation cost. The total cost reduction is found to be (i) 3.03% by the use of explicit fuel consumption estimation, (ii) up to 10.7% by accounting for variable vehicle speed and (iii) up to 10.5% by considering road categorization. As total cost involves explicit energy usage estimation, the proposed model has potential to offer a better support to aid sustainable logistics decision-making process. (C) 2017 Elsevier Ltd. All rights reserved.