A closed vendor managed inventory system under a mixed fleet of electric and conventional vehicles

SOYSAL M., Belbag S., Sel C.

COMPUTERS & INDUSTRIAL ENGINEERING, vol.156, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 156
  • Publication Date: 2021
  • Doi Number: 10.1016/j.cie.2021.107210
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Keywords: Closed-loop inventory routing problem, Mixed fleet, Electric vehicles, Energy consumption, Returnable transport items, PRODUCTION ROUTING PROBLEM, LOOP SUPPLY CHAIN, RETURNABLE TRANSPORT, REVERSE LOGISTICS, PERISHABLE PRODUCTS, MODEL, EMISSIONS, PICKUP
  • Hacettepe University Affiliated: Yes


In a closed-loop supply chain, where a Vendor Managed Inventory system is executed, the Closed-Loop Inventory Routing Problem is one of the main problems confronted by logistics decision-makers. This study addresses a Closed-Loop Inventory Routing Problem under a mixed fleet of electric and conventional vehicles. The problem is formulated as a Mixed-Integer Linear Programming model and a Fix&Optimize algorithm is developed to tackle larger problem instances. The proposed decision support models incorporate comprehensive estimation approaches for energy consumption of both electric and conventional vehicles that allow to better estimate fuel and electric cost and transportation emissions. The models respect uncertain reverse returnable transport items flow from customers as well. The numerical analyses demonstrate the benefits that could be obtained by means of the provided models. The Fix&Optimize heuristic yields in 5.72% lower costs within 59.23% shorter computation times on average compared to the Mixed-Integer Linear Programming model. The proposed models are capable to provide trade-off analyses for sustainable logistics management.