A closed vendor managed inventory system under a mixed fleet of electric and conventional vehicles. (June 2021)

• Record Type: Journal Article
• Authors: Soysal, Mehmet; Belbağ, Sedat; Sel, Çağrı
• Is Part Of: Computers & industrial engineering. Volume 156(2021)
• Publication Date: 2021-06
• Subjects: Vendor managed inventory system -- Closed-loop inventory routing problem -- Mixed fleet -- Electric vehicles -- Energy consumption -- Returnable transport items; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107210
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A heterogeneous fleet Closed-Loop Inventory Routing Problem for RTIs is addressed. The proposed model incorporates comprehensive energy estimation approaches for both electric and conventional vehicles. The model respects uncertain reverse returnable transport items flow from customers. The added value of the model has been presented through two cases and a broad set of experiments. The model is capable to provide trade-off analyses for sustainable logistics management. Abstract: 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.