4.7 Article

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

Journal

COMPUTERS & INDUSTRIAL ENGINEERING
Volume 156, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2021.107210

Keywords

Closed-loop inventory routing problem; Mixed fleet; Electric vehicles; Energy consumption; Returnable transport items

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This study addresses the Closed-Loop Inventory Routing Problem under a mixed fleet of electric and conventional vehicles, presenting a Mixed-Integer Linear Programming model and a Fix&Optimize algorithm to tackle larger problem instances. The models incorporate comprehensive estimation approaches for energy consumption, allowing for better estimation of costs and emissions, while also considering uncertain reverse returnable transport items flow from customers. Numerical analyses demonstrate the benefits of using the provided models, with the Fix&Optimize algorithm resulting in lower costs and shorter computation times compared to the Mixed-Integer Linear Programming model.
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.

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