Reverse logistics network design (RLND) is getting momentum as more organizations realize the benefits of recycling or remanufacturing of their end-of-life products. Similarly, there is an impetus for organizations to become more environmentally conscious or green. This environmental context has driven many organizations to invest in green technologies, with a recent emphasis on reducing greenhouse gas emissions. This environmental investment situation and decision can be addressed through the integration of facility location, operational planning, and vehicle type selection, while simultaneously accounting for carbon emissions from vehicles, inspection centers, and remanufacturing centers in a reverse logistics (RL) context. In the current study, we present a mixed-integer linear programming (MILP) model to solve a multi-tier multi-period green RL network, including vehicle type selection. This research integrates facility locations, vehicle type selection with emissions producing from transportation and operations at various processing centers. Prior research does not account for carbon emissions for this design problem type. Valuable managerial insights are obtained when incorporating carbon emissions cost.
- 512 Business and Management
- Reverse logistics
- Network design
- Mixed integer linear programming
- Carbon footprint
Areas of Strength and Areas of High Potential (AoS and AoHP)
- AoHP: Humanitarian and societal logistics