A green delivery-pickup problem for home hemodialysis machines; sharing economy in distributing scarce resources. (February 2020)

• Record Type: Journal Article
• Authors: Asghari, Mohammad; Mirzapour Al-e-hashem, S. Mohammad J.
• Is Part Of: Transportation research. Volume 134(2020)
• Publication Date: 2020-02
• Subjects: Sharing economy -- Delivery and pickup problem -- Green transportation -- Multi-objective optimization -- Self-learning NSGA-II; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2019.11.009
• Languages: English
• ISSNs: 1366-5545

Abstract

Highlights: Integrating the sharing economy concept into the green delivery-pickup problem. System cost and GHG emissions are simultaneously minimized by a bi-objective model. A real case from the healthcare sector shows the applicability of the model. A self-learning NSGA-II is developed to solve the proposed bi-objective model. The positive impact of sharing economy on eco-efficiency is demonstrated. Abstract: In this paper, we address a green delivery-pickup problem for Home Hemodialysis Machines (HHMs) categorized as scarce commodities. The system supplies the HHMs either from the central depot of the company or from the individual owners. Based on the sharing economy concept, the individuals who own the HHM devices can involve in this home health care system and share them with others through the fleet of the company to make money. After delivery of portable HHM devices to the clients (patients), they will be collected, disinfected and reallocated to fulfill the demands of the other customers. Moreover, respecting the environmental concerns, the vehicles' fuel consumption and consequently the GHG emissions are realistically assumed as a function of the vehicles' load, such that the company and especially the individual owners contribute to reducing GHG emissions, in addition to the primary economic motivations. Current research provides a bi-objective mixed-integer linear programming model which seeks minimizing total system cost and total carbon emissions. In order to solve the problem, Torabi and Hassini's (TH) technique is applied and then a multi-objective meta-heuristic algorithm, self-learning non-dominated sorting genetic algorithm (SNSGA-II), is developed for medium- and large-sized problems. Finally, the application of the problem is investigated by a real case study from the healthcare sector. Numerical analyses indicate that the proposed green sharing-enabled model has a meaningful impact on both operational-level logistics determinations as well as the environmental important attainment indicators. As notable savings are guaranteed in terms of total system cost and emission, the proposed model has a great potential to provide the item sharing activities with a proper sustainable solution.