Sustainable development-oriented location-transportation integrated optimization problem regarding multi-period multi-type disaster medical waste during COVID-19 pandemic

Abstract

After the outbreak of COVID-19 pandemic, devising an effective reverse logistics supply chain to clean up disaster medical waste is conducive to controlling and containing novel coronavirus transmission. Thus, the focus of this paper concentrates on multi-period multi-type disaster medical waste location-transportation integrated optimization problem with the concern of sustainability, which is formulated as a tri-objective mixed-integer programming model with the goals of maximizing total economic benefits, minimizing total carbon emissions and total potential social risks. Then, a real-world case from Wuhan using CPLEX solver is used to validate the developed model. Results indicate that constructing DMWTTSs with flexible capacity in different periods is encouraged to handle the sharply increasing disaster medical waste. The multi-period decision model outperforms the single-period one in disaster medical waste supply chains because the former has the capability of handling the uncertainties in the future periods. Increasingly, since the increase of budget doesn’t always work well and social resources are limited, the estimation of minimum budget to obtain optimum overall performance is of great importance.

Two-phase COVID-19 medical waste transport optimisation considering sustainability and infection probability

“…For instance, Kargar et al (2020) highlighted the importance of designing infectious medical waste supply chains to decelerate novel coronavirus spread and constructed a multi-objective linear programming model to minimise total costs and risks and maximise the amount of uncollected medical waste. Cao et al (2022) proposed a multi-objective programming model for the multi-period multi-type COVID-19 medical waste location-transport integrated problem, and intended to maximise total economic benefits, minimise total carbon emissions, and minimise total potential social risks. Valizadeh and Mozafari (2022) formulated a multi-period infectious COVID-19 medical waste collection problem as a single-objective mixed-integer programming model to minimise total costs.…”

“…In this sense, the multi-objective optimisation model seems more reasonable and practical. For instance, Hu and Sheu (2013) , Onan et al (2015) , Cheng et al (2018) , Moreno et al (2018) , and Cao et al (2022) applied multi-objective optimisation method to address post-disaster relief distribution and waste clean-up problems, thus enriching the methodology in the field of humanitarian operations.…”

“…Studies demonstrate that the novel coronavirus is surprisingly hardy because it can survive on an inanimate surface for up to 9 days and transmit between animals for 28 days ( Kampf et al, 2020 ). Yu et al (2020a) and Cao et al (2022) also emphasised that infection probability which should be considered in practical measures was a critical factor in managing COVID-19 medical waste. A real case from the Health Commission of Nanjing indicated the spread of novel coronavirus due to the improper clean-up activities for the contaminated COVID-19 medical waste at Nanjing Lukou International Airport ( http://wjw.nanjing.gov.cn/ ), which further supports the above-mentioned viewpoint.…”

“…It also must be acknowledged that COVID-19 medical waste clean-up activities are not only cost-intensive but benefit-creating for key stakeholders (e.g., logistics service providers) ( Boonmee et al, 2018 ). Subsequently, combined with the insights of Mantzaras and Voudrias (2017) , Tirkolaee et al (2021) , and Cao et al (2022) , the incorporation of social, environmental, and economic sustainability and a two-phase COVID-19 medical waste transport optimisation in the presence of infection probability needs to be further studied.…”

“…Thirdly, most recent studies resolved the multi-objective optimisation programming models by using Pareto optimisation, epsilon-constraint, and goal programming in humanitarian operations ( Onan et al, 2015 ; Govindan et al, 2021 ; Aghababaei et al, 2022 ). Yet, only a fraction of researchers focused on applying the lexicographic optimisation method or linear weighted sum approach ( Hu and Sheu, 2013 ; Yu et al, 2021a; Tirkolaee et al, 2021 ; Aghababaei et al, 2022 ; Cao et al, 2022 ). This paper leverages and extends the insights of the above studies to design a hybrid solution strategy that incorporates the above-mentioned two approaches, thus observing the priority of sustainable objectives and the influences of key factors on results, obtaining a feasible COVID-19 medical waste transport scheme.…”