Robust Optimization of the Multi-Objective Multi-Period Location-Routing Problem for Epidemic Logistics System With Uncertain Demand

Vehicle Routing Optimization for Pandemic Containment: A Systematic Review on Applications and Solution Approaches

“…Uncertainty: Considering the uncertainty in VRP operations during a pandemic, the focus was mainly placed on stochastic customer demand [77,[79][80][81]88,99]. Therefore, fuzzy chance-constrained programming [81], scenario-based approaches [77], and Monte Carlo simulation [99] were utilized to address the severe demand uncertainty.…”

“…Due to the critical nature of sustainability in the post-pandemic era, future studies should emphasize the importance of emission reduction or risk minimization. Shen (2009b) [67] Ceselli (2014) [68] Clarke (2017) [93] Akwafuo (2020) [96] EL Midaoui (2020) [94] Gao (2020) [104] Muslu (2020) [103] Pacheco (2020) [91] Calvet (2020) [92] Gai (2021) [72] Gamchi (2021) [80] Huilin (2021) [52] Long (2021) [79] Table 14. Cont.…”

“…The most common economic factor was cost minimization. The often used cost elements were transportation [93,108], fixed and variable outsourcing [105], the use of vehicles [52,87,105], fuel consumption [109], penalty cost for arriving early or late [76], facility opening costs [77], and shortage costs for relief suppliers [79], etc.…”

“…Given that the pandemic caused spikes in the demand of essential medical suppliers and medications, many researchers suggested using temporary healthcare facilities to distribute vaccines or drugs [68,79,94]. This strategy is effective in meeting the largest levels of demand with scarce resources, as citizens can travel by whatever means available to obtain treatments or medications in a temporary facility.…”

“…While multiple depots were used in a range of articles, the concept of no depot was applied in [91], where volunteers (such as non-profit organizations and citizens) helped in the distribution of medical supplies without returning to the original departure location. To deal with uncertain parameters, the fuzzy method [81,85], robust optimization [79], and chance-constrained programming technique [66,67] were used to estimate the unknown demand and travel time. In terms of parameters that vary dynamically over time, Gamchi et al [80] utilized a novel simulation model to estimate the amount of vaccine required to control the communicable disease, while Akwafuo et al [96] applied a three-stage approach to incorporate real-time demand.…”