Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic

A robust optimization problem for drone-based equitable pandemic vaccine distribution with uncertain supply

“… [51] and Mohammadi et al. [47] adopted vaccine coverage to account for both population and locality equity.…”

“…Mohammadi et al. [47] explored the impact of possible disruptions in the vaccine distribution chain via a scenario-based static robust model, which minimizes the total deaths and costs. However, few papers have taken the uncertain supply into account.…”

“…Unlike SP requiring accurate probability information, RO uses an uncertainty set without the probability distribution to characterize the uncertain attributes and focuses on the worst-case optimization. In practice, it is often difficult to obtain sufficient information to support a probability distribution of uncertain parameters [2] , [9] , [14] , [47] , [52] . Hence, this research adopts an RO method to hedge the influence of supply uncertainty on vaccine transportation and administration.…”

Fair-split distribution of multi-dose vaccines with prioritized age groups and dynamic demand: The case study of COVID-19

“…In fact, health policy-makers all around the world came to a global consensus on vaccination strategies to prioritize the vaccination of front-line workers (e.g., healthcare workers) against the disease, then vulnerable populations such as older people and people with weak immune systems. Accordingly, such prioritization decisions should be taken into account when designing a vaccine distribution network ( Mohammadi, Dehghan, Pirayesh, & Dolgui, 2022 ).…”

Bi-Objective Optimization: A Pareto Method with Analytical Solutions

“…Studies in bi-objective optimization constitute a non-trivial part in multi-objective analyses. For instance, Zhou et al [1], Kukkonen and Deb [2], Pinto-Varela et al [3], Lath et al [4], Pereyra et al [5], Garg [6], Futrell et al [7], Hirpa et al [8], Liu et al [9], Wang et al [10], Cheraghalipour et al [11], Ho-Huu et al [12], Yeh [13], Liu et al [14], Nagamanjula and Pethalakshmi [15], Xu et al [16], Diao et al [17], Mohammadi et al [18], Kparib et al [19], Kparib et al [20], Gulben and Orhan [21], Zaninudin and Paputungan [22], and Stutzle and Hoos [23]. Studies proposing multi-objective optimization techniques and solution can be found in Messac [24], Das and Dennis [25], Deb [26], Messac et al [27], Messac and Mattson [28], Kim and Weck [29], Zhang and Li [30], Chinchuluun and Pardalos [31], Mueller-Gritschneder et al [32], Pereyra et al [5], Pérez-Fernández et al [33], Marler and Arora [34], Gunantara [35], Orths et al [36], Collette and Siarry [37], Ehrgott [38], Eskelinen et al [39], Fonseca and Fleming [40], Alaa et al…”