A bi-objective optimization framework for designing an efficient fuel supply chain network in post-earthquakes

Metaheuristics in the Humanitarian Supply Chain

“…Inventory: Some authors include in the facility location problem the integration of inventories in the aid distribution from the distribution centers to the warehouses considering the shortages and the penalty in the total cost. Rezaei et al [57] develop a bio-objective optimization model to operate a supply chain of car fuel in earthquake areas. The objective function includes the unmet demand penalty and inventory cost that is solved with the Grasshopper Optimization Algorithm.…”

“…Additionally, from the reviewed papers, 44 are bi-or multi-objective and 36 have a single objective (RQ1). Regarding bi-objective and multi-objective, Rezaei et al [57] proposed a bi-objective deterministic model to address the inventory problem in the postdisaster phase for designing a fuel supply chain network. They used MOEA, NSGA-II and MOPSO algorithms to minimize the penalties due to both delayed and unsatisfied fuel demands and the difference between the satisfied demand in different earthquake-affected areas.…”

Dynamic optimization of emergency resource scheduling in a large-scale maritime oil spill accident

“…It is directly observed that the Pareto solutions obtained with the HAA algorithm generally dominate those obtained with the NSGA-II. To further investigate the performance of the two algorithms, three commonly used metrics are adopted as follows: space metric (Ghasemi et al, 2019), mean ideal distance (Ghasemi et al, 2019) and quantity metric (Rezaei et al, 2020). The comparison results in terms of space metric (SM), mean ideal distance (MID), quantity metric (QM) and computation time (CPU) are shown in Table 4.…”

Use of OR in earthquake operations management: A review of the literature and roadmap for future research

“…Note that less than half (17 out of 42) simultaneously consider multiple commodities and multi-modal transport. [168,184,188,189,193,195,197] Multi [178,182,192,198,200,201 Further analysis reveals that nearly all mathematical programing and heuristic studies for relief distribution adopt a multi-objective framework to capture different, possibly conflicting logistics performance indicators. Typical objectives and variations thereof include: minimizing the cost of transporting relief (e.g., [167,181,198,201]), minimizing response time (e.g., [179,182,185,189]), minimizing unmet demand (e.g., [151,170,186,199]), and maximizing route reliability (e.g., [178,190,200,204]).…”