Complexity of robust single facility location problems on networks with uncertain edge lengths
Discrete Applied Mathematics
An exact algorithm for the robust shortest path problem with interval data
Computers and Operations Research
Solution approaches for facility location of medical supplies for large-scale emergencies
Computers and Industrial Engineering
Robust multiperiod portfolio management in the presence of transaction costs
Computers and Operations Research
Computers and Operations Research
Production planning in furniture settings via robust optimization
Computers and Operations Research
Risk-averse two-stage stochastic programming with an application to disaster management
Computers and Operations Research
Robust scheduling on a single machine to minimize total flow time
Computers and Operations Research
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This work locates urgent relief distribution centers (URDCs) on a given set of candidate sites using a robust vertex p-center (RVPC) model. This model addresses uncertain travel times, represented using fixed intervals or ranges instead of probability distributions, between URDCs and affected areas. The objective of locating a predetermined number (p) of URDCs is to minimize worst-case deviation in maximum travel time from the optimal solution. To reduce the complexity of solving the RVPC problem, this work proposes a property that facilitates identification of the worst-case scenario for a given set of URDC locations. Since the problem is NP-hard, a heuristic framework is developed to efficiently obtain robust solutions. Then, a specific implementation of the framework, based on simulated annealing, is developed to conduct computational experiments. Experimental results show that the proposed heuristic is effective and efficient in obtaining robust solutions of interest. This work examines the impact of the degree of data uncertainty on the selected performance measures and the tradeoff between solution quality and robustness. Additionally, this work demonstrates the applicability of the proposed model to natural disasters based on a real-world instance. The result is compared with that obtained by a scenario-based, two-stage stochastic model. This work contributes significantly to the growing body of literature applying robust optimization approaches to emergency logistics.