Robust Maximal Covering Location Models Considering Partial Coverage

Date

2023-5-18

Author

Köksal, Burak

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Maximal Coverage Location Problem (MCLP) attempts to find a predetermined number of facilities to maximize the number of demand points that can be covered. In MCLP, while all demand points within a critical distance of a facility are completely covered, demand points exterior this region are not covered at all. In Partial MCLP (MCLP-P), another critical distance is introduced which allows coverage between two critical distances, monotonically decreasing with respect to demand points’ distance from facilities. In this thesis, we study MCLP-P under coverage uncertainty. We utilize robust optimization framework and introduce two different strategies to hedge against uncertainty. We propose multiple solution approaches for both strategies. We show interpretation of the proposed robust optimization models from the perspective of game theory using payoff tables. We present the impact of the models and compare the performance of the proposed solution approaches on randomly generated datasets.

Subject Keywords

Maximal coverage location problem, Robust optimization, Decision making under uncertainty, Facility location, Combinatorial optimization

URI

https://hdl.handle.net/11511/104411

Collections

Graduate School of Natural and Applied Sciences, Thesis