A Robust Maximal Covering Location Model Considering Partial Coverage

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 outside 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 study, we explore MCLP-P under coverage uncertainty. We utilize a robust optimization framework and introduce an approach to hedge against uncertainty. We present the model and the solution approaches and compare the performance of the proposed solution approaches on randomly generated datasets.

This research originates from the master's thesis of Burak Köksal, supervised by Prof. Esra Karasakal and Prof. Orhan Karasakal.

Short Bio

Burak Köksal is currently pursuing his PhD. degree at the University of Lorraine, within the Department of Computer Science. He earned his MSc. degree in the Industrial Engineering Department of Middle East Technical University in 2023. His research interests mainly include discrete optimization, decision-making under uncertainty, and supply chain management.

Venue

Middle East Technical University

Department of Industrial Engineering Seminar

Friday, May 10, 2024, 4:00 pm (on Teams)

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