Dr.  ÇOBAN'ın ekte özeti yer alan çalışmasını paylaşacağı sunumu 5 Şubat Çarşamba günü saat 14:00'te Dekanlık Toplantı salonunda gerçekleşecektir.

BETÜL ÇOBAN, Assistant Professor of Industrial Engineering at Abdullah Gül University

Earthquake Operations Management and a Case Study: Optimizing Pre-earthquake Mitigation
Measures to Improve The Efficiency of Evacuation Operations

Abstract
Large-scale earthquakes cause significant human and economic losses, necessitating effective
planning and operational decisions across all disaster stages. Despite the extensive use of Operational
Research (OR) techniques in Earthquake Operations Management (EOM), there remains a need for
widely applicable methodologies and frameworks. This study first provides a comprehensive review
of OR applications in EOM, identifying research gaps and offering a roadmap for future studies. It
categorizes existing research based on disaster stages, applied methodologies, and specific operational
problems, while also examining stakeholder involvement and case study applications. Special
attention is given to realism, comprehensiveness, practicality, and user-friendliness, with the aim of
enhancing the applicability of OR-based solutions.
The second objective of this study is to develop an integrated modeling approach that links mitigation
and response-stage operations. A key focus is the selection of roadway links for structural
strengthening to optimize disaster response effectiveness. Given the importance of connectivity
between affected areas and critical facilities (e.g., hospitals, fire stations, logistics centers), an
efficient and practical optimization method is proposed. A Capacitated Network Strengthening
Problem (CNSP) is formulated as a two-stage stochastic program, incorporating post-disaster resource
availability and infrastructure survivability consideration factors often oversimplified in prior studies.
To solve the CNSP, multi-objective optimization techniques are explored, followed by the Sample
Average Approximation (SAA) method for scenario reduction. Additionally, a Greedy Randomized
Adaptive Search Procedure (GRASP) heuristic is developed to handle large-scale instances efficiently.
The proposed approach is validated using real-life data and adapted literature-based instances.
Computational experiments demonstrate the model’s effectiveness and provide insights for decision-
makers. By integrating mitigation and response planning, this study contributes to improving
earthquake preparedness and response strategies in disaster logistics.