title: Optimizing Pre-Earthquake Mitigation Measures to Improve the Efficiency of Evacuation Operations
Creators: name: Coban, Betul
Publisher: name: University of Kent
Year: 2022

description: In order to reduce human losses and minimize social and economic disruption caused by large-scale earthquakes, effective planning and operational decisions need to be made by responsible agencies and institutions across all pre- and post-disaster stages. Despite the volume and variety of Earthquake Operations Management (EOM) studies employing Operational Research (OR) methodologies, the development of widely applicable methodologies and frameworks emerges as a key insight in need of greater attention. The first purpose of this dissertation is to highlight and discuss main lines of research involving the use of OR techniques applied specifically to earthquakes disasters. In the light of this purpose, this dissertation reveals the current research gaps in existing OR methodologies in the context of EOM and provides a roadmap for future research by a comprehensive review study. Throughout, we precisely categorize studies based on the disaster stage(s) being dealt with, methodology(ies) applied, and specific planning/operational problem type. We also provide details about the extent of stakeholder involvement and information relating to case studies. Some important considerations are examined relating to realism, comprehensiveness, practicality, and user-friendliness that have been taken from the various problem definitions and solution methodologies described in the literature. Therefore, this dissertation provides important insights on enhancing the realism and applicability of the solution methodologies. This thesis secondly aims at providing an integrated modelling approach that incorporates mitigation and response stage operations. The key issue is to select optimally the roadway links to be strengthened in a road network by considering their effect on the response stage operations. Given the critical importance of connectivity between affected areas and critical response facilities (i.e., hospitals, fire stations, relief logistics centres) for disaster response operations, as well as the need to improve accessibility as a precautionary measure through link strengthening investments, this dissertation is expected to make a significant contribution to the disaster logistics literature by providing an efficient and practical method to optimize these mitigation decisions. A Capacitated Network Strengthening Problem (CNSP), which involves optimizing pre-disaster mitigation decisions to strengthen road network links structurally to maximize the efficiency of post-earthquake evacuation operations, is formulated as a two-stage stochastic program. Existing studies that integrate decision making for mitigation and response stage operations include lack of consideration regarding post-earthquake resource availability (i.e., service capacity of hospitals). We take into account the service capacities of supplier facilities so that people can receive timely and necessary medical care. Existing studies have also used overly simplistic assumptions about infrastructure damage levels, operability/survivability of network links and the effectiveness of protection. In this study, operability basically depends on mitigation efforts, earthquake characteristics, and structure features. Due to the multi-objective structure of the CNSP, multi-objective approaches are first discussed to decide the best approach to solve the model. Second, the Sample Average Approximation (SAA) method is used to reduce the scenario set to a manageable size. The SAA procedure can be applied to solve the stochastic programs with a large number of scenarios, by which good solutions could be provided. Then, a heuristic algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP) is proposed to solve larger instances. By focusing on earthquakes, the necessary input parameters for the proposed model and solution approach are generated in a realistic setting. Computational experiments are conducted based on generated real-life data and instances adapted from the literature, both to demonstrate the use of the methods and to derive insights for decision authorities.