Fatma Sibel Salman

This study provides descriptive findings across 12 major earthquake-prone countries (2000–2024) using Scopus, Espacenet, EM-DAT, INFORM, and World Bank data to evaluate three hypotheses regarding the structural relationships among academic publication-to-patent patterns, earthquake loss profiles (economic vs. human), and multidimensional risk components. Firstly, developed nations (Japan, South Korea, USA) favor applied innovation with high patent-to-publication ratios, whereas developing nations (Türkiye, China, India, Iran) produce high publication volumes but low patent activity, focusing on disaster response rather than patentable mitigation. Secondly, earthquake losses split into two distinct profiles: the economic domain (Japan, New Zealand), characterized by high economic damages and low casualties, and the human vulnerability domain (Türkiye, Iran, China), where weak infrastructure drives high mortality. Thirdly, Principal Component Analysis (PCA) shows that aggregate risk scores mask unique drivers: some nations are driven by physical hazards (Türkiye) or institutional deficits (India, Iran), while their developed counterparts exhibit low vulnerability and strong protective capacities (Japan, New Zealand, USA). We conclude that bridging the global disaster divide requires integrating targeted knowledge application, assessing unique loss dimensions, and addressing nation-specific risk breakdowns.

The distribution of relief supplies to shelters is a critical aspect of post-disaster humanitarian logistics. In major disasters, prepositioned supplies often fall short of meeting all demands. We address the problem of planning vehicle routes from a distribution center to shelters while allocating limited relief supplies. To balance efficiency and equity, we formulate a bi-objective problem: minimizing a Gini-index-based measure of inequity in unsatisfied demand for fair distribution and minimizing total travel time for timely delivery. We propose a Mixed Integer Programming (MIP) model and use the $ε$-constraint method to handle the bi-objective nature. By deriving mathematical properties of the optimal solution, we introduce valid inequalities and design an algorithm for optimal delivery allocations given feasible vehicle routes. A branch-and-price (B&P) algorithm is developed to solve the problem efficiently. Computational tests on realistic datasets from a past earthquake in Van, Turkey, and predicted data for Istanbul's Kartal region show that the B&P algorithm significantly outperforms commercial MIP solvers. Our bi-objective approach reduces aid distribution inequity by 34% without compromising efficiency. Results indicate that when time constraints are very loose or tight, lexicographic optimization prioritizing demand coverage over fairness is effective. For moderately restrictive time constraints, a balanced approach is essential to avoid inequitable outcomes.

Forced displacement crises have become a pressing humanitarian concern. Refugee movements expose individuals to dire living conditions with severe inaccessibility to essential resources. Humanitarian organizations play a vital role in alleviating these hardships through relief aid interventions. This study aims to optimize the fulfillment of recurring needs for geographically dispersed refugee groups en route to safe destinations. Here, capacitated mobile facilities are tasked with delivering relief aid to refugee groups periodically to ensure equitable service frequency. We formulate the problem as a Markov decision process with multinomial state-transition distributions, shaped by external migration pull factors such as safety conditions, road accessibility, and spatial proximity. The objective is to minimize the relocation and replenishment costs of mobile facilities, along with the deprivation costs faced by underserved refugees. We develop an approximate dynamic programming algorithm featuring a novel policy replication routine. To complement this offline method, we introduce a state-dependent variable threshold policy that enables high-quality, real-time relief provision. Using instances inspired by the Syrian refugee crisis, our results demonstrate the substantial value of stochastic modeling, yielding a 25% reduction in expected total costs compared to deterministic baselines and up to 12% savings through coordinated planning among humanitarian actors. The proposed methods remain effective under dispersed and cohesive refugee group dynamics and multi-destination migration scenarios. Furthermore, we uncover high-frequency traversal and service hotspots along migration paths to provide tactical insights for parameter calibration and resource prepositioning. Collectively, our findings offer practical insights for managing ongoing and future refugee migration crises.

In home healthcare service systems, each healthcare service provider (HSP) is assigned a list of patients to be visited at their homes. We focus on generating a daily patient visit plan that selects the patients to be visited according to their priorities and locations, and determines the route of each HSP. Additionally, we address unexpected urgent patients by solving an optimization problem involving all HSPs cooperating when an urgent patient visit request arises. This problem is formulated with multiple objectives in a lexicographic optimization framework. Two approaches have been implemented: a mixed integer programming model solved within a time limit (TL-MIP) and a Greedy Randomized Adaptive Search Procedure followed by Variable Neighborhood Search (GRASP+VNS). These approaches are compared in a case study that considers serving patients, with several performance metrics analyzed through extensive simulation experiments. The results indicate that the heuristic approach (GRASP+VNS) significantly reduces run times (by approximately 85% on the average overall instances) compared to the TL-MIP approach, while providing solutions that are not far from the TL-MIP approach in terms of the total priority of visited patients, the heuristic deviates at most 2% over different types of instances. Centralized planning with cooperation among two or three service providers reduced the total travel time by 30% and 45%, respectively, and decreased the number of postponed visits by 50% compared to the non-cooperation model.

In the aftermath of a sudden catastrophe, first responders (FRs) strive to reach and rescue immobile victims. Simultaneously, civilians use the same roads to evacuate, access medical facilities and shelters, or reunite with their relatives via private vehicles. The escalated traffic congestion can significantly hinder critical FR operations. A proposal from the Türkiye Ministry of Transportation and Infrastructure is to allocate a lane on specific road segments exclusively for FR use, mark them clearly, and precommunicate them publicly. For a successful implementation of this proposal, an FR path should exist from designated entry points to each FR demand point in the network. The reserved FR lanes along these paths will be inaccessible to evacuees, potentially increasing evacuation times. Hence, in this study, we aim to determine a subset of links along which an FR lane should be reserved and analyze the resulting evacuation flow under evacuees’ selfish routing behavior. We introduce this problem as the first responder network design problem (FRNDP) and formulate it as a mixed-integer nonlinear program. To efficiently solve FRNDP, we introduce a novel bilevel nested heuristic, the Graver augmented multiseed algorithm (GAMA) within GAMA, called GAGA. We test GAGA on synthetic graph instances of various sizes as well as scenarios related to a potential Istanbul earthquake. Our comparisons with a state-of-the-art exact algorithm for network design problems demonstrate that GAGA offers a promising alternative approach and highlights the need for further exploration of quantum-inspired computing to tackle complex real-world problems. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue. Funding: S. Tayur and A. Tenneti acknowledge Raytheon BBN (RTX-BBN) for its support through a Carnegie Mellon University-BBN contract as part of a Defense Advanced Research Projects Agency project on quantum-inspired classical computing. A. Karahalios is supported by the National Science Foundation Graduate Research Fellowship Program [Grants DGE1745016, DGE2140739]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0574 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0574 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Less‐than‐truckload (LTL) transportation is a widely used shipping modality within the logistics industry, facilitating the movement of loads insufficient in size to occupy the entirety of a truck's capacity. Drawing on the real‐life problem of a third‐party logistics (3PL) carrier, which navigates an expansive transportation network and unpredictable demand patterns, we propose a centralized planning approach for LTL transportation, taking advantage of consolidation opportunities at cross‐dock hubs to minimize long‐term freight costs while ensuring on‐time delivery. We present an integer linear programming (ILP) formulation to generate a multi‐day plan with known demands and a daily planning problem (DPP) model to be solved daily, emulating the available order information in a real planning scenario. DPP optimizes truck routes and loading plans, incorporating a penalty cost for postponed transportation requests. We devise a novel multi‐stage matheuristic integrating geographical decomposition with tractable integer programming models and column generation‐based heuristics. With this approach, the DPP is solved in fewer than 2 h of run‐time for real problem instances with as high as 1400 transportation orders. We benchmark our results against a baseline heuristic that mirrors the firm's existing planning approach through a multi‐period simulation. Our comparative evaluations reveal a noteworthy 7.8% improvement in long‐term freight costs, concurrently maintaining adherence to the 98% on‐time delivery target.

The software in this repository is developed to solve the First Responder Network Design Problem (FRNDP) using Graver Augmentation Multi-seeded Algorithm (GAMA) and Branch-and-Bound.