Researcher:
Salman, Fatma Sibel

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Fatma Sibel

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Salman

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Salman, Fatma Sibel

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Now showing 1 - 10 of 64
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    Publication
    Relief item inventory planning under centralized and decentralized bilateral cooperation and uncertain transshipment quantities
    (Elsevier Science Inc, 2024) Coşkun, Abdullah; Department of Industrial Engineering; Department of Industrial Engineering; Salman, Fatma Sibel; Pashapour, Amirreza; College of Engineering; Graduate School of Sciences and Engineering
    Pre-positioning relief inventory ensures timely delivery of in-kind aid after a catastrophe. Tragic disasters like major earthquakes are rare and unpredictable;therefore, stockpiled items may not be used. To avoid overstocking and reduce shortage risk, the cooperation of two humanitarian agencies in supporting each other in case of shortages is suggested in the literature. In this study, we utilize newsvendor-based quantitative models to optimize the pre-disaster stocking decisions of agencies under centralized and decentralized cooperation mechanisms. In the former, both agencies jointly determine their inventory levels to maximize their combined benefits of relief operations, whereas, in the latter, each agency establishes its stocking level in isolation via a game theoretic approach. In both systems, the two agencies agree to transship their excessive items to the other party if needed. In this regard, we investigate the situation where only a portion of the transshipped items, denoted as the reliability factor, can be received and effectively utilized at the destination due to the chaotic nature of the disaster. Considering a deterministic reliability factor, we obtain the singular optimal inventory levels in the centralized system and identify the unique Nash Equilibrium in the decentralized system. Subsequently, we formulate a two-stage stochastic program, considering a random reliability factor for both cooperation systems. The study concludes by offering a range of managerial insights. Our analyses quantify the sub-optimality resulting from decentralized decision-making across diverse parameter settings using the concept of the price of anarchy. The findings highlight that centralized cooperation becomes particularly advisable when the average demand within either agency is high, the transshipment process is secure (i.e., the reliability factor is high), and transshipment costs remain low.
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    Fair and effective vaccine allocation during a pandemic
    (Elsevier Science Ltd, 2024) Erdoğan, Güneş; Yücel, Eda; Department of Industrial Engineering; Department of Industrial Engineering; Kiavash, Parinaz; Salman, Fatma Sibel; Graduate School of Sciences and Engineering; College of Engineering
    This paper presents a novel model for the Vaccine Allocation Problem (VAP), which aims to allocate the available vaccines to population locations over multiple periods during a pandemic. We model the disease progression and the impact of vaccination on the spread of the disease and mortality to minimise total expected mortality and location inequity in terms of mortality ratios under total vaccine supply and hospital and vaccination centre capacity limitations at the locations. The spread of the disease is modelled through an extension of the well -established Susceptible-Infected-Recovered (SIR) epidemiological model that accounts for multiple vaccine doses. The VAP is modelled as a nonlinear mixed -integer programming model and solved to optimality using the Gurobi solver. A set of scenarios with parameters regarding the COVID-19 pandemic in the UK over 12 weeks are constructed using a hypercube experimental design on varying disease spread, vaccine availability, hospital capacity, and vaccination capacity factors. The results indicate the statistical significance of vaccine availability and the parameters regarding the spread of the disease.
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    Optimization models for disaster response operations: a literature review
    (Springer, 2024) Kamyabniya, Afshin; Saure, Antoine; Benichou, Noureddine; Patrick, Jonathan; Department of Industrial Engineering; Department of Industrial Engineering; Salman, Fatma Sibel;  ; College of Engineering;  
    Disaster operations management (DOM) seeks to mitigate the harmful impact of natural disasters on individuals, society, infrastructure, economic activities, and the environment. Due to the increasing number of people affected worldwide, and the increase in weather-related disasters, DOM has become increasingly important. In this survey, we focus on the post-disaster stage of DOM that involves response operations. We review studies that propose optimization models to supporting the following four relief logistics operations: (i) relief items distribution, (ii) location of relief facilities and temporary shelters, (iii) integrated relief items distribution and shelter location, and (iv) transportation of affected population. Optimization models from 127 articles published between 2013 and 2022, focusing on relief logistics operations during natural disasters, are categorized by disaster type and thoroughly analyzed. Each model provides a case study illustrating its application in addressing key relief logistics operations. We also analyse the extent to which these studies address the critical assumptions and methodological gaps identified by Galindo and Batta (Eur J Oper Res 230:201-211, 2013), Caunhye et al. (Socio-econ Plan Sci 46:4-13, 2012), and Kovacs and Moshtari (Eur J Oper Res 276:395-408, 2019) and the neglected research directions noted by the authors of other relevant review papers. Based on our findings, we provide avenues for potential future research. Our analysis shows a slow increase in the total number of papers published until 2018-2019 and a sharp decrease afterwards, the latter most likely as a consequence of the COVID-19 pandemic. More than half of the papers in our selection concern earthquakes while less than ten papers deal with wildfires, cyclones, or tsunamis. The majority of the stochastic optimization models consider uncertainty in the demand and supply of relief items, while some other crucial sources of uncertainty such as funding availability and donations of relief items (e.g., blood products) remain understudied. Furthermore, most of the papers in our selection fail to incorporate key characteristics of disaster relief operations such as its dynamic nature and information updates during the response phase. Finally, a large number of studies use exact commercial software to solve their models, which may not be computationally efficient or practical for large-scale problems, specifically under uncertainty.
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    Online algorithms for ambulance routing in disaster response with time-varying victim conditions
    (Springer, 2024) Shiri, Davood; Akbari, Vahid; Department of Industrial Engineering; Department of Industrial Engineering; Salman, Fatma Sibel;  ; College of Engineering;  
    We present a novel online optimization approach to tackle the ambulance routing problem on a road network, specifically designed to handle uncertainties in travel times, triage levels, required treatment times of victims, and potential changes in victim conditions in post-disaster scenarios. We assume that this information can be learned incrementally online while the ambulances get to the scene. We analyze this problem using the competitive ratio criterion and demonstrate that, when faced with a worst-case instance of this problem, neither deterministic nor randomized online solutions can attain a finite competitive ratio. Subsequently, we present a variety of innovative online heuristics to address this problem which can operate with very low computational running times. We assess the effectiveness of our online solutions by comparing them with each other and with offline solutions derived from complete information. Our analysis involves examining instances from existing literature as well as newly generated large-sized instances. One of our algorithms demonstrates superior performance when compared to the others, achieving experimental competitive ratios that closely approach the optimal ratio of one.
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    Capacitated mobile facility location problem with mobile demand: efficient relief aid provision to en route refugees
    (Pergamon-Elsevier Science Ltd, 2024) Gunnec, Dilek; Yucel, Eda; Department of Industrial Engineering; Department of Industrial Engineering; Pashapour, Amirreza; Salman, Fatma Sibel;  ; Graduate School of Sciences and Engineering; College of Engineering;  
    As a humanity crisis, the tragedy of forced displacement entails relief aid distribution efforts among en route refugees to alleviate their migration hardships. This study aims to assist humanitarian organizations in cost-efficiently optimizing the logistics of capacitated mobile facilities utilized to deliver relief aid to transiting refugees in a multi-period setting. The problem is referred to as the Capacitated Mobile Facility Location Problem with Mobile Demands (CMFLP-MD). In CMFLP-MD, refugee groups follow specific paths, and meanwhile, they receive relief aid at least once every fixed number of consecutive periods, maintaining continuity of service. To this end, the overall costs associated with capacitated mobile facilities, including fixed, service provision, and relocation costs, are minimized. We formulate a mixed integer linear programming (MILP) model and propose two solution methods to solve this complex problem: an accelerated Benders decomposition approach as an exact solution method and a matheuristic algorithm that relies on an enhanced fix-and-optimize agenda. We evaluate our methodologies by designing realistic instances based on the Honduras migration crisis that commenced in 2018. Our numerical results reveal that the accelerated Benders decomposition excels MILP with a 46% run time improvement on average while acquiring solutions at least as good as the MILP across all instances. Moreover, our matheuristic acquires high-quality solutions with a 2.4% average gap compared to best-incumbents rapidly. An in-depth exploration of the solution properties underscores the robustness of our relief distribution plans under varying migration circumstances. Across several metrics, our sensitivity analyses also highlight the managerial advantages of implementing CMFLP-MD solutions.
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    Managing home health-care services with dynamic arrivals during a public health emergency
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024) Araz, Özgür M.; Department of Industrial Engineering; Department of Industrial Engineering; Çınar, Ahmet; Salman, Fatma Sibel; Parçaoğlu, Mert;  ; Graduate School of Sciences and Engineering; College of Engineering;  
    We consider a public health emergency, during which a high number of patients and their varying health conditions necessitate prioritizing patients receiving home health care. Moreover, the dynamic emergence of patients needing urgent care during the day should be handled by rescheduling these patients. In this article, we present a reoptimization framework for this dynamic problem to periodically determine which patients will be visited in which order on each day to maximize the total priority of visited patients and to minimize the overtime for the health-care provider. This optimization framework also aims to minimize total routing time. A mixed-integer programming (MIP) model is formulated and solved at predetermined reoptimization times, to assure that urgent patients are visited within the current day, while visits of others may be postponed, if overtime is not desired or limited. The effectiveness of a schedule is evaluated with respect to several performance metrics, such as the number of patients whose visits are postponed to the next day, waiting time of urgent patients, and required overtime. The MIP-based approach is compared to two practical heuristics that achieve satisfactory performance under a nervous service system by excelling in different criteria. The MIP-based reoptimization approach is demonstrated for a case during the COVID-19 pandemic. We contribute to the home health-care literature by managing dynamic/urgent patient arrivals under a multiperiod setting with prioritized patients, where we optimize different rescheduling objectives via three alternative reoptimization approaches. © 1988-2012 IEEE.
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    An adaptive and diversified vehicle routing approach to reducing the security risk of cash-in-transit operations
    (Wiley, 2017) Bozkaya, Burçin; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Salman, Fatma Sibel; Telciler, Kaan; Faculty Member; Master Student; College of Engineering; Graduate School of Sciences and Engineering; 178838; N/A
    We consider the route optimization problem of transporting valuables in cash-in-transit (CIT) operations. The problem arises as a rich variant of the capacitated vehicle routing problem (CVRP) with time windows and pickup and deliveries. Due to the high-risk nature of this operation (e.g., robberies) we consider a bi-objective function where we attempt to minimize the total transportation cost and the security risk of transporting valuables along the designed routes. For risk minimization, we propose a composite risk measure that is a weighted sum of two risk components: (i) following the same or very similar routes, and (ii) visiting neighborhoods with low socioeconomic status along the routes. We also consider vehicle capacities in terms of monetary value carried as per insurance regulations. We develop an adaptive randomized bi-objective path selection algorithm that uses the composite risk measure in choosing alternative paths between origin-destination pairs over a sequence of days. We solve the rich CVRP approximately for each day with updated costs. We test our solution approach on a data set from a CIT delivery service provider and provide insights on how the routes diversify daily. Our approach generates a spectrum of solutions with costrisk trade-off to support decision making.
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    Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity
    (Elsevier Science Bv, 2017) Department of Industrial Engineering; Department of Industrial Engineering; Department of Industrial Engineering; Akbari, Vahid; Salman, Fatma Sibel; Teaching Faculty; Faculty Member; College of Engineering; College of Engineering; N/A; 178838
    After a natural disaster roads can be damaged or blocked by debris, while bridges and viaducts may collapse. This commonly observed hazard causes some road sections to be closed and may even disconnect the road network. In the immediate disaster response phase work teams are dispatched to open a subset of roads to reconnect the network. Closed roads are traversable only after they are unblocked/cleared by one of the teams. The main objective of this research is to provide an efficient solution method to generate a synchronized work schedule for the road clearing teams. The solution should specify the synchronized routes of each clearing team so that: 1) connectivity of the network is regained, and 2) none of the closed roads are traversed unless their unblocking/clearing procedure is finished. In this study we develop an exact Mixed Integer Programming (MIP) formulation to solve this problem. Furthermore, we propose a matheuristic that is based on an MIP-relaxation and a local search algorithm. We prove that the optimality gap of the relaxation solution is bounded by K times the lower bound obtained from the relaxed model, where K is the number of teams. We show computationally that the matheuristic obtains optimal or near-optimal solutions. (C) 2016 Elsevier B.V. All rights reserved.
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    Publication
    Managing home health-care services with dynamic arrivals during a public health emergency
    (Institute of Electrical and Electronics Engineers (IEEE), 2022) Araz, Ozgur M.; N/A; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Çınar, Ahmet; Salman, Fatma Sibel; Parçaoğlu, Mert; PhD Student; Faculty Member; Master Student; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 178838; N/A
    We consider a public health emergency, during which a high number of patients and their varying health conditions necessitate prioritizing patients receiving home health care. Moreover, the dynamic emergence of patients needing urgent care during the day should be handled by rescheduling these patients. In this article, we present a reoptimization framework for this dynamic problem to periodically determine which patients will be visited in which order on each day to maximize the total priority of visited patients and to minimize the overtime for the health-care provider. This optimization framework also aims to minimize total routing time. A mixed-integer programming (MIP) model is formulated and solved at predetermined reoptimization times, to assure that urgent patients are visited within the current day, while visits of others may be postponed, if overtime is not desired or limited. The effectiveness of a schedule is evaluated with respect to several performance metrics, such as the number of patients whose visits are postponed to the next day, waiting time of urgent patients, and required overtime. The MIP-based approach is compared to two practical heuristics that achieve satisfactory performance under a nervous service system by excelling in different criteria. The MIP-based reoptimization approach is demonstrated for a case during the COVID-19 pandemic. We contribute to the home health-care literature by managing dynamic/urgent patient arrivals under a multiperiod setting with prioritized patients, where we optimize different rescheduling objectives via three alternative reoptimization approaches.
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    Locating disaster response facilities in İstanbul
    (Taylor & Francis, 2011) Görmez, N.; Köksalan, M.; Department of Industrial Engineering; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; College of Engineering; 178838
    We study the problem of locating disaster response and relief facilities in the city of Istanbul, where a massively destructive earthquake is expected to occur in the near future. The Metropolitan Municipality of Istanbul decided to establish facilities to preposition relief aid and execute post-disaster response operations. We propose a two-tier distribution system that utilizes existing public facilities locally in addition to the new facilities that will act as regional supply points. We develop mathematical models to decide on the locations of the new facilities with the objectives of minimizing the average-weighted distance between casualty locations and closest facilities, and opening a small number of facilities, subject to distance limits and backup requirements under regional vulnerability considerations. We analyze the trade-offs between these two objectives under various disaster scenarios and investigate the solutions for several modelling extensions. The results demonstrate that a small number of facilities will be sufficient and their locations are robust to various parameter and modelling changes.