Dr. Reut Noham.
Researcher at the Department of Industrial Engineering, Faculty of Engineering, Tel Aviv University
About Me
I am a faculty member and the head of the Lab for Healthcare and Non-for-Profit Operations at the Department of Industrial Engineering at Tel-Aviv University. I was a post-doctoral fellow at the Department of Industrial Engineering and Management Sciences at Northwestern University and received my Ph.D. degree in Industrial Engineering from Tel-Aviv University in 2019. My research interests include supply chain management and logistics with a focus on humanitarian supply chains, healthcare systems, and non-profit optimization. I employ Analytics and OR tools to model and analyze problems and solution methods to improve the quality of life in an uncertain world. At the Lab for Healthcare and Non-for-Profit Operations, we develop innovative and implementable models for dynamic decision-making in collaboration with policymakers and practitioners from the public sector. I am committed to advancing innovative scientific solutions while providing a platform for engaging policymakers and the public with our cutting-edge research.
Latest Publication
Dual mode scheduling in volunteer management
Escallon-Barrios, Mariana, Reut Noham, Karen Smilowitz. (2023), Socio-Economic Planning Sciences.
Nonprofit organizations have adopted online scheduling platforms that give autonomy to volunteers in the scheduling process. However, this strategy can create imbalances in task coverage, often requiring staff to fill the gaps. The aim of this study is to develop scheduling strategies to create a balanced schedule that effectively combines workforce types (paid staff and volunteers) while keeping volunteers engaged. This is achieved by accounting for volunteers’ responses to changes in scheduling options. We develop an optimization model that recognizes volunteers’ scheduling responses and utilizes these responses to design policies aimed at achieving a balanced coverage across time slots. This involves reducing over-covered and under-covered time slots over the planning horizon. By understanding the preferences of volunteers, organizations can modify their current policies to better match supply with demand keeping their volunteers engaged. We provide an implementable scheduling strategy combining staff assignment and volunteers’ autonomy in scheduling choices. Case study results show an improvement compared to current scheduling policies. Volunteers’ satisfaction increases, resulting in a long-term impact on the organizations and the communities they serve.
Recent projects
Enhancing Humanitarian Supply Chain Responsiveness through Lateral Transshipments: A Novel Modeling Approach
Reut Noham, Ohad Eisenhandler
Lateral transshipments allow facilities that are at the same echelon in the supply chain to share inventory. While extensively studied in commercial supply chains for their ability to enhance responsiveness, they have recently gained some attention in humanitarian logistics as well. In this paper, we explore the potential benefits of incorporating lateral transshipments within humanitarian operations, suggesting an optimization framework that is more tailored to the unique challenges of humanitarian supply chains. Specifically, we consider the problem of determining how to pre-position relief supplies in a set of facilities before any disaster occurs, under the assumption that transshipments may be performed at the post-disaster phase. The setting we consider involves a discrete set of disaster scenarios and a budget constraint. We show that "traditional" modeling approaches, such as maximizing expected social welfare, may lead to counter-intuitive and risk-insensitive solutions. As an alternative, we propose a modeling approach that has not been applied so far in the literature, to handle the well-known effectiveness-equity trade-off under uncertainty, while also accounting for efficiency. This approach aims to maximize two conflicting goals: the total social welfare across all scenarios, on the one hand, and the alignment of social welfare in each scenario with its associated probability, on the other hand. We explore how these dual considerations, common in scenario-based planning and particularly in humanitarian operations, introduce an additional, higher-level layer to the trade-off between effectiveness and equity. Through a series of numerical experiments, we compare the outcomes of these modeling approaches; demonstrate how transshipments may affect pre-disaster inventory decisions and make disaster response more effective and equitable; and highlight the characteristics of cases in which transshipments may prove to be especially beneficial.
Facilitating Rapid and Cost-Effective Diagnosis Using a Data-Driven Approach
Yifat Alcalay, David Hagin, Daphna Paran, Reut Noham
Establishing a clinical differential diagnosis (DD) is a systematic process utilized in medical practice to reach the most reasonable diagnosis based on patients’ signs and symptoms. This process involves the creation of a list of potential conditions to explain the patient’s symptoms, followed by laboratory testing to either confirm or eliminate these conditions. However, ordering the correct diagnostic tests is sometimes challenging, with evidence suggesting a tendency of physicians for significant over-testing. There are several reasons for over-testing, including lack of knowledge, defensive medicine and addressing patient’s expectations. Independent of the reason, the end results of over-testing are higher costs and increased laboratory workload, which in turn prolongs tests’ turnaround time and could thereby delay diagnosis. In addition, ordering irrelevant tests often leads to false-positive outcomes or irrelevant findings, which can lead to further unnecessary investigations, some could include invasive testing and impose significant risk to the patient. There is therefore a critical need to improve test selection in an educational and non-critic way, that would invite physician collaboration, reduce costs and improve patient care. In this study we employ eXplainable Artificial Intelligence (XAI) and develop data-driven tools and methods to improve cost-effectiveness and accuracy of ordered diagnostic tests. If successfully developed and implemented would reduce costs by ordering only the correct diagnostic tests; reduce laboratory workload, thereby shortening the turnaround time; and prevent unnecessary further testing due to unexpected and unintended test results.
Multi-Session Appointment Scheduling with Heterogeneous Clients
Reut Noham, Karen Smilowitz
Clients seeking paramedical therapies and rehabilitation services generally require frequent appointments over an extended period. Motivated by an early intervention program that provides therapeutic services to infants and toddlers with developmental delays and disabilities, we study scheduling policies that are designed to meet the needs of heterogeneous clients and the operational considerations of the providers. The clients can be heterogeneous in many dimensions: availability and preferences over time, length of service needed, and urgency of need. We aim to better understand how the different ways a provider may prioritize these factors influence scheduling decisions. To do so, the problem of assigning clients to available days and time slots of the service provider is described as a Markov Decision Process. Clients are assigned as requests arrive when only probabilistic knowledge of future clients is known. Given the characteristic of the client and the availability of the provider, our model determines which client requests (specifying day and slot) the service provider should accept in line with a specified prioritization of the provider. We characterize the structural properties of optimal scheduling decisions under idealized conditions. We then use these properties to develop a heuristic for general cases. We evaluate the performance of this heuristic relative to intuitive rule-of-thumb heuristics. Ultimately, we show that dynamic scheduling policies can decrease the number of rejected requests and improve health outcomes while maintaining high utilization of service providers.
Unveiling of Motivational Drivers for Household Adjustment for Earthquakes Toward Equity-driven Emergency Relief Supply Chains - A Cross-Disciplinary Study
Reut Noham, Moran Bodas
Despite efforts by civil defense organizations to promote preparedness behaviors, levels of households' adjustment for earthquake remain insufficiently low. The challenge in achieving higher levels of public readiness may be rooted in suboptimal understanding of human motivation to engage in preparedness behavior. This is further complexed in contexts in which population are being frequently exposed to emergencies, such as in Israel. The proposed study has two main objectives. First, to identify and describe motivators for household adjustment behavior for earthquakes and their socio-demographic correlates, in particular socio-economic status (SES). Second, to develop a mathematical model that integrates these motivators with decisions regarding supply allocation of relief/aid, aimed at increasing cooperation and enhancing emergency relief operations. This will be achieved through a multi-disciplinary approach looking at both social psychology and operations research and analytics. The research will include a preliminary stage of a cross-sectional study exploring the efficacy of different incentives as motivators of households' adjustment for earthquakes. It will then proceed to develop a mathematical model for optimizing inventory and incentive allocation for better earthquake preparedness. The research will consolidate the output of both aspects into an applicable model for effective and equitable public policy for earthquake preparedness.