Publications
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.
An indirect prioritization approach to optimizing sample referral networks for HIV early infant diagnosis
Reut Noham, Michal Tzur, Dan Yamin (2022), IISE Transactions, 54(4), 405-420.
Early diagnosis and treatment of newborns with Human Immunodeficiency Virus (HIV) can substantially reduce mortality rates. Polymerase chain reduction technology is desirable for diagnosing HIV-exposed infants and for monitoring the disease progression in older patients. In low- and middle-income countries (LMICs), processing both types of tests requires the use of scarce resources. In this article, we present a supply chain network model for referring/assigning HIV test samples from clinics to labs. These assignments aim to minimize the expected infant mortality from AIDS due to delays in the return of test results. Using queuing theory, we present an analytical framework to evaluate the distribution of the sample waiting times at the testing labs and incorporate it into a mathematical model. The suggested framework takes into consideration the non-stationarity in the availability of reagents and technical staff. Hence, our model provides a method to find an assignment strategy that involves an indirect prioritization of samples that are more likely than others to be positive. We also develop a heuristic to simplify the implementation of an assignment strategy and provide general managerial insights for operating sample referral networks in LMICs with limited resources. Using a case study from Tanzania, we show that the potential improvement is substantial, especially when some labs are utilized almost to their full capacity. Our results apply to other settings in which expensive equipment with volatile availability is used to perform crucial operations, for example, the recent COVID-19 pandemic.
Design and incentive decisions to increase cooperation in humanitarian relief networks
Reut Noham, Michal Tzur (2020), IISE Transactions, 52(12), 1297-1311.
During humanitarian relief operations, designated facilities are established to assist the affected population and distribute relief goods. In settings where the authorities manage the operations, they instruct the population regarding which facility they should visit. However, in times of crises and uncertainty, these instructions are often not followed. In this work, we investigate how the authorities should invest in incentivizing the population to follow their instructions. These decisions need to be combined with those concerning the relief network design. The population’s behavior and level of cooperation are key factors in deciding on the incentive investments. We present a new mathematical model that incorporates decisions regarding which populations to incentivize to follow the local authorities’ instructions. Then, we develop properties that can help the authorities decide on the level of investment in incentives. A numerical study demonstrates that incentives can improve the system’s performance and enable an equitable supply allocation. Furthermore, an investment in a small number of communities is typically sufficient to significantly improve the system’s performance. We also demonstrate that incentives affect relief-network design decisions.
Designing humanitarian supply chains by incorporating actual post-disaster decisions
Reut Noham, Michal Tzur (2018), European Journal of Operational Research, 265(3), 1064-1077.
Existing models for disaster preparedness and response address network design and resource allocation challenges. However, these models typically adopt a global optimization point of view, which may not be attainable since they do not consider the actual decision-making process after a disaster occurs. This process is based mostly on practitioners' knowledge and experience, rules of thumb and the population behavior. In this paper, we develop a new mathematical model that incorporates such practical considerations. The model includes actual post-disaster decisions through a set of “humanitarian constraints”. We then present an efficient optimal solution method to solve small/medium-size instances of the resulting problem and a heuristic algorithm based on the Tabu-search method for large instances of the problem. We test our methods on problems with randomly generated data, as well as real data obtained from the Geophysical Institute of Israel. The results demonstrate that our heuristic performs exceptionally well, and optimal solutions are obtained in almost all cases. More importantly, we show that ignoring the actual decision-making process that occurs at the post-disaster stage results in inferior actual overall solutions. Using the humanitarian constraints improves the entire supply chain performance. Therefore, it is critical to accurately incorporate post-disaster decisions during the pre-disaster planning phase.
The single and multi‐item transshipment problem with fixed transshipment costs
Reut Noham, Michal Tzur (2014), Naval Research Logistics, 61: 637–664.
This article deals with supply chain systems in which lateral transshipments are allowed. For a system with two retailers facing stochastic demand, we relax the assumption of negligible fixed transshipment costs, thus, extending existing results for the single-item case and introducing a new model with multiple items. The goal is to determine optimal transshipment and replenishment policies, such that the total centralized expected profit of both retailers is maximized. For the single-item problem with fixed transshipment costs, we develop optimality conditions, analyze the expected profit function, and identify the optimal solution. We extend our analysis to multiple items with joint fixed transshipment costs, a problem that has not been investigated previously in the literature, and show how the optimality conditions may be extended for any number of items. Due to the complexity involved in solving these conditions, we suggest a simple heuristic based on the single-item results. Finally, we conduct a numerical study that provides managerial insights on the solutions obtained in various settings and demonstrates that the suggested heuristic performs very well.