COVID-19 is a highly infectious respiratory virus that has posed a great threat to the general public. In order to prevent its spread, many governments have enacted stringent measures. Supply chains around the world are facing major disruptions and difficulties adjusting to the new demands and needs of a locked down world. In this paper, we will address the relationship between supply chain operations and the ongoing COVID-19 pandemic. Given current global shortages in essential goods such as medication, we explore the connection between said shortage and supply chain issues, such as the lack of supply chain transparency and resilience, as well as unsustainable just-in-time manufacturing. To mitigate the effects of these issues and protect supply chain operations, we propose some recommendations, such as nationalizing the medical supply chains, adopting a plus one diversification approach, and increasing safety stock. These recommendations are given to not only mitigate current consequences in relation to the ongoing crisis, but also to suggest measures that will provide firms the resiliency needed to weather similar potential shortages in the future.

The concept of chaining, or in more general terms, sparse process structure, has been extremely influential in the process flexibility area, with many large automakers already making this the cornerstone of their business strategies to remain competitive in the industry. The effectiveness of the process strategy, using chains or other sparse structures, has been validated in numerous empirical studies. However, to the best of our knowledge, there have been relatively few concrete analytical results on the performance of such strategies, vis-a-vis the full flexibility system, especially when the system size is large or when the demand and supply are asymmetrical. This paper is an attempt to bridge this gap.We study the problem from two angles: (1) For the symmetrical system where the (mean) demand and plant capacity are balanced and identical, we utilize the concept of a generalized random walk to evaluate the asymptotic performance of the chaining structure in this environment. We show that a simple chaining structure performs surprisingly well for a variety of realistic demand distributions, even when the system size is large. (2) More generally, consider the linear optimization problemand A is a m × n matrix. When b is random, the process flexibility design problem reduces to choosing a small set of variables in S (typically |S| ∼ O(m)) so that E b (Z(b, S)) is as close to E b (Z(b, {1, . . . , n}) as possible. For the more general problem, we identify a class of conditions under which only a sparse flexible structure is needed so that the expected performance is already within optimality of the full flexibility system.Our approach provides a theoretical justification for the widely held maxim: In many practical situations, adding a small number of links to the process flexibility structure can significantly enhance the ability of the system to match (fixed) production capacity with (random) demand.

O ne key factor contributing to emergency department (ED) overcrowding is prolonged waiting time for admission to inpatient wards, also known as ED boarding time. To gain insights into reducing this waiting time, we study operations in the inpatient wards and their interface with the ED. We focus on understanding the effect of inpatient discharge policies and other operational policies on the time-of-day waiting time performance, such as the fraction of patients waiting longer than six hours in the ED before being admitted. Based on an empirical study at a Singaporean hospital, we propose a novel stochastic processing network with the following characteristics to model inpatient operations: (1) A patient's service time in the inpatient wards depends on that patient's admission and discharge times and length of stay. The service times capture a two-time-scale phenomenon and are not independent and identically distributed.(2) Pre-and post-allocation delays model the extra amount of waiting caused by secondary bottlenecks other than bed unavailability, such as nurse shortage.(3) Patients waiting for a bed can overflow to a nonprimary ward when the waiting time reaches a threshold, where the threshold is time dependent. We show, via simulation studies, that our model is able to capture the inpatient flow dynamics at hourly resolution and can evaluate the impact of operational policies on both the daily and time-of-day waiting time performance. In particular, our model predicts that implementing a hypothetical policy can eliminate excessive waiting for those patients who request beds in mornings. This policy incorporates the following components: a discharge distribution with the first discharge peak between 8 a.m. and 9 a.m. and 26% of patients discharging before noon, and constant-mean allocation delays throughout the day. The insights gained from our model can help hospital managers to choose among different policies to implement depending on the choice of objective, such as to reduce the peak waiting in the morning or to reduce daily waiting time statistics.

We develop practical operations research models to support decision making in the design and management of public bicycle-sharing systems. We develop a network flow model with proportionality constraints to estimate the flow of bicycles within the network and the number of trips supported, given an initial allocation of bicycles at each station. We also examine the effectiveness of periodic redistribution of bicycles in the network to support greater flow, and the impact on the number of docks needed. We conduct our numerical analysis using transit data from train operators in Singapore. Given that a substantial proportion of passengers in the train system commute a short distance—more than 16% of passengers alight within two stops from the origin—this forms a latent segment of demand for a bicycle-sharing program. We argue that for a bicycle-sharing system to be most effective for this customer segment, the system must deploy the right number of bicycles at the right places, because this affects the utilization rate of the bicycles and how bicycles circulate within the system. We also identify the appropriate operational environments in which periodic redistribution of bicycles will be most effective for improving system performance.

We examine how a flexible process structure might be designed to allow the production system to better cope with fluctuating supply and demand, and to match supply with demand in a more effective manner.We argue that good flexible process structures are essentially highly connected graphs, and use the concept of graph expansion (a measure of graph connectivity) to achieve various insights into this design problem.A number of design guidelines are well known in the literature. Principles such as "a long chain performs better than many short chains," and that one should "try to equalize the number of plants (resp. products), measured in total units of capacity (resp. mean demand), which each product (resp. plant) in the chain is directly connected to," can now be interpreted from this new angle as a development of different ways in which the underlying network can achieve a good expansion ratio. The same principle extends to other new design guidelines -trying to equalize the number of plants (measured in total number of units) assigned to each pair (or even triplet) of products, or vice versa, can also help the decision maker to arrive at a good process structure.We analyze the worst-case performance of the flexible design problem under a more general setting, which encompasses a large class of objective functions. We show that whenever demand and supply are balanced and symmetrical, the graph expander structure (a highly connected but sparse graph) is within ǫ optimality of the fully flexible system, for all demand scenarios, although it uses a far smaller number of links. Furthermore, the same graph expander structure works uniformly well for all objective functions in this class.Based on this insight, we develop a simple and easy-to-implement heuristic to design flexible process structure. Numerical results show that this heuristic performs well for a variety of numerical examples previously studied in the literature. We also use this idea on a set of real data obtained from a bread delivery system in Singapore, with the goal of minimizing the excess amounts of bread brought to each location.