Managing Operations in Health and Humanitarian Systems Considering Consistency and Uncertainty

This dissertation focuses on various problems motivated by health and humanitarian systems. When handling the problems, we pay attention to practical aspects of the real applications and consider intangible costs as well as predictable and unpredictable changes that may occur in the environment. One of the desires in real world problems is to have solutions that do not deviate too much from period to period, which is defined as consistency in this work. Consistency concern is studied in a variant of a home health care scheduling problem and multiple solution methods are proposed. This work addresses a real-world home health care scheduling problem (HHCSP) faced by a home care agency in the United States. In home health care scheduling, there is a desire to retain consistency with respect to the home health aide servicing each patient; this consistency is referred to as continuity of care. To address this preference for continuity of care, we propose a rolling horizon approach to the scheduling problem and introduce the consistent home health care scheduling problem (Con-HHCSP). We present two different constructive methods to solve HHCSP on a daily basis: an integer programming-based method with approximations and a variant of a petal heuristic; as well as various improvement heuristics such as Large Neighborhood Search (LNS) and swap heuristics. We present adjustments on these methods to address Con-HHCSP, where the goal is to be able to quantify and control the deviation of the new schedule suggested each day from the existing schedule in place, so that some of the existing assignments may be retained in the new schedule that is produced. We discuss the performance and computational efficiency of these methods. Further exploring the consistency desire in inventory planning, we introduce the multi-period Consistent Newsvendor Problem (Con-Nv) where the order quantities are kept consistent over time. Consistency in the decisions has some value due to operational restrictions observed in practice, which is often ignored in favor of finding the optimum solution that maximizes the tangible benefits. However, not planning for the infeasibilities that may arise due to inconsistency or not considering the intangible benefits of being consistent would result in suboptimal solutions. In this work, we present and compare different approaches for keeping the order quantities consistent over multiple periods in a multi-period Newsvendor problem. We analyze the costs and benefits of consistency, namely, its impact on the operational costs, as well as on decreasing the supply risk. We also highlight the risk of demand forecast inaccuracy in inventory planning and its impact on consistency approaches. Finally, we address Endogenous Network Restoration under Uncertainty (ENRU), where the edges are disrupted and the connections between the nodes are lost, and the goal is to restore some of the edges to connect all nodes to the source node. Motivated by post-disaster debris clearance operations, the restoration activities are endogenous such that the current restoration decision depends on previous restoration decisions and actions, and the restoration times are stochastic that are revealed as the network is explored. We study ENRU, incorporating both endogeneity and uncertainty into network restoration decisions. We analyze the structural properties of ENRU and present useful observations to inform solution approaches for this and related problems. We model ENRU as a Partially Observable Markov Decision Process (POMDP), and propose alternative solution methods such as Mixed Integer Programming (MIP) model, Monte Carlo Search Tree (MCST) based heuristic, and a greedy heuristic. We compare the performances of these methods in an extensive computational study. Our results suggest that a static solution approach performs significantly worse than dynamic approaches where decisions can be updated over time as information about the network is revealed. The dynamic MCST based heuristic provides good quality solutions in a short amount of time for a variety of large size instances.

Date

2021-05-01

Resource Type

Text

Resource Subtype

Dissertation

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