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- Genre: Doctoral Dissertation
Description
This dissertation addresses access management problems that occur in both emergency and outpatient clinics with the objective of allocating the available resources to improve performance measures by considering the trade-offs. Two main settings are considered for estimating patient willingness-to-wait (WtW) behavior for outpatient appointments with statistical analyses of data: allocation of the limited booking horizon to patients of different priorities by using time windows in an outpatient setting considering patient behavior, and allocation of hospital beds to admitted Emergency Department (ED) patients. For each chapter, a different approach based on the problem context is developed and the performance is analyzed by implementing analytical and simulation models. Real hospital data is used in the analyses to provide evidence that the methodologies introduced are beneficial in addressing real life problems, and real improvements can be achievable by using the policies that are suggested.
This dissertation starts with studying an outpatient clinic context to develop an effective resource allocation mechanism that can improve patient access to clinic appointments. I first start with identifying patient behavior in terms of willingness-to-wait to an outpatient appointment. Two statistical models are developed to estimate patient WtW distribution by using data on booked appointments and appointment requests. Several analyses are conducted on simulated data to observe effectiveness and accuracy of the estimations.
Then, this dissertation introduces a time windows based policy that utilizes patient behavior to improve access by using appointment delay as a lever. The policy improves patient access by allocating the available capacity to the patients from different priorities by dividing the booking horizon into time intervals that can be used by each priority group which strategically delay lower priority patients.
Finally, the patient routing between ED and inpatient units to improve the patient access to hospital beds is studied. The strategy that captures the trade-off between patient safety and quality of care is characterized as a threshold type. Through the simulation experiments developed by real data collected from a hospital, the achievable improvement of implementing such a strategy that considers the safety-quality of care trade-off is illustrated.
This dissertation starts with studying an outpatient clinic context to develop an effective resource allocation mechanism that can improve patient access to clinic appointments. I first start with identifying patient behavior in terms of willingness-to-wait to an outpatient appointment. Two statistical models are developed to estimate patient WtW distribution by using data on booked appointments and appointment requests. Several analyses are conducted on simulated data to observe effectiveness and accuracy of the estimations.
Then, this dissertation introduces a time windows based policy that utilizes patient behavior to improve access by using appointment delay as a lever. The policy improves patient access by allocating the available capacity to the patients from different priorities by dividing the booking horizon into time intervals that can be used by each priority group which strategically delay lower priority patients.
Finally, the patient routing between ED and inpatient units to improve the patient access to hospital beds is studied. The strategy that captures the trade-off between patient safety and quality of care is characterized as a threshold type. Through the simulation experiments developed by real data collected from a hospital, the achievable improvement of implementing such a strategy that considers the safety-quality of care trade-off is illustrated.
ContributorsKilinc, Derya (Author) / Gel, Esma (Thesis advisor) / Pasupathy, Kalyan (Committee member) / Sefair, Jorge (Committee member) / Sir, Mustafa (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2019
Description
To maintain long term success, a manufacturing company should be managed and operated under the guidance of properly designed capacity, production and logistics plans that are formulated in coordination with its manufacturing footprint, so that its managerial goals on both strategic and tactical levels can be fulfilled. In particular, sufficient flexibility and efficiency should be ensured so that future customer demand can be met at a profit. This dissertation is motivated by an automobile manufacturer's mid-term and long-term decision problems, but applies to any multi-plant, multi-product manufacturer with evolving product portfolios and significant fixed and variable production costs. Via introducing the concepts of effective capacity and product-specific flexibility, two mixed integer programming (MIP) models are proposed to help manufacturers shape their mid-term capacity plans and long-term product allocation plans. With fixed tooling flexibility, production and logistics considerations are integrated into a mid-term capacity planning model to develop well-informed and balanced tactical plans, which utilize various capacity adjustment options to coordinate production, inventory, and shipping schedules throughout the planning horizon so that overall operational and capacity adjustment costs are minimized. For long-term product allocation planning, strategic tooling configuration plans that empower the production of multi-generation products at minimal configuration and operational costs are established for all plants throughout the planning horizon considering product-specific commonality and compatibility. New product introductions and demand uncertainty over the planning horizon are incorporated. As a result, potential production sites for each product and corresponding process flexibility are determined. An efficient heuristic method is developed and shown to perform well in solution quality and computational requirements.
ContributorsYao, Xufeng (Author) / Askin, Ronald (Thesis advisor) / Sefair, Jorge (Thesis advisor) / Escobedo, Adolfo (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2021
Description
Project portfolio selection (PPS) is a significant problem faced by most organizations. How to best select the many innovative ideas that a company has developed to deploy in a proper and sustained manner with a balanced allocation of its resources over multiple time periods is one of vital importance to a company's goals. This dissertation details the steps involved in deploying a more intuitive portfolio selection framework that facilitates bringing analysts and management to a consensus on ongoing company efforts and buy into final decisions. A binary integer programming selection model that constructs an efficient frontier allows the evaluation of portfolios on many different criteria and allows decision makers (DM) to bring their experience and insight to the table when making a decision is discussed. A binary fractional integer program provides additional choices by optimizing portfolios on cost-benefit ratios over multiple time periods is also presented. By combining this framework with an `elimination by aspects' model of decision making, DMs evaluate portfolios on various objectives and ensure the selection of a portfolio most in line with their goals. By presenting a modeling framework to easily model a large number of project inter-dependencies and an evolutionary algorithm that is intelligently guided in the search for attractive portfolios by a beam search heuristic, practitioners are given a ready recipe to solve big problem instances to generate attractive project portfolios for their organizations. Finally, this dissertation attempts to address the problem of risk and uncertainty in project portfolio selection. After exploring the selection of portfolios based on trade-offs between a primary benefit and a primary cost, the third important dimension of uncertainty of outcome and the risk a decision maker is willing to take on in their quest to select the best portfolio for their organization is examined.
ContributorsSampath, Siddhartha (Author) / Gel, Esma (Thesis advisor) / Fowler, Jown W (Thesis advisor) / Kempf, Karl G. (Committee member) / Pan, Rong (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2018
Description
This research develops heuristics to manage both mandatory and optional network capacity reductions to better serve the network flows. The main application discussed relates to transportation networks, and flow cost relates to travel cost of users of the network. Temporary mandatory capacity reductions are required by maintenance activities. The objective of managing maintenance activities and the attendant temporary network capacity reductions is to schedule the required segment closures so that all maintenance work can be completed on time, and the total flow cost over the maintenance period is minimized for different types of flows. The goal of optional network capacity reduction is to selectively reduce the capacity of some links to improve the overall efficiency of user-optimized flows, where each traveler takes the route that minimizes the traveler’s trip cost. In this dissertation, both managing mandatory and optional network capacity reductions are addressed with the consideration of network-wide flow diversions due to changed link capacities.
This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule.
Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.
This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule.
Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.
ContributorsPeng, Dening (Author) / Mirchandani, Pitu B. (Thesis advisor) / Sefair, Jorge (Committee member) / Wu, Teresa (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2017
Description
Dividing a geographical region into a collection of smaller territories according to definite planning criteria, known as territorial design, is prevalent in many real-world situations. For instance, in a logistics context, a city is often divided into non-overlapping service regions containing city blocks of customers, where dedicated vehicles are distributed to serve them. Location-allocation models can be used to design these territories by locating facilities and allocating customers or communities to the chosen locations, with the aim of optimizing one or several decision criteria. The first part of the research focuses on a novel location-allocation problem, specifically edge-based districting. It introduces models that partition edges of a road network into service areas satisfying three desirable criteria: contiguity, compactness, and work balance. These models incorporate node-to-edge network distances, making them suitable for logistics applications. This research formally establishes the NP-hardness of the EBD problem. To reduce the solution space, network insights are leveraged to derive non-valid logic cuts, a method that removes integer-feasible but non-optimal solutions. Computational tests show significant benefits, with computational time improvements of over an order of magnitude for large-scale instances. The second part explores the edge-based contiguous p-median (ECpM) problem, which seeks to partition a road network into compact and contiguous territories. Two binary programming models are introduced. The first model uses an exponential number of cut set-based constraints, and it is paired with a separation algorithm that generates only a small subset of these constraints. The second model uses a polynomial number of shortest-path constraints, and it can be solved with off-the-shelf optimization solvers. Computational tests show that the shortest-path formulation outperforms the cut set-based formulation, solving larger instances and achieving up to 52x speedups. Structural insights and connections between the ECpM problem and the EBD problem are also explored. The third part investigates the interconnections between node-based districting (NBD) and edge-based districting problems. From a modeling perspective, it describes a set of steps to transform any EBD instance so that it can be equivalently solved with NBD districting models. In addition, it explores how the network insights developed for the EBD models can be adapted for two node-based models that incorporate node-to-node network distances.
ContributorsKassem, Zeyad Essam Mohamed (Author) / Escobedo, Adolfo (Thesis advisor) / Sefair, Jorge (Committee member) / Iquebal, Ashif (Committee member) / Parker, Nathan (Committee member) / Mirchandani, Pitu (Committee member) / Arizona State University (Publisher)
Created2024
Description
The rank aggregation problem has ubiquitous applications in operations research, artificial intelligence, computational social choice, and various other fields. Generally, rank aggregation is utilized whenever a set of judges (human or non-human) express their preferences over a set of items, and it is necessary to find a consensus ranking that best represents these preferences collectively. Many real-world instances of this problem involve a very large number of items, include ties, and/or contain partial information, which brings a challenge to decision-makers. This work makes several contributions to overcoming these challenges. Most attention on this problem has focused on an NP-hard distance-based variant known as Kemeny aggregation, for which solution approaches with provable guarantees that can handle difficult large-scale instances remain elusive. Firstly, this work introduces exact and approximate methodologies inspired by the social choice foundations of the problem, namely the Condorcet criterion, to decompose the problem. To deal with instances where exact partitioning does not yield many subsets, it proposes Approximate Condorcet Partitioning, which is a scalable solution technique capable of handling large-scale instances while providing provable guarantees. Secondly, this work delves into the rank aggregation problem under the generalized Kendall-tau distance, which contains Kemeny aggregation as a special case. This new problem provides a robust and highly-flexible framework for handling ties. First, it derives exact and heuristic solution methods for the generalized problem. Second, it introduces a novel social choice property that encloses existing variations of the Condorcet criterion as special cases. Thirdly, this work focuses on top-k list aggregation. Top-k lists are a special form of item orderings wherein out of n total items only a small number of them, k, are explicitly ordered. Top-k lists are being increasingly utilized in various fields including recommendation systems, information retrieval, and machine learning. This work introduces exact and inexact methods for consolidating a collection of heterogeneous top- lists. Furthermore, the strength of the proposed exact formulations is analyzed from a polyhedral point of view. Finally, this work identifies the top-100 U.S. universities by consolidating four prominent university rankings to assess the computational implications of this problem.
ContributorsAkbari, Sina (Author) / Escobedo, Adolfo (Thesis advisor) / Byeon, Geunyeong (Committee member) / Sefair, Jorge (Committee member) / Wu, Shin-Yi (Committee member) / Arizona State University (Publisher)
Created2022
Description
In this dissertation, a cyber-physical system called MIDAS (Managing Interacting Demand And Supply) has been developed, where the “supply” refers to the transportation infrastructure including traffic controls while the “demand” refers to its dynamic traffic loads. The strength of MIDAS lies in its ability to proactively control and manage mixed vehicular traffic, having various levels of autonomy, through traffic intersections. Using real-time traffic control algorithms MIDAS minimizes wait times, congestion, and travel times on existing roadways. For traffic engineers, efficient control of complicated traffic movements used at diamond interchanges (DI), which interface streets with freeways, is challenging for normal human driven vehicular traffic, let alone for communicationally-connected vehicles (CVs) due to stochastic demand and uncertainties. This dissertation first develops a proactive traffic control algorithm, MIDAS, using forward-recursion dynamic programming (DP), for scheduling large set of traffic movements of non-connected vehicles and CVs at the DIs, over a finite-time horizon. MIDAS captures measurements from fixed detectors and captures Lagrangian measurements from CVs, to estimate link travel times, arrival times and turning movements. Simulation study shows MIDAS’ outperforms (a) a current optimal state-of-art optimal fixed-cycle time control scheme, and (b) a state-of-art traffic adaptive cycle-free scheme.
Subsequently, this dissertation addresses the challenges of improving the road capacity by platooning fully autonomous vehicles (AVs), resulting in smaller headways and greater road utilization. With the MIDAS AI (Autonomous Intersection) control, an effective platooning strategy is developed, and optimal release sequence of AVs is determined using a new forward-recursive DP that minimizes the time-loss delays of AVs. MIDAS AI evaluates the DP decisions every second and communicates optimal actions to the AVs.
Although MIDAS AI’s exact DP achieves optimal solution in almost real-time compared to other exact algorithms, it suffers from scalability. To address this challenge, the dissertation then develops MIDAS RAIC (Reinforced Autonomous Intersection Control), a deep reinforcement learning based real-time dynamic traffic control system for AVs at an intersection. Simulation results show the proposed deep Q-learning architecture trains MIDAS RAIC to learn a near-optimal policy that minimizes the total cumulative time loss delay and performs nearly as well as the MIDAS AI.
ContributorsPotluri, Viswanath (Author) / Mirchandani, Pitu (Thesis advisor) / Ju, Feng (Committee member) / Zhou, Xuesong (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2023
Description
Assembly lines are low-cost production systems that manufacture similar finished units in large quantities. Manufacturers utilize mixed-model assembly lines to produce customized items that are not identical but share some general features in response to consumer needs. To maintain efficiency, the aim is to find the best feasible option to balance the lines efficiently; allocating each task to a workstation to satisfy all restrictions and fulfill all operational requirements in such a way that the line has the highest performance and maximum throughput. The work to be done at each workstation and line depends on the precise product configuration and is not constant across all models. This research seeks to enhance the subject of assembly line balancing by establishing a model for creating the most efficient assembly system. Several realistic characteristics are included into efficient optimization techniques and mathematical models to provide a more comprehensive model for building assembly systems. This involves analyzing the learning growth by task, employing parallel line designs, and configuring mixed models structure under particular constraints and criteria. This dissertation covers a gap in the literature by utilizing some exact and approximation modeling approaches. These methods are based on mathematical programming techniques, including integer and mixed integer models and heuristics. In this dissertation, heuristic approximations are employed to address problem-solving challenges caused by the problem's combinatorial complexity. This study proposes a model that considers learning curve effects and dynamic demand. This is exemplified in instances of a new assembly line, new employees, introducing new products or simply implementing engineering change orders. To achieve a cost-based optimal solution, an integer mathematical formulation is proposed to minimize the production line's total cost under the impact of learning and demand fulfillment. The research further creates approaches to obtain a comprehensive model in the case of single and mixed models for parallel lines systems. Optimization models and heuristics are developed under various aspects, such as cycle times by line and tooling considerations. Numerous extensions are explored effectively to analyze the cost impact under certain constraints and implications. The implementation results demonstrate that the proposed models and heuristics provide valuable insights.
ContributorsAlhomaidi, Esam (Author) / Askin, Ronald G (Thesis advisor) / Yan, Hao (Committee member) / Iquebal, Ashif (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2023
Description
The stable and efficient operation of the transmission network is fundamental to the power system’s ability to deliver electricity reliably and cheaply. As average temperatures continue to rise, the ability of the transmission network to meet demand is diminished. Higher temperatures lead to congestion by reducing thermal limits of lines while simultaneously reducing generation potential. Furthermore, they contribute to the growing frequency and ferocity of devasting weather events. Due to prohibitive costs and limited real estate for building new lines, it is necessary to consider flexible investment options (e.g., transmission switching, capacity expansion, etc.) to improve the functionality and efficiency of the grid. Increased flexibility, however, requires many discrete choices, rendering fully accurate models intractable. This dissertation derives several classes of structural valid inequalities and employs them to accelerate the solution process for each of the proposed expansion planning problems. The valid inequalities leverage the variability of the cumulative capacity-reactance products of parallel simple paths in networks with flexible topology, such as those found in transmission expansion planning problems. Ongoing changes to the climate and weather will have vastly differing impacts a regional and local scale, yet these effects are difficult to predict. This dissertation models the long-term and short-term uncertainty of rising temperatures and severe weather events on transmission network components in both stochastic and robust mixed-integer linear programming frameworks. It develops a novel test case constructed from publicly available data on the Arizona transmission network. The models and test case are used to test the impacts of climate and weather on regional expansion decisions.
ContributorsSkolfield, Joshua Kyle (Author) / Escobedo, Adolfo R (Thesis advisor) / Sefair, Jorge (Committee member) / Mirchandani, Pitu (Committee member) / Hedman, Mojdeh (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation focuses on three large-scale optimization problems and devising algorithms to solve them. In addition to the societal impact of each problem’s solution, this dissertation contributes to the optimization literature a set of decomposition algorithms for problems whose optimal solution is sparse. These algorithms exploit problem-specific properties and use tailored strategies based on iterative refinement (outer-approximations). The proposed algorithms are not rooted in duality theory, providing an alternative to existing methods based on linear programming relaxations. However, it is possible to embed existing decomposition methods into the proposed framework. These general decomposition principles extend to other combinatorial optimization problems.
The first problem is a route assignment and scheduling problem in which a set of vehicles need to traverse a directed network while maintaining a minimum inter-vehicle distance at any time. This problem is inspired by applications in hazmat logistics and the coordination of autonomous agents. The proposed approach includes realistic features such as continuous-time vehicle scheduling, heterogeneous speeds, minimum and maximum waiting times at any node, among others.
The second problem is a fixed-charge network design, which aims to find a minimum-cost plan to transport a target amount of a commodity between known origins and destinations. In addition to the typical flow decisions, the model chooses the capacity of each arc and selects sources and sinks. The proposed algorithms admit any nondecreasing piecewise linear cost structure. This model is applied to the Carbon Capture and Storage (CCS) problem, which is to design a minimum-cost pipeline network to transport CO2 between industrial sources and geologic reservoirs for long-term storage.
The third problem extends the proposed decomposition framework to a special case of joint chance constraint programming with independent random variables. This model is applied to the probabilistic transportation problem, where demands are assumed stochastic and independent. Using an empirical probability distribution, this problem is formulated as an integer program with the goal of finding a minimum-cost distribution plan that satisfies all the demands with a minimum given probability. The proposed scalable algorithm is based on a concave envelop approximation of the empirical probability function, which is iteratively refined as needed.
The first problem is a route assignment and scheduling problem in which a set of vehicles need to traverse a directed network while maintaining a minimum inter-vehicle distance at any time. This problem is inspired by applications in hazmat logistics and the coordination of autonomous agents. The proposed approach includes realistic features such as continuous-time vehicle scheduling, heterogeneous speeds, minimum and maximum waiting times at any node, among others.
The second problem is a fixed-charge network design, which aims to find a minimum-cost plan to transport a target amount of a commodity between known origins and destinations. In addition to the typical flow decisions, the model chooses the capacity of each arc and selects sources and sinks. The proposed algorithms admit any nondecreasing piecewise linear cost structure. This model is applied to the Carbon Capture and Storage (CCS) problem, which is to design a minimum-cost pipeline network to transport CO2 between industrial sources and geologic reservoirs for long-term storage.
The third problem extends the proposed decomposition framework to a special case of joint chance constraint programming with independent random variables. This model is applied to the probabilistic transportation problem, where demands are assumed stochastic and independent. Using an empirical probability distribution, this problem is formulated as an integer program with the goal of finding a minimum-cost distribution plan that satisfies all the demands with a minimum given probability. The proposed scalable algorithm is based on a concave envelop approximation of the empirical probability function, which is iteratively refined as needed.
ContributorsMatin Moghaddam, Navid (Author) / Sefair, Jorge (Thesis advisor) / Mirchandani, Pitu (Committee member) / Escobedo, Adolfo (Committee member) / Grubesic, Anthony (Committee member) / Arizona State University (Publisher)
Created2020