Integrated Machine Learning and Optimization Frameworks with Applications in Operations Management

Incorporation of contextual inference in the optimality analysis of operational problems is a canonical characteristic of data-informed decision making that requires interdisciplinary research. In an attempt to achieve individualization in operations management, we design rigorous and yet practical mechanisms that boost efficiency, restrain uncertainty and elevate real-time decision making through integration of ideas from machine learning and operations research literature. In our first study, we investigate the decision of whether to admit a patient to a critical care unit which is a crucial operational problem that has significant influence on both hospital performance and patient outcomes. Hospitals currently lack a methodology to selectively admit patients to these units in a way that patient’s individual health metrics can be incorporated while considering the hospital’s operational constraints. We model the problem as a complex loss queueing network with a stochastic model of how long risk-stratified patients spend time in particular units and how they transition between units. A data-driven optimization methodology then approximates an optimal admission control policy for the network of units. While enforcing low levels of patient blocking, we optimize a monotonic dual-threshold admission policy. Our methodology captures utilization and accessibility in a network model of care pathways while supporting the personalized allocation of scarce care resources to the neediest patients. The interesting benefits of admission thresholds that vary by day of week are also examined. In the second study, we analyze the efficiency of surgical unit operations in the era of big data. The accuracy of surgical case duration predictions is a crucial element in hospital operational performance. We propose a comprehensive methodology that incorporates both structured and unstructured data to generate individualized predictions regarding the overall distribution of surgery durations. Consequently, we investigate methods to incorporate such individualized predictions into operational decision-making. We introduce novel prescriptive models to address optimization under uncertainty in the fundamental surgery appointment scheduling problem by utilizing the multi-dimensional data features available prior to the surgery. Electronic medical records systems provide detailed patient features that enable the prediction of individualized case time distributions; however, existing approaches in this context usually employ only limited, aggregate information, and do not take advantages of these detailed features. We show how the quantile regression forest, can be integrated into three common optimization formulations that capture the stochasticity in addressing this problem, including stochastic optimization, robust optimization and distributionally robust optimization. In the last part of this dissertation, we provide the first study on online learning problems under stochastic constraints that are "soft", i.e., need to be satisfied with high likelihood. Under a Bayesian framework, we propose and analyze a scheme that provides statistical feasibility guarantees throughout the learning horizon, by using posterior Monte Carlo samples to form sampled constraints that generalize the scenario generation approach commonly used in chance-constrained programming. We demonstrate how our scheme can be integrated into Thompson sampling and illustrate it with an application in online advertisement.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145936/1/meisami_1.pd

Supply chain network design under uncertainty and risk

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.We consider the research problem of quantitative support for decision making in supply chain network design (SCND). We first identify the requirements for a comprehensive SCND as (i) a methodology to select uncertainties, (ii) a stochastic optimisation model, and (iii) an appropriate solution algorithm. We propose a process to select a manageable number of uncertainties to be included in a stochastic program for SCND. We develop a comprehensive two-stage stochastic program for SCND that includes uncertainty in demand, currency exchange rates, labour costs, productivity, supplier costs, and transport costs. Also, we consider conditional value at risk (CV@R) to explore the trade-off between risk and return. We use a scenario generator based on moment matching to represent the multivariate uncertainty. The resulting stochastic integer program is computationally challenging and we propose a novel iterative solution algorithm called adaptive scenario refinement (ASR) to process the problem. We describe the rationale underlying ASR, validate it for a set of benchmark problems, and discuss the benefits of the algorithm applied to our SCND problem. Finally, we demonstrate the benefits of the proposed model in a case study and show that multiple sources of uncertainty and risk are important to consider in the SCND. Whereas in the literature most research is on demand uncertainty, our study suggests that exchange rate uncertainty is more important for the choice of optimal supply chain strategies in international production networks. The SCND model and the use of the coherent downside risk measure in the stochastic program are innovative and novel; these and the ASR solution algorithm taken together make contributions to knowledge

Achieving High Reliability and Efficiency in Maintaining Large-Scale Storage Systems through Optimal Resource Provisioning and Data Placement

With the explosive increase in the amount of data being generated by various applications, large-scale distributed and parallel storage systems have become common data storage solutions and been widely deployed and utilized in both industry and academia. While these high performance storage systems significantly accelerate the data storage and retrieval, they also bring some critical issues in system maintenance and management. In this dissertation, I propose three methodologies to address three of these critical issues. First, I develop an optimal resource management and spare provisioning model to minimize the impact brought by component failures and ensure a highly operational experience in maintaining large-scale storage systems. Second, in order to cost-effectively integrate solid-state drives (SSD) into large-scale storage systems, I design a holistic algorithm which can adaptively predict the popularity of data objects by leveraging temporal locality in their access pattern and adjust their placement among solid-state drives and regular hard disk drives so that the data access throughput as well as the storage space efficiency of the large-scale heterogeneous storage systems can be improved. Finally, I propose a new checkpoint placement optimization model which can maximize the computation efficiency of large-scale scientific applications while guarantee the endurance requirements of the SSD-based burst buffer in high performance hierarchical storage systems. All these models and algorithms are validated through extensive evaluation using data collected from deployed large-scale storage systems and the evaluation results demonstrate our models and algorithms can significantly improve the reliability and efficiency of large-scale distributed and parallel storage systems

EUROPEAN CONFERENCE ON QUEUEING THEORY 2016

International audienceThis booklet contains the proceedings of the second European Conference in Queueing Theory (ECQT) that was held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT, Toulouse, France. ECQT is a biannual event where scientists and technicians in queueing theory and related areas get together to promote research, encourage interaction and exchange ideas. The spirit of the conference is to be a queueing event organized from within Europe, but open to participants from all over the world. The technical program of the 2016 edition consisted of 112 presentations organized in 29 sessions covering all trends in queueing theory, including the development of the theory, methodology advances, computational aspects and applications. Another exciting feature of ECQT2016 was the institution of the Takács Award for outstanding PhD thesis on "Queueing Theory and its Applications"

Personalized Data-Driven Learning and Optimization: Theory and Applications to Healthcare

This dissertation is broadly about developing new personalized data-driven learning and optimization methods with theoretical performance guarantees for three important applications in healthcare operations management and medical decision-making. In these research problems, we are dealing with longitudinal settings, where the decision-maker needs to make multi-stage personalized decisions while collecting data in-between stages. In each stage, the decision-maker incorporates the newly observed data in order to update his current system's model or belief, thereby making better decisions next. This new class of data-driven learning and optimization methods indeed learns from data over time so as to make efficient and effective decisions for each individual in real-time under dynamic, uncertain environments. The theoretical contributions lie in the design and analysis of these new predictive and prescriptive learning and optimization methods and proving theoretical performance guarantees for them. The practical contributions are to apply these methods to resolve unmet real-world needs in healthcare operations management and medical decision-making so as to yield managerial and practical insights and new functionality.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167949/1/keyvan_1.pd

Routing and delivery planning: algorithms and system implementation.

Wong Chi Fat.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 107-115).Abstracts in English and Chinese.List of Tables --- p.ixList of Figures --- p.xChapter 1. --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Literature Review --- p.3Chapter 1.2.1 --- Shortest Path Problem --- p.4Chapter 1.2.2 --- Vehicle Routing Problem with Time Windows --- p.6Chapter 1.3 --- Thesis Outline --- p.9Chapter 2. --- Time-varying Shortest Path with Constraints in a 2-level Network --- p.11Chapter 2.1 --- Introduction --- p.11Chapter 2.2 --- Problem Formulation of TCSP --- p.12Chapter 2.3 --- Arbitrary Waiting Time --- p.13Chapter 2.4 --- TCSP in a 2-level Network --- p.15Chapter 2.4.1 --- Problem Formulation of TCSP in a 2-level Network --- p.17Chapter 2.5 --- Algorithms Solving TCSP in a 2-level Network --- p.20Chapter 2.5.1 --- Exact Algorithm --- p.21Chapter 2.5.2 --- Heuristic Algorithm --- p.23Chapter 2.6 --- Concluding Remarks --- p.30Chapter 3. --- Vehicle Routing Problem with Time Windows and Stochastic Travel Times --- p.32Chapter 3.1 --- Introduction --- p.32Chapter 3.2 --- Problem Formulation --- p.34Chapter 3.3 --- General Branch-and-cut Algorithm --- p.42Chapter 3.4 --- Modified Branch-and-cut Algorithm --- p.44Chapter 3.4.1 --- Prefixing --- p.45Chapter 3.4.2 --- Directed Partial Path Inequalities --- p.47Chapter 3.4.3 --- Exponential Smoothing --- p.50Chapter 3.4.4 --- Fast Fathoming --- p.54Chapter 3.4.5 --- Modified Branch-and-cut algorithm --- p.56Chapter 3.5 --- Computational Analysis --- p.57Chapter 3.5.1 --- "Performance of Prefixing, Direct Partial Path Inequalities and Exponential Smoothing" --- p.57Chapter 3.5.2 --- Performance of Fast Fathoming --- p.63Chapter 3.5.3 --- Summary of Computational Analysis --- p.67Chapter 3.6 --- Concluding Remarks --- p.67Chapter 4. --- System Features and Implementation --- p.69Chapter 4.1 --- Introduction --- p.59Chapter 4.2 --- System Features --- p.70Chapter 4.2.1 --- Map-based Interface and Network Model --- p.70Chapter 4.2.2 --- Database Management and Query --- p.73Chapter 4.3 --- Decision Support Tools --- p.75Chapter 4.3.1 --- Route Finding --- p.75Chapter 4.3.2 --- Delivery Planning --- p.77Chapter 4.4 --- System Implementation --- p.80Chapter 4.5 --- Further Development --- p.82Chapter 5. --- Vehicle Routing Software SurveyChapter 5.1 --- Introduction --- p.83Chapter 5.2 --- Essential Features in CVRS Nowadays --- p.84Chapter 5.2.1 --- Common Features --- p.34Chapter 5.2.2 --- Advanced Features --- p.90Chapter 5.3 --- Concluding Remarks --- p.94Chapter 6. --- Summary & Future Work --- p.97Appendix A --- p.101Appendix B --- p.104Bibliography --- p.10

Simulation and analysis of adaptive routing and flow control in wide area communication networks

This thesis presents the development of new simulation and analytic models for the performance analysis of wide area communication networks. The models are used to analyse adaptive routing and flow control in fully connected circuit switched and sparsely connected packet switched networks. In particular the performance of routing algorithms derived from the L(_R-I) linear learning automata model are assessed for both types of network. A novel architecture using the INMOS Transputer is constructed for simulation of both circuit and packet switched networks in a loosely coupled multi- microprocessor environment. The network topology is mapped onto an identically configured array of processing centres to overcome the processing bottleneck of conventional Von Neumann architecture machines. Previous analytic work in circuit switched work is extended to include both asymmetrical networks and adaptive routing policies. In the analysis of packet switched networks analytic models of adaptive routing and flow control are integrated to produce a powerful, integrated environment for performance analysis The work concludes that routing algorithms based on linear learning automata have significant potential in both fully connected circuit switched networks and sparsely connected packet switched networks

Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book