International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Integrative Predictive Support Systems for Hospital’s Resource Planning and Scheduling

RÉSUMÉ: Le système de santé du Canada a du mal à gérer le nombre croissant de patients ayant plusieurs maladies chroniques nécessitant l’accès à des soins de longue durée, et cela, principalement en raison de vieillissement de la population. Cela entraîne notamment de longs délais d’attente pour les patients et une augmentation des frais des soins de santé. Comme les hôpitaux représentent la plus grande part du budget de la santé, ils doivent améliorer leur efficacité opérationnelle en utilisant plus efficacement leurs ressources. En particulier, les hôpitaux qui fournissent aux patients des soins directs et un accès à des ressources coûteuses telles que les chirurgiens, les salles d’opération, les unités de soins intensifs et les salles d’opération, ont de la pression pour gérer leurs ressources efficacement. Les chercheurs en recherche opérationnelle ont largement abordé les problèmes liés à la planification et l’ordonnancement des ressources dans les hôpitaux pendant de nombreuses années. Les modèles analytiques conventionnels visent ainsi à améliorer l’efficacité de la prise de décision de planification des ressources hospitalières à des fins stratégiques (à long terme), tactiques (à moyen terme) et opérationnelles (à court terme). Cependant, ces modèles ont du mal à adresser efficacement la complexité, la variabilité, et l’incertitude inhérentes aux opérations hospitalières, car ils utilisent souvent des distributions statistiques simplistes pour émuler ces opérations. Par conséquent, ils sont sous-optimaux dans des contextes réels d’utilisation. Avec l’accroissement continu des quantités massives de données collectées dans les hôpitaux et les systèmes de santé, ainsi que les progrès dans le domaine de la modélisation prédictive, la communauté de la recherche opérationnelle a maintenant l’occasion de mieux analyser, comprendre et reproduire la complexité, la variabilité et l’incertitude des opérations hospitalières. À cette fin, l’objectif principal de cette thèse est de développer des cadres prédictifs intégrés, capables d’analyser et d’extraire des informations à partir de masses de données afin de mieux éclairer la planification et l’ordonnancement des ressources hospitalières aux niveaux stratégique, tactique, et opérationnel. Au meilleur des connaissances de l’auteur, cette thèse est une des premières à proposer des cadres pour la conception de systèmes prédictifs dans les hôpitaux. Au niveau stratégique et tactique, le premier article (chapitre 4) développe un cadre hybride basé sur l’apprentissage machine et la simulation pour prédire la demande personnalisée des patients au niveau des ressources hospitalières. Le cadre reflète notamment la relation à long terme entre les hôpitaux et les patients ayant des maladies chroniques, couvrant ainsi un horizon à long terme et intégrant le fait que les patients ont besoin, non pas d’une, mais de plusieurs visites à l’hôpital et accès à divers types de ressources. Dans cette thèse, nous proposons une approche novatrice basée sur l’apprentissage profond avec notamment un modèle de réseaux de neurones qui modélise les interactions complexes des patients chroniques avec les ressources hospitalières tout au long de leurs trajectoires de traitement. Cette nouvelle approche propose une série de réseaux de neurones où l’entrée de chaque réseau est définie comme la sortie de prédiction de son précédent. Les modèles proposés sont ainsi capables de prédire le traitement suivant du patient avec une précision (« recall ») allant de 68% à 79%. En plus de prévoir la prochaine étape des traitements des patients, nous proposons aussi une deuxième série de réseaux de neurones qui fournissent le temps prévu pour le prochain traitement. Ces trajectoires temporelles ainsi prédites sont ensuite incorporées dans une simulation à base d’agents capable de prédire la demande personnalisée et agrégée en ressources rares des hôpitaux à moyen et long terme en fonctions des profils des patients à traiter. Nous avons appliqué ce cadre intégratif à des données hospitalières réelles et montrons que le cadre proposé prédit efficacement la demande à moyen et à long terme de ressources rares dans les hôpitaux avec une précision de 77% (trajectoire) et de 64% (délai entre étapes), qui surpasse considérablement à la fois les méthodes traditionnelles de prévision demande et les techniques standard d’apprentissage automatique. Au niveau tactique et opérationnel, l’article présenté au chapitre 5 propose un modèle intégratif pour la prédiction des durées d’intervention chirurgicale personnalisées. Ce cadre est le premier de ce genre, et permet d’incorporer des attributs opérationnels et temporels liés à la planification, en plus d’attributs liés aux patients, aux procédures et aux chirurgiens pour prévoir ainsi la durée des interventions chirurgicales. De plus, ce cadre illustre l’efficacité d’algorithmes d’apprentissage automatique, tels que « Random Forest » et « Support Vector Machine » pour capturer les relations complexes entre les prédicteurs de la durée des interventions chirurgicales. Nous avons appliqué ce cadre à des données hospitalières réelles et constaté une amélioration de 31% de la précision des prédictions par rapport à la pratique. De plus, les résultats montrent que les décisions liées à la planification telles que l’ordonnancement des procédures et l’affectation des blocs ont un impact significatif sur les durées d’intervention chirurgicale. Ce résultat a des implications importantes pour la littérature dédiée à la planification et l’ordonnancement des salles d’opération aux niveaux tactique et opérationnel. Autrement dit, ce résultat implique que la planification optimale des salles d’opération n’est possible que si l’on optimise conjointement la durée et l’ordre des chirurgies. Au niveau opérationnel, l’article présenté au chapitre 6 propose un modèle intégratif pour la prédiction du risque de défaillance opérationnelle, et notamment du risque de temps supplémentaire. En pratique, même le plus précis des outils utilisés ne permet pas de prédire la variabilité des processus hospitaliers avec une précision de 100%. Par conséquent, au niveau opérationnel, il est important d’éviter les décisions qui ont un risque élevé d’échec pouvant ainsi entraîner des conséquences négatives significatives, qui peuvent à leur tour impliquer des coûts supplémentaires, une qualité de soins inférieure, et causer une insatisfaction à la fois des patients et du personnel. Dans cette thèse, nous appliquons des techniques d’apprentissage machine probabiliste au problème des heures supplémentaires en salle d’opération. Plus précisément, nous montrons, en utilisant des données hospitalières réelles, que les algorithmes proposés sont capables de classer les horaires des salles d’opération qui entraînent des heures supplémentaires avec une précision de 88%. La performance des prédictions ainsi calculées est de plus améliorée grâce à l’utilisation de techniques d’étalonnage appliquées aux résultats d’algorithmes d’apprentissage automatique. Le modèle de risque proposé a ainsi des implications significatives à la fois pour la pratique de la gestion les ressources au niveau opérationnel, mais aussi pour la littérature académique. Tout d’abord, le modèle de risque proposé peut facilement être intégré dans les systèmes de planification des salles à l’hôpital afin d’aider les décideurs à éviter des horaires risqués. Deuxièmement, le modèle de risque proposé peut être utilisé conjointement avec les modèles existants d’ordonnancement des salles d’opération pour améliorer la performance opérationnelle des solutions.----------ABSTRACT: Canada’s health care system is struggling to manage the increasing demand of patients with multiple chronic issues who require access to long-term care, primarily due to Canada’s aging population. This has resulted in long patient wait times and increasing healthcare costs. Since hospitals represent the largest share of the healthcare budget, they are required to improve their operational efficiency by making better use of their resources. In particular, hospitals that provide patients direct care and access to expensive resources such as surgeons, operating rooms, ICUs and wards are under scrutiny on whether or not they manage their resources effectively. Operations research scholars have extensively addressed problems related to resource planning and scheduling in hospitals for many years. Conventional analytical models aim to improve the efficiency of decision-making in hospital resource planning at strategic (long-term), tactical (mid-term) and operational (short-term) levels. However, these models suffer from limited ability in effectively capturing the inherent complexity, variability and uncertainty of hospital operations because they often assume crude and simplistic statistical distributions to imitate these operations. Consequently, they are suboptimal in real-life settings. With the massive amount of data gathered in the hospitals and healthcare systems and advances in the field of predictive modeling, the operations research community are now given the opportunity to better analyze, understand and replicate the complexity, variability and uncertainty of hospital operations. To this end, the main objective of this thesis is to develop integrate predictive frameworks that are capable of analyzing and extracting important patterns from large-scale data that better inform hospital resource planning and scheduling systems at the strategic, tactical and operational levels. To the best of the author’s knowledge, this thesis is a pioneer in proposing frameworks for the design of hospital-wide integrative predictive support systems. At the strategic and tactical level, the first article (Chapter 4) develops a hybrid machine learning-simulation framework for predicting personalized patient demand for hospital resources. The framework captures the long-term relationship between hospitals and chronic patients, which spans over a long-term horizon and incorporates the fact that patients will need, not one, but several visits to the hospital and access to various types of resources over a long time period. In this thesis, we propose a novel approach based on deep feedforward neural network model that models the complex interactions of chronic patients with hospital resources during their treatment pathways. The proposed novel approach does so by developing a series of sequential individually trained deep feedforward neural networks, where each network’s input is set as the prediction output of its preceding. The proposed models are capable of predicting patient’s next treatment with an accuracy (measured by “recall”) ranging from 68% to 79%. In addition to predicting the transition of patients between treatments in their clinical pathways, we propose a second series of temporal deep feedforward neural network models that provide the expected receiving time for the next treatment. The trained pathway and temporal predictive models are incorporated into an agent-based simulation which is capable of predicting personalized and aggregated demand for hospitals’ scarce resources for the mid-term and long-term time horizon. We applied the proposed integrative framework to real hospital data and showed that proposed framework effectively predicts mid-term and long-term demand for hospital scarce resources with an accuracy of 77% and 64%, respectively, which dramatically outperforms traditional demand forecasting methods and standard machine learning techniques. At the tactical and operational level, the article proposed in chapter 5 is an integrative predictive model for personalized surgical procedure durations. The framework is the first of its kind to incorporate scheduling-related, operational and temporal attributes in addition to patient specific, procedure specific and surgeon specific attributes to predict surgical procedure durations. Furthermore, the framework illustrates the effectiveness of machine learning algorithms such as Random Forest and Support Vector Machine to capture the complex relationships among the predictors of surgical procedure durations. We applied the proposed framework to real hospital data and found an improvement of 31% in the accuracy of our predictive model compared to its practice benchmark. Furthermore, the results show that scheduling-related decisions such as procedure sequencing and block assignment have a significant impact on surgical procedure durations. This result has significant implications for operating room planning and scheduling literature at both tactical and operational levels. Namely, it indicates that optimal operating room planning is achieved only through joint optimization of surgical duration procedures and schedules. At the operational level, the article presented in chapter 6 proposes an integrative predictive model for operational failure risk assessment. Interestingly, even the most accurate predictive tools used in practice fall short in predicting variability in hospital processes with 100% accuracy. Therefore, at the operational level it is important to avoid decisions that have a high risk of failure which may subsequently result in significant adverse consequences, which, in turn, may incur additional costs, lower quality of care and cause patient and staff dissatisfaction. In this thesis, we apply probabilistic machine learning techniques to the operating room overtime problem. We show that the proposed algorithms are capable of classifying operating room schedules that result in overtime with an accuracy of 88% when applied to real hospital data. The predictive performance is further improved through the use of calibration techniques applied to the output of machine learning algorithms. The proposed risk model has significant implications for practice and operational level resource scheduling literature. First, the proposed risk model can easily be integrated into operating room scheduling systems at the hospital which ultimately assist decision makers in avoiding risky schedules. Second the proposed risk model may be used in conjunction with existing operating room scheduling models to improve the operational performance of commonplace solutions

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"

AFIT School of Engineering Contributions to Air Force Research and Technology. Calendar Year 1972

This report contains abstracts of Master of Science Theses, Doctoral Dissertations, and faculty publications completed during the 1972 calendar year at the School of Engineering, Air Force Institute of Technology, at Wright-Patterson Air Force Base, Ohio

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

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence

Pricing in Network Revenue Management Systems with Reusable Resources

This thesis focuses on the problem of pricing reusable products in the network revenue management setting. In a nutshell, dynamic pricing problem concerns pricing and selling a finite inventory of products within a given time horizon so as to maximize the total revenue. Most of the existing literature studies the setting with perishable products in which the products sold are permanently removed from inventory. In this thesis, we tackle a different and arguably more challenging problem with reusable products wherein the products are returned back to the seller upon serving a customer and can be used to serve another customer. This class of problems finds a broad range of applications including hotel management, cloud computing, workforce management, call center service, and car rental management. In the first chapter of the thesis, we address the pricing of reusable resources with advance reservation when the demand function is known as a function of price and the demand follows a Poisson point process. We demonstrate that a simple static pricing policy is asymptotically optimal when demand and capacity are scaled without bound. The performance of the policy is measured as a ratio with respect to the policy that does not exhibit any blocking. We also show that the static policy becomes optimal at a rate close to 1 over the square root of n, where n is a scaling factor. Simulation results show the asymptotic behavior but additionally, it shows that for small-scaled systems, the static pricing policy performs very well relative to the no-blocking policy. In the second chapter of the thesis, we consider the learning variant of the same problem in which the customer’s response to selling price and the demand distribution are not known a priori. Connecting this problem to multi-armed bandits (MAB), we propose a variant of the upper confidence bounds (UCB) algorithm. The setting is different from literature in that capacity constraints exist and booking profile is dynamically updated. We solve an LP in every period where the UCB estimates guides the right-hand side parameter and outputs a distribution over the finite pricing actions. We demonstrate that for some large scaling factor n, with high probability, the seller will always choose the optimum after the testing phase and will not exhibit any blocking. In the third and final chapter of the thesis, we employ unsupervised learning methods to tackle the pricing policy from a practical point-of-view. In particular, model-free reinforcement learning method is used to implicitly learn the transition dynamics that governs the reward process to maximize revenue. Deep neural networks are used to parametrize the action policy and value function and through a simulated environment. We show that the generated pricing policy, using purely data, achieved good performance with respect to traffic, revenue, and blocking.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155046/1/abernal_1.pd