A Computational Approach to Patient Flow Logistics in Hospitals

Scheduling decisions in hospitals are often taken in a decentralized way. This means that different specialized hospital units decide autonomously on e.g. patient admissions and schedules of shared resources. Decision support in such a setting requires methods and techniques that are different from the majority of existing literature in which centralized models are assumed. The design and analysis of such methods and techniques is the focus of this thesis. Specifically, we develop computational models to provide dynamic decision support for hospital resource management, the prediction of future resource occupancy and the application thereof. Hospital resource management targets the efficient deployment of resources like operating rooms and beds. Allocating resources to hospital units is a major managerial issue as the relationship between resources, utilization and patient flow of different patient groups is complex. The issues are further complicated by the fact that patient arrivals are dynamic and treatment processes are stochastic. Our approach to providing decision support combines techniques from multi-agent systems and computational intelligence (CI). This combination of techniques allows to properly consider the dynamics of the problem while reflecting the distributed decision making practice in hospitals. Multi-agent techniques are used to model multiple hospital care units and their decision policies, multiple patient groups with stochastic treatment processes and uncertain resource availability due to overlapping patient treatment processes. The agent-based model closely resembles the real-world situation. Optimization and learning techniques from CI allow for designing and evaluating improved (adaptive) decision policies for the agent-based model, which can then be implemented easily in hospital practice. In order to gain insight into the functioning of this complex and dynamic problem setting, we developed an agent-based model for the hospital care units with their patients. To assess the applicability of this agent-based model, we developed an extensive simulation. Several experiments demonstrate the functionality of the simulation and show that it is an accurate representation of the real world. The simulation is used to study decision support in resource management and patient admission control. To further improve the quality of decision support, we study the prediction of future hospital resource usage. Using prediction, the future impact of taking a certain decision can be taken into account. In the problem setting at hand for instance, predicting the resource utilization resulting from an admission decision is important to prevent future bottlenecks that may cause the blocking of patient flow and increase patient waiting times. The methods we investigate for the task of prediction are forward simulation and supervised learning using neural networks. In an extensive analysis we study the underlying probability distributions of resource occupancy and investigate, by stochastic techniques, how to obtain accurate and precise prediction outcomes. To optimize resource allocation decisions we consider multiple criteria that are important in the hospital problem setting. We use three conflicting objectives in the optimization: maximal patient throughput, minimal resource costs and minimal usage of back-up capacity. All criteria can be taken into account by finding decision policies that have the best trade-off between the criteria. We derived various decision policies that partly allow for adaptive resource allocations. The design of the policies allows the policies to be easily understandable for hospital experts. Moreover, we present a bed exchange mechanism that enables a realistic implementation of these adaptive policies in practice. In our optimization approach, the parameters of the different decision policies are determined using a multiobjective evolutionary algorithm (MOEA). Specifically, the MOEA optimizes the output of the simulation (i.e. the three optimization criteria) as a function of the policy parameters. Our results on resource management show that the benchmark allocations obtained from a case study are considerably improved by the optimized decision policies. Furthermore, our results show that using adaptive policies can lead to better results and that further improvements may be obtained by integrating prediction into a decision policy

Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Mobile Crowd Sensing in Edge Computing Environment

abstract: The mobile crowdsensing (MCS) applications leverage the user data to derive useful information by data-driven evaluation of innovative user contexts and gathering of information at a high data rate. Such access to context-rich data can potentially enable computationally intensive crowd-sourcing applications such as tracking a missing person or capturing a highlight video of an event. Using snippets and pictures captured from multiple mobile phone cameras with specific contexts can improve the data acquired in such applications. These MCS applications require efficient processing and analysis to generate results in real time. A human user, mobile device and their interactions cause a change in context on the mobile device affecting the quality contextual data that is gathered. Usage of MCS data in real-time mobile applications is challenging due to the complex inter-relationship between: a) availability of context, context is available with the mobile phones and not with the cloud, b) cost of data transfer to remote cloud servers, both in terms of communication time and energy, and c) availability of local computational resources on the mobile phone, computation may lead to rapid battery drain or increased response time. The resource-constrained mobile devices need to offload some of their computation. This thesis proposes ContextAiDe an end-end architecture for data-driven distributed applications aware of human mobile interactions using Edge computing. Edge processing supports real-time applications by reducing communication costs. The goal is to optimize the quality and the cost of acquiring the data using a) modeling and prediction of mobile user contexts, b) efficient strategies of scheduling application tasks on heterogeneous devices including multi-core devices such as GPU c) power-aware scheduling of virtual machine (VM) applications in cloud infrastructure e.g. elastic VMs. ContextAiDe middleware is integrated into the mobile application via Android API. The evaluation consists of overheads and costs analysis in the scenario of ``perpetrator tracking" application on the cloud, fog servers, and mobile devices. LifeMap data sets containing actual sensor data traces from mobile devices are used to simulate the application run for large scale evaluation.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Exploring Deep-Sea Minerals : Systems modeling for an emerging industry and unknown futures

Dyphavsmineraler er et omdiskutert tema. I tilsvar til befolknings- og økonomisk vekst, i tillegg til etterspørselen fra elektrifisering, teknologisk revolusjon og geopolitisk urolighet, er verden forventet å etterspørre en betydelig større, og betydelig mer diversifisert tilgang til kritiske mineraler. Dyphavs mineraler har lenge blitt sett på som en alternativ kilde til veletablerte, men ujevnt geografisk fordelte mineralressurser på land. Samtidig vil uthenting av mineraler i dyphavet kunne bety en industrielt skalert forstyrrelse av et miljø man i varierende grad forstår, og fullt ut kan vurdere konsekvensene av. Denne avhandlingen utforsker og belyser hvordan en mineral-industri på dyphavet vil kunne foregå og bidrar med en basis for kvalifiserte beslutninger og fornuftig politikk-utvikling i den veldig usikre, og veldig tidskritiske sfæren som utgjør dyphavsmineraler. Dette gjøres ved først å fremlegge en flerdimensjonal problem-definisjon for dyphavsmineralindustri, og dernest, med utgangspunkt i stokastisk dynamisk optimering, diskuteres underliggende årsaker for hvorfor dyphavsmineraler kan vokse frem som industri. Avslutningsvis, med utgangspunkt i stokastisk System Dynamikk modellering og simulering av tilfellet «dyphavsmineraler i norsk farvann», vurderer denne avhandlingen hvordan slik aktivitet vil utarte i Norskehavet, og hvor innovasjon og utvikling bør fokuseres for å danne grunnlag for en robust industri i en usikker virkelighet. Denne avhandlingen bidrar med en aggregert, multidimensional, og system orientert problemdefinisjon for dyphavsmineralindustri. Den bidrar modeller og analyse som avkoder effekten av ulike økonomiske faktorer som kan akselerere, eller bremse, en overgang til dyphavs mineraler. Videre bidrar denne avhandlingen med en syntese for mineral industri i Norsk farvann, så vel som et mulig økonomisk rammeverk, og det mest lovende området for innovasjon og utvikling for denne industrien. Avhandlingen konkluderer videre med, på tross av epistemisk usikkerhet, at det er det er tydelige underliggende årsaker for at en mineralindustri på dyphavet vil kunne vokse frem, og at det er rasjonelle argumenter for å understøtte en slik utvikling. Avhandlingen konkluderer videre at dyphavsmineraler kan vise seg både profitabelt eller ikke, og at dette avhenger av innovasjonsstrategi, geopolitiske, miljømessige og klimamessige hensyn, men først og fremst av evnen til å navigere innen epistemisk usikkerhet.Deep-sea mining sparks heated debate. In response to population- and economic growth, as well as to the demands of electrification, technological shifts, and geopolitical turmoil, the world is projected to require a substantially increased and diversified supply of critical minerals. Deep-sea minerals and deep-sea mining have long been considered an alternative resource to established and asymmetrically distributed terrestrial identified mineral resources. However, deep-sea mining entails industrial-scale intervention in a poorly understood environment with equally opaque consequences. This Thesis explores how deep-sea mining may unfold and contributes a basis for qualified decisions and sound policy design in the very uncertain and very urgent realm of deep-sea mining. This is done by first providing a multidimensional problem definition for deep-sea mining. Then, with analysis drawn from stochastic dynamic optimization, underlying reasons deep-sea mining may emerge as an industry are discussed. Finally, based on stochastic System Dynamics modelling and simulation, the Thesis considers how such an industry may unfold on the Norwegian continental shelf and how such an industry may innovate to become robust in an uncertain environment. The Thesis contributes an aggregated, multidimensional, systems-based deep-sea mining problem definition. It contributes models and analysis deciphering the potency of different economic factors that may drive or inhibit a transition towards deep-sea mining. It further contributes a synthesis of the emerging deep-sea mining industry in Norwegian waters, its potential economic framework, and its most auspicious room for innovation and development. The Thesis concludes that deep-sea mining may indeed be encouraged to emerge despite epistemic uncertainty and that there are valid reasons for such emergence. The Thesis further concludes that the emergence of deep-sea mining could prove either profitable or not, depending on innovation policies, geopolitical climatic and environmental priorities, and, most importantly, the qualified navigation of epistemic uncertainty.Doktorgradsavhandlin

Analyst-driven development of an open-source simulation tool to address poor uptake of O.R. in healthcare

Computer simulation studies of health and care problems have been reported extensively in the academic literature, but the one-off research projects typically undertaken have failed to create an enduring legacy of widespread use by healthcare practitioners. Simulation and other modelling tools designed and developed to be used routinely have not fared much better either. Following a review of the literature and a survey of frontline analysts in the UK NHS, we found that one reason for this is because simulation tools have, to date, not been developed with the requirements of the end-user in the heart of the development process. Starting with a thorough needs assessment of NHS based healthcare analysts, this study outlines a set of practical design principles to guide development of simulation software tool for conducting patient flow simulation studies. The overall requirement is that patient flow be modelled over a number of inter-connected points of delivery while capturing the stochastic nature of patient arrivals and hospital length of stay, as well as the dynamic delays to patient discharge and transfer of care between different points of care delivery. In ensuring a cost-free solution that is both versatile and user-friendly, and coded in an increasingly popular language among the envisaged end users, the tool was implemented is the R programming language and software environment, with the user interface implemented in the interactive R-Shiny application. The talk will provide an overview of the project lifecycle including an illustrative example of an empirical simulation study concerning the centralisation of an acute stroke pathway

Artificial Intelligence as an Enabler of Quick and Effective Production Repurposing Manufactur-ing: An Exploratory Review and Future Research Propositions

The outbreak of Covid-19 created disruptions in manufacturing operations. One of the most serious negative impacts is the shortage of critical medical supplies. Manufacturing firms faced pressure from governments to use their manufacturing capacity to repurpose their production for meeting the critical demand for necessary products. For this purpose, recent advancements in technology and artificial intelligence (AI) could act as response solutions to conquer the threats linked with repurposing manufacturing (RM). The study’s purpose is to investigate the significance of AI in RM through a systematic literature review (SLR). This study gathered around 453 articles from the SCOPUS database in the selected research field. Structural Topic Modeling (STM) was utilized to generate emerging research themes from the selected documents on AI in RM. In addition, to study the research trends in the field of AI in RM, a bibliometric analysis was undertaken using the R-package. The findings of the study showed that there is a vast scope for research in this area as the yearly global production of articles in this field is limited. However, it is an evolving field and many research collaborations were identified. The study proposes a comprehensive research framework and propositions for future research development

Innovative Logistics Management under Uncertainty using Markov Model

This paper proposes an innovative uncertainty management using a stochastic model to formulate logistics network starting from order processing, purchasing, inventory management, transportation, and reverse logistics activities. As this activity chain fits well with Markov process, we exploit the very principle to represent not only the transition among various activities, but also the inherent uncertainty that has plagued logistics activities across the board. The logistics network model is thus designed to support logistics management by retrieving and analyzing logistics performance in a timely and cost effective manner. The application of information technology entails this network to become a Markovian information model that is stochastically predictable and flexibly manageable. A case study is presented to highlight the significance of the model. Keywords: Logistics network; Markov process; Risk management; Uncertainty management

PhD Thesis Proposal: Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Resource optimization in health care, manufacturing, and military operations requires the careful choreography of people and equipment to effectively fulfill the responsibilities of the profession. However, resource optimization is a computationally challenging problem, and poorly utilizing resources can have drastic consequences. Within these professions, there are human domain experts who are able to learn from experience to develop strategies, heuristics, and rules-of-thumb to effectively utilize the resources at their disposal. Manually codifying these heuristics within a computational tool is a laborious process and leaves much to be desired. Even with a codified set of heuristics, it is not clear how to best insert an autonomous decision-support system into the human decision-making process. The aim of this thesis is to develop an autonomous computational method for learning domain-expert heuristics from demonstration that can support the human decision-making process. We propose a new framework, called apprenticeship scheduling, which learns and embeds these heuristics within a scalable resource optimization algorithm for real-time decision-support. Our initial investigation, comprised of developing scalable methods for scheduling and studying shared control in human-machine collaborative resource optimization, inspires the development of our apprenticeship scheduling approach. We present a promising, initial prototype for learning heuristics from demonstration and outline a plan for our continuing work

Public Innovation and Digital Transformation

Public innovation and digitalization are reshaping organizations and society in various ways and within multiple fields, as innovations are essential in transforming our world and addressing global sustainability and development challenges. This book addresses the fascinating relationship of these two contemporary topics and explores the role of digital transformation in promoting public innovation. This edited collection includes examples of innovations that emerge suddenly, practices for processing innovations, and the requirements for transformation from innovation to the ""new normal"". Acknowledging that public innovation refers to the development and realization of new and creative ideas that challenge conventional wisdom and disrupt the established practices within a specific context, expert contributions from international scholars explore and illustrate the various activities that are happening in the world of multiple digitalization opportunities. The content covers public administration, technical and business management, human, social, and future sciences, paying attention to the interaction between public and private sectors to utilize digitalization in order to facilitate public innovation. This timely book will be of interest to researchers, academics and students in the fields of technology and innovation management, as well as knowledge management, public service management and administration