An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

Optimizing Rehabilitation and Maintenance of Hospitals

Hospitals are one of the core elements of a health care system that provide medical service to the patients. Hospital facility management is a complex issue as it involves the management of several complex systems that have a direct impact on the delivery of health care issues. This research focuses on two vital aspects of hospital facility management, (1) level of service provided by the hospital and (2) technical aspects of mission critical hospital subsystems. This study proposes two models in order to maintain and improve the level of service delivered to the patients. The first model operates at the macro-level and undertakes the Network-level Hospital Rehabilitation Trade off model (NEHIR). The model optimizes the scheduling of rehabilitation works through the use of genetic algorithm optimization engine. The model features through five modules, (1) Database module that stores the hospitals data, (2) Backward Markov chain module that estimates the transition probability matrix, (3) Deterioration prediction module that predict the future condition of the asset, (4) Rehabilitation Cost optimization and (5) Multi-objective rehabilitation schedule optimization that conducts a tradeoff between the modified rehabilitation cost and the number of unserved patients. The second model operates at the micro-level and undertakes the Hospital-level Reliability Centered Maintenance model (HOREM). The model optimizes the maintenance tasks for critical subsystems and optimize the allocation of maintenance budget among the hospital subsystems. HOREM model is consisted of five modules as follows, (1) Reliability Centered Maintenance module that was used to define the components, functions, functional failure, failure modes, failure consequence and maintenance type for subsystems components, (2) fuzzy logic system module for determining the probability of failure of different replacement/restoration intervals, (3) Monte-Carlo simulation module determining the probability of failure of different inspection intervals, (4) Multi-objective maintenance optimization module that tradeoff between the downtime and maintenance costs and (5) Systems Integration optimization module that optimize the top management maintenance budget on hospitals subsystems. Two case studies were considered for verification and validation. The first case study is comprised of four hospitals was used for NEHIR model validation. The results of NEHIR model showed 8% decrease in number of unserved patients and 20% saving in rehabilitation costs. The second case study was one hospital that was used for validating HOREM model. The results of HOREM model showed 17% reduction in maintenance costs compared to traditional methods for the same downtime

Evolutionary Game Theoretic Multi-Objective Optimization Algorithms and Their Applications

Multi-objective optimization problems require more than one objective functions to be optimized simultaneously. They are widely applied in many science fields, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conicting objectives. Most of the real world multi-objective optimization problems are NP-Hard problems. It may be too computationally costly to find an exact solution but sometimes a near optimal solution is sufficient. In these cases, Multi-Objective Evolutionary Algorithms (MOEAs) provide good approximate solutions to problems that cannot be solved easily using other techniques. However Evolutionary Algorithm is not stable due to its random nature, it may produce very different results every time it runs. This dissertation proposes an Evolutionary Game Theory (EGT) framework based algorithm (EGTMOA) that provides optimality and stability at the same time. EGTMOA combines the notion of stability from EGT and optimality from MOEA to form a novel and promising algorithm to solve multi-objective optimization problems. This dissertation studies three different multi-objective optimization applications, Cloud Virtual Machine Placement, Body Sensor Networks, and Multi-Hub Molecular Communication along with their proposed EGTMOA framework based algorithms. Experiment results show that EGTMOAs outperform many well known multi-objective evolutionary algorithms in stability, performance and runtime

Resource allocation optimization problems in the public sector

This dissertation consists of three distinct, although conceptually related, public sector topics: the Transportation Security Agency (TSA), U.S. Customs and Border Patrol (CBP), and the Georgia Trauma Care Network Commission (GTCNC). The topics are unified in their mathematical modeling and mixed-integer programming solution strategies. In Chapter 2, we discuss strategies for solving large-scale integer programs to include column generation and the known heuristic of particle swarm optimization (PSO). In order to solve problems with an exponential number of decision variables, we employ Dantzig-Wolfe decomposition to take advantage of the special subproblem structures encountered in resource allocation problems. In each of the resource allocation problems presented, we concentrate on selecting an optimal portfolio of improvement measures. In most cases, the number of potential portfolios of investment is too large to be expressed explicitly or stored on a computer. We use column generation to effectively solve these problems to optimality, but are hindered by the solution time and large CPU requirement. We explore utilizing multi-swarm particle swarm optimization to solve the decomposition heuristically. We also explore integrating multi-swarm PSO into the column generation framework to solve the pricing problem for entering columns of negative reduced cost. In Chapter 3, we present a TSA problem to allocate security measures across all federally funded airports nationwide. This project establishes a quantitative construct for enterprise risk assessment and optimal resource allocation to achieve the best aviation security. We first analyze and model the various aviation transportation risks and establish their interdependencies. The mixed-integer program determines how best to invest any additional security measures for the best overall risk protection and return on investment. Our analysis involves cascading and inter-dependency modeling of the multi-tier risk taxonomy and overlaying security measurements. The model selects optimal security measure allocations for each airport with the objectives to minimize the probability of false clears, maximize the probability of threat detection, and maximize the risk posture (ability to mitigate risks) in aviation security. The risk assessment and optimal resource allocation construct are generalizable and are applied to the CBP problem. In Chapter 4, we optimize security measure investments to achieve the most cost-effective deterrence and detection capabilities for the CBP. A large-scale resource allocation integer program was successfully modeled that rapidly returns good Pareto optimal results. The model incorporates the utility of each measure, the probability of success, along with multiple objectives. To the best of our knowledge, our work presents the first mathematical model that optimizes security strategies for the CBP and is the first to introduce a utility factor to emphasize deterrence and detection impact. The model accommodates different resources, constraints, and various types of objectives. In Chapter 5, we analyze the emergency trauma network problem first by simulation. The simulation offers a framework of resource allocation for trauma systems and possible ways to evaluate the impact of the investments on the overall performance of the trauma system. The simulation works as an effective proof of concept to demonstrate that improvements to patient well-being can be measured and that alternative solutions can be analyzed. We then explore three different formulations to model the Emergency Trauma Network as a mixed-integer programming model. The first model is a Multi-Region, Multi-Depot, Multi-Trip Vehicle Routing Problem with Time Windows. This is a known expansion of the vehicle routing problem that has been extended to model the Georgia trauma network. We then adapt an Ambulance Routing Problem (ARP) to the previously mentioned VRP. There are no known ARPs of this magnitude/extension of a VRP. One of the primary differences is many ARPs are constructed for disaster scenarios versus day-to-day emergency trauma operations. The new ARP also implements more constraints based on trauma level limitations for patients and hospitals. Lastly, the Resource Allocation ARP is constructed to reflect the investment decisions presented in the simulation.Ph.D

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Simheuristic and learnheuristic algorithms for the temporary-facility location and queuing problem during population treatment or testing events

Epidemic outbreaks, such as the one generated by the coronavirus disease, have raised the need for more efficient healthcare logistics. One of the challenges that many governments have to face in such scenarios is the deployment of temporary medical facilities across a region with the purpose of providing medical services to their citizens. This work tackles this temporary-facility location and queuing problem with the goals of minimizing costs, the expected completion time, population travel and waiting times. The completion time for a facility depends on the numbers assigned to those facilities as well as stochastic arrival times. This work proposes a learnheuristic algorithm to solve the facility location and population assignment problem. Firstly a machine learning algorithm is trained using data from a queuing model (simulation module). The learnheuristic then constructs solutions using the machine learning algorithm to rapidly evaluate decisions in terms of facility completion and population waiting times. The efficiency and quality of the algorithm is demonstrated by comparison with exact and simulation-only (simheuristic) methodologies. A series of experiments are performed which explore the trade offs between solution cost, completion time, population travel and waiting times.Peer ReviewedPostprint (author's final draft

Cost Evaluation and Portfolio Management Optimization for Biopharmaceutical Product Development

The pharmaceutical industry is suffering from declining R&D productivity and yet biopharmaceutical firms have been attracting increasing venture capital investment. Effective R&D portfolio management can deliver above average returns under increasing costs of drug development and the high risk of clinical trial failure. This points to the need for advanced decisional tools that facilitate decision-making in R&D portfolio management by efficiently identifying optimal solutions while accounting for resource constraints such as budgets and uncertainties such as attrition rates. This thesis presents the development of such tools and their application to typical industrial portfolio management scenarios. A drug development lifecycle cost model was designed to simulate the clinical and non-clinical activities in the drug development process from the pre-clinical stage through to market approval. The model was formulated using activity-based object-oriented programming that allows the activity-specific information to be collected and summarized. The model provides the decision-maker with the ability to forecast future cash flows and their distribution across clinical trial, manufacturing, and process development activities. The evaluation model was applied to case studies to analyse the non-clinical budgets needed at each phase of development for process development and manufacturing to ensure a market success each year. These cost benchmarking case studies focused on distinct product categories, namely pharmaceutical, biopharmaceutical, and cell therapy products, under different attrition rates. A stochastic optimization tool was built that extended the drug development lifecycle cost evaluation model and linked it to combinatorial optimization algorithms to support biopharmaceutical portfolio management decision-making. The tool made use of the Monte Carlo simulation technique to capture the impact of uncertainties inherent in the drug development process. Dynamic simulation mechanisms were designed to model the progression of activities and allocation of resources. A bespoke multi-objective evolutionary algorithm was developed to locate optimal portfolio management solutions from a large decision space of possible permutations. The functionality of the tool was demonstrated using case studies with various budget and capacity constraints. Analysis of the optimization results highlighted the cash flow breakdowns across both activity categories and development stages. This work contributed to the effort of providing quantitative support to portfolio management decision-making and illustrated the benefits of combining cost evaluation with portfolio optimization to enhance process understanding and achieve better performance

Hospital resource planning : a case-based application for surgical services of a Colombian hospital

Para la toma de decisiones estratégicas se hace uso de la Planificación de recursos hospitalarios (HRP, por sus siglas en inglés) la cual es importante para permitir una gestión eficiente de los recursos, identificar aquellos que causan cuellos de botella, mejorar el flujo de pacientes, brindar tratamiento oportuno y reducir los costos. Este proyecto propone y desarrolla un aplicativo que implementa un modelo cuantitativo de HRP para el servicio quirúrgico de cirugías electivas en un hospital colombiano mediante el uso de un algoritmo genético con escenarios de demanda estocástica como método de solución. El aplicativo propuesto toma en cuenta el impacto en la programación táctica y operativa de los recursos quirúrgicos para cirugías electivas, este impacto se refleja en la interacción con una herramienta de programación.For strategic decision making, the implementation of Hospital Resource Planning (HRP) is important to allow efficient resource management, identify those resources which are causing bottlenecks, improve patient flow, and provide timely treatment and reduce costs. This project proposes and develops an application that implements a quantitative HRP model for the elective surgical service for a Colombian Hospital by using a Genetic Algorithm with stochastic demand scenarios as solution method. The application takes into account its impact on tactical and operative scheduling of surgical resources for elective surgeries, this impact is reflected in the interaction with a scheduling tool.Ingeniero (a) IndustrialPregrad