Window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators

The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality

Energy aware hybrid flow shop scheduling

Only if humanity acts quickly and resolutely can we limit global warming' conclude more than 25,000 academics with the statement of SCIENTISTS FOR FUTURE. The concern about global warming and the extinction of species has steadily increased in recent years

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling

MULTI-OBJECTIVE DESIGN AUTOMATION FOR RECONFIGURABLE MULTI-PROCESSOR SYSTEMS

Ph.DDOCTOR OF PHILOSOPH

Development of a grid service for multi-objective design optimisation

The emerging grid technology is receiving great attention from researchers and applications that need computational and data capabilities to enhance performance and efficiency. Multi-Objective Design Optimisation (MODO) is computationally and data challenging. The challenges become even more with the emergence of evolutionary computing (EC) techniques which produce multiple solutions in a single simulation run. Other challenges are the complexity in mathematical models and multidisciplinary involvement of experts, thus making MODO collaborative and interactive in nature. These challenges call for a problem solving environment (P SE) that can provide computational and optimisation resources to MODO experts as services. Current PSEs provide only the technical specifications of the services which is used by programmers and do not have service specifications for designers that use the system to support design optimisation as services. There is need for PSEs to have service specification document that describes how the services are provided to the end users. Additionally, providing MODO resources as services enabled designers to share resources that they do not have through service subscription. The aim of this research is to develop specifications and architecture of a grid service for MODO. The specifications provide the service use cases that are used to build MODO services. A service specification document is proposed and this enables service providers to follow a process for providing services to end users. In this research, literature was reviewed and industry survey conducted. This was followed by the design, development, case study and validation. The research studied related PSEs in literature and industry to come up with a service specification document that captures the process for grid service definition. This specification was used to develop a framework for MODO applications. An architecture based on this framework was proposed and implemented as DECGrid (Decision Engineering Centre Grid) prototype. Three real-life case studies were used to validate the prototype. The results obtained compared favourably with the results in literature. Different scenarios for using the services among distributed design experts demonstrated the computational synergy and efficiency in collaboration. The mathematical model building service and optimisation service enabled designers to collaboratively build models using the collaboration service. This helps designers without optimisation knowledge to perform optimisation. The key contributions in this research are the service specifications that support MODO, the framework developed which provides the process for definining the services and the architecture used to implement the framework. The key limitations of the research are the use of only engineering design optimisation case studies and the prototype is not tested in industry.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Minimising energy use and mould growth risk in tropical hospitals

Critical areas in a hospital, such as Intensive Care Units (ICUs) and isolation rooms, are designed to strict health standards. More often than not, these areas operate continuously to maintain designed indoor conditions in order to ensure the safety of patients, making them energy intensive areas. Several attempts have been made to design them to be more energy-efficient. However, cases have emerged in hot and humid countries like Malaysia where combination of poor design, operation and maintenance practices, exacerbated by the humid outdoor conditions especially during night time, have led to occurrences of mould growth in these critical areas. A question arise whether energy efficient design of a critical area can be achieved without incurring a risk of mould growth due to factors like moisture transfer, or continuous part load operation of HVAC systems. The objective of research in this thesis is to investigate the trade-off between optimizing the building and HVAC systems and minimizing the risk of mould growth in hospital buildings located in hot and humid climates. The problem formulation is a single zone isolation room with dimensions based from a real-life isolation room of a district hospital in Malaysia. The design variables, namely HVAC systems and the details of building constructions were selected as input files for energy performance evaluation using EnergyPlus. The output from the simulation will be compared with the selected existing mould growth model during post processing to determine the optimum solution. Simulation and the generation of solutions will be repeated until the most optimum solution is achieved. A binary-encoded Genetic Algorithm (GA) was used as an approach to the minimisation of hospital building energy use. The GA is proven to be effective in performing multi-objective optimisation, since the objective functions for this research are more than one; namely, the minimum annual energy use in the isolation room and the critical indoor surface conditions, such as temperature and relative humidity, below which there would be no mould growth. The research has shown that the normal practice of isolation room design for Malaysian hospitals does not work in minimising energy use and minimising the risk of mould growth and a new design guideline for isolation rooms in Malaysia is recommended. The principal originality of the research will be the application of optimisation methods to investigate the relationship, or trade-off between energy use and the risk of mould growth, particularly for hospital buildings in a hot and humid climate. In this respect, the new knowledge will be on the optimisation procedure and required modelling/analysis components. This combinatorial approach would serve as decision making tool for building and HVAC systems designers in designing more energy-efficient overall environment systems in hospitals, with particular attention to critical areas that are operating continuously