65 research outputs found
Problemas de localização-distribuição de serviços semiobnóxios: aproximações e apoio à decisão
Doutoramento em Gestão IndustrialA presente tese resulta de um trabalho de investigação cujo objectivo se
centrou no problema de localização-distribuição (PLD) que pretende abordar,
de forma integrada, duas actividades logísticas intimamente relacionadas: a
localização de equipamentos e a distribuição de produtos.
O PLD, nomeadamente a sua modelação matemática, tem sido estudado na
literatura, dando origem a diversas aproximações que resultam de diferentes
cenários reais. Importa portanto agrupar as diferentes variantes por forma a
facilitar e potenciar a sua investigação. Após fazer uma revisão e propor uma
taxonomia dos modelos de localização-distribuição, este trabalho foca-se na
resolução de alguns modelos considerados como mais representativos. É feita
assim a análise de dois dos PLDs mais básicos (os problema capacitados com
procura nos nós e nos arcos), sendo apresentadas, para ambos, propostas de
resolução. Posteriormente, é abordada a localização-distribuição de serviços
semiobnóxios. Este tipo de serviços, ainda que seja necessário e
indispensável para o público em geral, dada a sua natureza, exerce um efeito
desagradável sobre as comunidades contíguas. Assim, aos critérios
tipicamente utilizados na tomada de decisão sobre a localização destes
serviços (habitualmente a minimização de custo) é necessário adicionar
preocupações que reflectem a manutenção da qualidade de vida das regiões
que sofrem o impacto do resultado da referida decisão.
A abordagem da localização-distribuição de serviços semiobnóxios requer
portanto uma análise multi-objectivo. Esta análise pode ser feita com recurso a
dois métodos distintos: não interactivos e interactivos. Ambos são abordados
nesta tese, com novas propostas, sendo o método interactivo proposto
aplicável a outros problemas de programação inteira mista multi-objectivo.
Por último, é desenvolvida uma ferramenta de apoio à decisão para os
problemas abordados nesta tese, sendo apresentada a metodologia adoptada
e as suas principais funcionalidades. A ferramenta desenvolvida tem grandes
preocupações com a interface de utilizador, visto ser direccionada para
decisores que tipicamente não têm conhecimentos sobre os modelos
matemáticos subjacentes a este tipo de problemas.This thesis main objective is to address the location-routing problem (LRP)
which intends to tackle, using an integrated approach, two highly related
logistics activities: the location of facilities and the distribution of materials.
The LRP, namely its mathematical formulation, has been studied in the
literature, and several approaches have emerged, corresponding to different
real-world scenarios. Therefore, it is important to identify and group the
different LRP variants, in order to segment current research and foster future
studies. After presenting a review and a taxonomy of location-routing models,
the following research focuses on solving some of its variants. Thus, a study of
two of the most basic LRPs (capacitated problems with demand either on the
nodes or on the arcs) is performed, and new approaches are presented.
Afterwards, the location-routing of semi-obnoxious facilities is addressed.
These are facilities that, although providing useful and indispensible services,
given their nature, bring about an undesirable effect to adjacent communities.
Consequently, to the usual objectives when considering their location (cost
minimization), new ones must be added that are able to reflect concerns
regarding the quality of life of the communities impacted by the outcome of
these decisions.
The location-routing of semi-obnoxious facilities therefore requires to be
analysed using multi-objective approaches, which can be of two types: noninteractive
or interactive. Both are discussed and new methods proposed in this
thesis; the proposed interactive method is suitable to other multi-objective
mixed integer programming problems.
Finally, a newly developed decision-support tool to address the LRP is
presented (being the adopted methodology discussed, and its main
functionalities shown). This tool has great concerns regarding the user
interface, as it is directed at decision makers who typically don’t have specific
knowledge of the underlying models of this type of problems
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New variants of variable neighbourhood search for 0-1 mixed integer programming and clustering
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Many real-world optimisation problems are discrete in nature. Although recent rapid developments in computer technologies are steadily increasing the speed of computations, the size of an instance of a hard discrete optimisation problem solvable in prescribed time does not increase linearly with the computer speed. This calls for the development of new solution methodologies for solving larger instances in shorter time. Furthermore, large instances of discrete optimisation problems are normally impossible to solve to optimality within a reasonable computational time/space and can only be tackled with a heuristic approach.
In this thesis the development of so called matheuristics, the heuristics which are based on the mathematical formulation of the problem, is studied and employed within the variable neighbourhood search framework. Some new variants of the variable neighbourhood searchmetaheuristic itself are suggested, which naturally emerge from exploiting the information from the mathematical programming formulation of the problem. However, those variants may also be applied to problems described by the combinatorial formulation. A unifying perspective on modern advances in local search-based metaheuristics, a so called hyper-reactive approach, is also proposed. Two NP-hard discrete optimisation problems are considered: 0-1 mixed integer programming and clustering with application to colour image quantisation. Several new heuristics for 0-1 mixed integer programming problem are developed, based on the principle of variable neighbourhood search. One set of proposed heuristics consists of improvement heuristics, which attempt to find high-quality near-optimal solutions starting from a given feasible solution. Another set consists of constructive heuristics, which attempt to find initial feasible solutions for 0-1 mixed integer programs. Finally, some variable neighbourhood search based clustering techniques are applied for solving the colour image quantisation problem. All new methods presented are compared to other algorithms recommended in literature and a comprehensive performance analysis is provided. Computational results show that the methods proposed either outperform the existing state-of-the-art methods for the problems observed, or provide comparable results.
The theory and algorithms presented in this thesis indicate that hybridisation of the CPLEX MIP solver and the VNS metaheuristic can be very effective for solving large instances of the 0-1 mixed integer programming problem. More generally, the results presented in this thesis suggest that hybridisation of exact (commercial) integer programming solvers and some metaheuristic methods is of high interest and such combinations deserve further practical and theoretical investigation. Results also show that VNS can be successfully applied to solving a colour image quantisation problem.Support from the Mathematical Institute, Serbian Academy of Sciences and Arts, are acknowledged for this research
Machine learning into metaheuristics: A survey and taxonomy of data-driven metaheuristics
During the last years, research in applying machine learning (ML) to design efficient, effective and robust metaheuristics became increasingly popular. Many of those data driven metaheuristics have generated high quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this paper we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies which might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem, low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic which needs further in-depth investigations
New variants of variable neighbourhood search for 0-1 mixed integer programming and clustering
Many real-world optimisation problems are discrete in nature. Although recent rapid developments in computer technologies are steadily increasing the speed of computations, the size of an instance of a hard discrete optimisation problem solvable in prescribed time does not increase linearly with the computer speed. This calls for the development of new solution methodologies for solving larger instances in shorter time. Furthermore, large instances of discrete optimisation problems are normally impossible to solve to optimality within a reasonable computational time/space and can only be tackled with a heuristic approach. In this thesis the development of so called matheuristics, the heuristics which are based on the mathematical formulation of the problem, is studied and employed within the variable neighbourhood search framework. Some new variants of the variable neighbourhood searchmetaheuristic itself are suggested, which naturally emerge from exploiting the information from the mathematical programming formulation of the problem. However, those variants may also be applied to problems described by the combinatorial formulation. A unifying perspective on modern advances in local search-based metaheuristics, a so called hyper-reactive approach, is also proposed. Two NP-hard discrete optimisation problems are considered: 0-1 mixed integer programming and clustering with application to colour image quantisation. Several new heuristics for 0-1 mixed integer programming problem are developed, based on the principle of variable neighbourhood search. One set of proposed heuristics consists of improvement heuristics, which attempt to find high-quality near-optimal solutions starting from a given feasible solution. Another set consists of constructive heuristics, which attempt to find initial feasible solutions for 0-1 mixed integer programs. Finally, some variable neighbourhood search based clustering techniques are applied for solving the colour image quantisation problem. All new methods presented are compared to other algorithms recommended in literature and a comprehensive performance analysis is provided. Computational results show that the methods proposed either outperform the existing state-of-the-art methods for the problems observed, or provide comparable results. The theory and algorithms presented in this thesis indicate that hybridisation of the CPLEX MIP solver and the VNS metaheuristic can be very effective for solving large instances of the 0-1 mixed integer programming problem. More generally, the results presented in this thesis suggest that hybridisation of exact (commercial) integer programming solvers and some metaheuristic methods is of high interest and such combinations deserve further practical and theoretical investigation. Results also show that VNS can be successfully applied to solving a colour image quantisation problem.EThOS - Electronic Theses Online ServiceMathematical Institute, Serbian Academy of Sciences and ArtsGBUnited Kingdo
Modelling the Demand for Long-term Care to Optimise Local Level Planning
Long-term care (LTC) includes the range of health, social and voluntary support services provided to those with chronic illness, physical or mental disability. LTC has been widely studied in the literature, in particular due to concerns surrounding how future demographic shifts may impact the LTC system’s ability to cater to increasing amounts of patients not withstanding what the future cost impact might be. With that said, few studies have attempted to model demand at the local level for the purposes of informing local service delivery and organisation. Many developing countries with mature and developed systems of LTC in place are under pressure to reduce health care spend, whilst delivering greater value for money. We suggest that the lack of local studies in LTC stems from the lack of a strong case for the benefits of demand modelling at the local level in combination with low quantity and incomplete social care data. We propose a mathematical model to show how savings may be generated under different models of commitment with third party providers. Secondly, we propose a hybrid-fuzzy demand model to generate estimates of demand in the short to medium term that can be used to inform contract design based on local area needs – such an approach we argue is more suited to problems in which historic activity is incomplete or limited. Our results show that commitment models can be of great use to local health care planners with respect to lowering their care costs, at the same time our formulation had wider generic applicability to procurement type problems where commitment size in addition to the timing of commitments needs to be determined
Traveling Salesman Problem
This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering
Adaptive operator search for the capacitated arc routine problem
Evolutionary Computation approaches for Combinatorial Optimization have been successfully proposed for a plethora of different NP-Hard Problems. This research area has achieved acknowledgeable results and obtained remarkable progresses, and it has ultimately established itself as one of the most studied in AI. Yet, predicting the approximation ability of Evolutionary Algorithms (EAs) on novel problem instances remains a difficult easy task. As a consequence, their application in a real-world optimization context is reduced, as EAs are often considered not reliable and mature enough to be adopted in an industrial scenario. This thesis proposes new approaches to endow such meta-heuristics with a mechanism that would allow them to extract information during the search and to adaptively use such information in order to modify their behaviour and ultimately improve their performances. We consider the case study of the Capacitated Arc Routing Problem (CARP), to demonstrate the effectiveness of adaptive search techniques in a complex problem deeply connected with real-world scenarios. In particular, the main contributions of this thesis are:
1. An investigation of the adoption of a Parameter Tuning mechanism to adaptively choose the crossover operator that is used during the search;
2. The study of a novel Adaptive Operator Selection technique based on the use of Fitness Landscape Analysis techniques and on Online Learning;
3. A novel approach based on Knowledge Incorporation focusing on the reuse of information learned from the execution of a meta-heuristic on past instances, that is later used to improve the performances on the newly encountered
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