44 research outputs found
Dynamic multi-objective optimization using evolutionary algorithms
Dynamic Multi-objective Optimization Problems (DMOPs) offer an opportunity to examine and solve challenging real world scenarios where trade-off solutions between conflicting objectives change over time. Definition of benchmark problems allows modelling of industry scenarios across transport, power and communications networks, manufacturing and logistics. Recently, significant progress has been made in the variety and complexity of DMOP benchmarks and the incorporation of realistic dynamic characteristics. However, significant gaps still exist in standardised methodology for DMOPs, specific problem domain examples and in the understanding of the impacts and explanations of dynamic characteristics. This thesis provides major contributions on these three topics within evolutionary dynamic multi-objective optimization. Firstly, experimental protocols for DMOPs are varied. This limits the applicability and relevance of results produced and conclusions made in the field. A major source of the inconsistency lies in the parameters used to define specific problem instances being examined. The uninformed selection of these has historically held back understanding of their impacts and standardisation in experimental approach to these parameters in the multi-objective problem domain. Using the frequency and severity (or magnitude) of change events, a more informed approach to DMOP experimentation is conceptualized, implemented and evaluated. Establishment of a baseline performance expectation across a comprehensive range of dynamic instances for well-studied DMOP benchmarks is analyzed. To maximize relevance, these profiles are composed from the performance of evolutionary algorithms commonly used for baseline comparisons and those with simple dynamic responses. Comparison and contrast with the coverage of parameter combinations in the sampled literature highlights the importance of these contributions. Secondly, the provision of useful and realistic DMOPs in the combinatorial domain is limited in previous literature. A novel dynamic benchmark problem is presented by the extension of the Travelling Thief Problem (TTP) to include a variety of realistic and contextually justified dynamic changes. Investigation of problem information exploitation and it's potential application as a dynamic response is a key output of these results and context is provided through comparison to results obtained by adapting existing TTP heuristics. Observation driven iterative development prompted the investigation of multi-population island model strategies, together with improvements in the approaches to accurately describe and compare the performance of algorithm models for DMOPs, a contribution which is applicable beyond the dynamic TTP. Thirdly, the purpose of DMOPs is to reconstruct realistic scenarios, or features from them, to allow for experimentation and development of better optimization algorithms. However, numerous important characteristics from real systems still require implementation and will drive research and development of algorithms and mechanisms to handle these industrially relevant problem classes. The novel challenges associated with these implementations are significant and diverse, even for a simple development such as consideration of DMOPs with multiple time dependencies. Real world systems with dynamics are likely to contain multiple temporally changing aspects, particularly in energy and transport domains. Problems with more than one dynamic problem component allow for asynchronous changes and a differing severity between components that leads to an explosion in the size of the possible dynamic instance space. Both continuous and combinatorial problem domains require structured investigation into the best practices for experimental design, algorithm application and performance measurement, comparison and visualization. Highlighting the challenges, the key requirements for effective progress and recommendations on experimentation are explored here
Operational research:methods and applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order
Automated design of population-based algorithms: a case study in vehicle routing
Metaheuristics have been extensively studied to solve constraint combinatorial optimisation problems such as vehicle routing problems. Most existing algorithms require considerable human effort and different kinds of expertise in algorithm design. These manually designed algorithms are discarded after solving the specific instances. It is highly desirable to automate the design of search algorithms, thus to solve problem instances effectively with less human intervention.
This thesis develops a novel general search framework to formulate in a unified way a range of population-based algorithms. Within this framework, generic algorithmic components such as selection heuristics on the population and evolution operators are defined, and can be composed using machine learning to generate effective search algorithms automatically. This unified framework aims to serve as the basis to analyse algorithmic components,
generating effective search algorithms for complex combinatorial optimisation problems. Three key research issues within the general search framework are identified: automated design of evolution operators, of selection heuristics, and of both.
To accurately describe the search space of algorithm design as a new task for machine learning, this thesis identifies new key features, namely search-dependent and instance-dependent features. These features are identified to assist effective algorithm design. With these features, a set of state-of-the-art reinforcement learning techniques, such as deep Q-network based and proximal policy optimisation based models and maximum entropy mechanisms have been developed to intelligently select and combine appropriate evolution operators and selection heuristics during different stages of the optimisation process. The effectiveness and generality of these algorithms automatically designed within the proposed general search framework are validated comprehensively across different capacitated vehicle routing problem with time windows benchmark instances. This thesis contributes to making a key step towards automated algorithm design with a general framework supporting fundamental analysis by effective machine learning
Operational Research: Methods and Applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
An investigation of F-Race training strategies for cross domain optimisation with memetic algorithms
Parameter tuning is a challenging and time-consuming task, crucial to obtaining improved metaheuristic performance. There is growing interest in cross-domain search methods, which consider a range of optimisation problems rather than being specialised for a single domain. Metaheuristics and hyper-heuristics are typically used as high-level cross-domain search methods, utilising problem-specific low-level heuristics for each problem domain to modify a solution. Such methods have a number of parameters to control their behaviour, whose initial settings can influence their search behaviour significantly. Previous methods in the literature either fix these parameters based on previous experience, or set them specifically for particular problem instances. There is a lack of extensive research investigating the tuning of these parameters systematically. In this paper, F-Race is deployed as an automated cross-domain parameter tuning approach. The parameters of a steady-state memetic algorithm and the low-level heuristics used by this algorithm are tuned across nine single-objective problem domains, using different training strategies and budgets to investigate whether F-Race is capable of effectively tuning parameters for cross-domain search. The empirical results show that the proposed methods manage to find good parameter settings, outperforming many methods from the literature, with different configurations identified as the best depending upon the training approach used
Play Among Books
How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an “infinite flow” of real books
Automation and Control
Advances in automation and control today cover many areas of technology where human input is minimized. This book discusses numerous types and applications of automation and control. Chapters address topics such as building information modeling (BIM)–based automated code compliance checking (ACCC), control algorithms useful for military operations and video games, rescue competitions using unmanned aerial-ground robots, and stochastic control systems
Applied Methuerstic computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Desenvolvimento de uma metodologia baseada em um modelo exato para resolver o picker routing problem em um caso real
Orientador: Prof. Dr. Cassius Tadeu ScarpinDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas, Programa de Pós-Graduação em Gestão de Organizações, Liderança e Decisão. Defesa : Curitiba, 14/10/2022Inclui referênciasResumo: Neste trabalho apresenta-se uma aplicação real de um modelo exato para o Problema de Roteamento de Separadores de Pedidos, também conhecido com Picker Routing Problem (PRP), em uma Rede varejista do setor supermercadista. O estudo de caso feito na pesquisa foi no Centro de Distribuição desta rede supermercadista. O PRP consiste em determinar a menor rota a ser percorrida por um separador em um Centro de Distribuição (CD) de forma a coletar manualmente todos os produtos contidos em um determinado pedido. Tem-se como objetivo a aplicação de um modelo de Programação Linear Inteira Mista (PLIM), encontrado na literatura, e a comparação dos resultados obtidos com o atual método utilizado na empresa, a heurística SShape. Para isso, dados reais de pedidos de um determinado período foram coletados e algumas suposições relativas ao tamanho do problema e ao leiaute do CD foram feitas para gerar os 65 cenários de testes estabelecidos. Para atingir o objetivo almejado, foi necessário elaborar um algoritmo em três etapas, em linguagem de programação C#. A primeira etapa é o tratamento de dados e ajuste do leiaute para a elaboração do modelo Matemático. Com uso do solver GUROBI para a resolução dos testes, realizou-se a segunda etapa. A terceira etapa consistiu na aplicação da heurística S-Shape para possibilitar a comparação entre os métodos. As comparações entre o modelo aplicado e a heurística da empresa foram avaliadas em termos de economias (em metros) do trajeto gerado e tempo de resolução. Em 81,54% dos testes, o modelo obteve melhores resultados, gerando rotas com distâncias menores. Os outros 18,46% ambos os métodos retornaram o mesmo resultado. A melhoria média geral ficou em 8,41%. O modelo com parâmetro alterado resolveu 87,69% dos testes em até 30 minutos, considerado como tempo aceitável em termos práticos operacionais. Para os 12,31% dos testes resolvidos acima de 30 minutos, uma manipulação nos dados para contornar essa situação foi sugerida. Dessa forma, foi considerada como vantajosa a aplicação do modelo para o problema real de roteamento de pickers.Abstract: This work presents a real application of an exact model for the Picker Routing Problem (PRP), in a retail chain in the supermarket sector. The case study done in the research was in the Distribution Center of this supermarket chain. The PRP consists of determining the shortest route to be taken by a picker in a Distribution Center (DC) in order to manually collect all the products contained in a given order. The objective is to apply a Mixed Integer Linear Programming (MILP) model, found in the literature, and to compare the results obtained with the current method used in the company, the SShape heuristic. For this, actual order data for a given period was collected and some assumptions regarding the size of the problem and the CD layout were made to generate the 65 established test scenarios. To achieve the desired goal, it was necessary to develop an algorithm in three steps, in C # programming language. The first step is the data treatment and adjustment of the layout for the elaboration of the Mathematical model. Using the GUROBI solver to solve the tests, the second step was performed. The third step consisted of applying the S-Shape heuristic to make it possible to compare the methods. The comparisons between the applied model and the company's heuristic were evaluated in terms of savings (in meters) of the generated route and resolution time. In 81.54% of the tests, the model obtained better results, generating routes with shorter distances. The other 18.46% both methods returned the same result. The overall average improvement was 8.41%. The model with an altered parameter solved 87.69% of the tests within 30 minutes, considered an acceptable timeframe in operational practical terms. For the 12.31% of the tests resolved over 30 minutes, a manipulation of the data to get around this situation was suggested. Thus, it was considered advantageous to apply the model to the real problem of picker routing
Holistic, data-driven, service and supply chain optimisation: linked optimisation.
The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis