Modelo Matemático e Meta-Heurística Simulated Annealing para Elaboração de Roteiros Turísticos com base no Tourist Trip Design Problem

Muito embora existam diversos pacotes de viagens com destinos predefinidos contemplando locais mais populares, nos últimos anos tem crescido a procura por soluções que criem roteiros personalizados voltados às necessidades de cada turista. Para suprir essa nova demanda surge o Problema de Elaboração de Rotas Turísticas (PERT) ou TouristTrip Design Problem (TTDP) o qual Van Oudheusden e Vansteenwegen (2007) sugerem o uso do OrienteeringProblem (OP) e suas extensões para resolução desta classe de problemas. Esta dissertação tem por objetivo o desenvolvimento de um modelo matemático e de uma meta-heurística SimulatedAnnealing (SA) para resolução do TouristTrip Design Problem (TTDP)

O problema de orientação de equipas capacitado com janelas temporais aplicado à recolha e transporte de leite cru

Dissertação de mestrado integrado em Engenharia e Gestão Industrialglobalização obriga, de certa forma, a que as empresas tenham maior rigor na qualidade dos produtos e serviços prestados, bem como a entrega dos produtos e serviços certos, nos locais certos e nas horas certas, aumentando assim os seus níveis de serviço. Um setor muito importante nesse processo, porém muito dispendioso, é o transporte, o que torna essencial a sua incessante otimização com vista a minimizar os seus custos. Nesta dissertação propõe-se a aplicação de modelos baseados nos problemas de orientação de equipas com restrições de capacidades e janelas temporais com vista a otimizar a recolha e transporte de leite cru por parte das pequenas e médias empresas portuguesas. Inicialmente apresenta-se o atual estado da arte sobre os problemas de orientação, nomeadamente as diversas variantes dos problemas de orientação de equipas. De seguida é exposto o estado da arte dos algoritmos genéticos bem como da framework BRKGA. Apresenta-se o problema da recolha e transporte de leite cru em Portugal por parte das pequenas e médias empresas bem como a modelação do mesmo num modelo matemático representativo para a obtenção de soluções a nível computacional. Apresenta-se a aplicação standalone desenvolvida, em linguagem C++ com recurso ao IDE NetBeans e o Qt Creator, para facilitar a criação de instâncias e obtenção de soluções para as mesmas. Apresenta-se ainda a análise dos resultados experimentais obtidos nos testes computacionais realizados às diversas instâncias geradas aleatoriamente. Os resultados são expostos em folhas de cálculo e gráficos para permitir uma melhor análise dos mesmos. Por fim, são expostas as principais conclusões retiradas do trabalho desenvolvido e as metas para um trabalho futuro.Globalization forces, in a way, the companies have greater rigour in the quality of products and services, as well as the delivery of right goods and services, in the right places and at the right time, thereby increasing their levels of services. A very important sector in that process, though very expensive, is transport. Therefore, it is essential a continuous optimization in order to reduce their costs. This dissertation proposes the application of models based on the capacitated team orienteering problems with time windows constraints in order to optimize the collection and transport of raw milk from small and medium-sized Portuguese’s enterprises. Initially presented the current state of the art about the orienteering problems, particularly the several variants of teams orienteering problems. Then is presented the state of the art of genetic algorithms as well as of the framework BRKGA. It is presented the problem of the collection and transport of raw milk in Portugal by small and medium-sized enterprises as well as the modelling of a representative mathematical model in order to obtain solutions in a computational level. It is presented the standalone application developed in C++ using the NetBeans IDE and the Qt Creator IDE to facilitate the creation of instances and obtaining solutions for them. Presents the analysis of the results obtained in tests conducted at several instances randomly generated. The results are displayed in spreadsheets and graphics for a better analysis of them. Finally, the main conclusions from this work and the goals for future work are exposed

Інформаційна система з підтримки роботи керівника мобільної групи з питань охорони та безпеки торгівельної мережі

Актуальність. Для холдингу Fozzy дуже важливою компонентою є ефективна робота працівників, особливо тих, які працюють поза межами офісу. Департаменту дуже важливо розуміти, як працює ця категорія людей, які поставлені перед ними задачі, і як вони з ними справляються. Тому, використання інформаційних технологій з метою організації часу мобільної групи є актуальною задачею на сьогоднішній день, тому що це не тільки допоможе працівникам у роботі, а і надасть можливість керівництву чітко усвідомлювати ті задачі, які поставлені перед працівником і відслідковувати їх виконання та завантаженість кожного із них. Саме тому і набули широкого розповсюдження персоналізовані електронні органайзери (Microsoft Outlook), у функціонал яких покладено задачу організації робочого часу. Математична модель може дещо змінюватись у зв’язку зі зміною умов досліджуваної області. Математичною моделлю цієї роботи є задача Організації робочого часу Куратора СБ з використанням часових вікон (Security Leader Problem with Time Windows, SLPTW). Мета роботи і задачі дослідження. Метою є підвищення ефективності організації процесу відвідування філій та вирішення задач працівником мобільної групи. Для досягнення поставленої мети необхідно вирішити такі завдання: провести аналітику відомих методів вирішення задачі SLPTW; удосконалити існуючий метод розв’язання задачі за допомогою технологій паралельного програмування; програмно реалізувати алгоритм SLPTW; вирішити задачу алгоритмічної реалізації алгоритму; провести дослідження на ефективність реалізованого алгоритму.[] Об’єкт дослідження – є процес прокладання маршрутів керівнику мобільної групи з питань охорони та безпеки торгівельної мережі. Предмет дослідження – задача підвищення ефективності організації робочого часу працівника мобільної групи. Методи дослідження, використані в роботі, відносять до класу алгоритмів метаевристики. Наукова новизна отриманих результатів базується на вдосконаленні алгоритму ILS (повторюваного локального пошуку) та у порівнянні його з алгоритмом SA (імітаційного відпалу), застосування паралельної форми обчислень паралельного у програмуванні з метою модифікації алгоритму повторюваного локального пошуку для вирішення проблематики задачі TOPTW. Зв’язок роботи з науковими програмами, планами, темами. Робота реалізовувалась на кафедрі АСОІУ факультету ФІОТ Національного технічного університету України «Київський політехнічний інститут ім. Ігоря Сікорського»For the Fozzy group holding, a very important component is the efficient work of employees, especially those who work outside the office. It is very important for the department to understand how this category of people who are tasked with it and how they handle it. Therefore, the use of information technology to organize the time of the mobile group is an urgent task today, because it will not only help workers in the work, but also allow management to clearly understand the tasks that are set before the employee and monitor their performance, and the workload of each of them. In this regard, personalized electronic organizers (Microsoft Outlook), which include the functionality of working time, have become widespread. When solving it, the mathematical model may differ depending on which domain conditions are taken into account. In this paper, the mathematical model is the task of organizing the Security Leader Problem with Time Windows (SLPTW). Since response time for software is an important feature, developing an efficient algorithm for the task at hand is an up-to-date task. Therefore, this work is dedicated to the research and refinement of SLPTW. Purpose and tasks of the study. The goal is to maximize the aggregate value of visiting affiliates and solving problems with a mobile group employee. To achieve this goal it is necessary to solve the following tasks: analyze known results of solving the SLPTW task; to develop a method (modification of an existing method) of solving a problem using parallel programming technologies; develop algorithmic support for the SLPTW task; to develop software implementation of algorithm (s); Сonduct research on the effectiveness of the developed algorithmic support. The object of study is the process of drawing routes to a mobile group employee. The subject of the study - the task of improving the organization of working hours of a mobile group employee. The research methods used in the work are based on metaheuristic algorithms. The scientific novelty of the obtained results is to modify the algorithm of repetitive local search, to compare it with the algorithm of imitative annealing, to use the parallel programming technologies to modify the algorithms of repetitive local search, and to simulate annealing algorithm to solve the problem of the SLPTW problem. Relationship with working with scientific programs, plans, topics. The work was performed in the branch of the Department of Automated Information Processing and Control Systems of the National Technical University of Ukraine «Kyiv Polytechnic Institute. Igor Sikorsky»

Mathematical formulations and optimization algorithms for solving rich vehicle routing problems.

Objectives and methods of study: The main objective of this work is to analyze and solve three different rich selective Vehicle Routing Problems (VRPs). The first problem is a bi-objective variant of the well-known Traveling Purchaser Problem (TPP) in which the purchased products are delivered to customers. This variant aims to find a route for which the total cost (transportation plus purchasing costs) and the sum of the customers’s waiting time are simultaneously minimized. A mixed integer bi-objective programming formulation of the problem is presented and tested with CPLEX 12.6 within an ǫ-constraint framework which fails to find non-dominated solutions for instances containing more than 10 nodes. Therefore, a heuristic based on relinked local search and Variable Neighborhood Search (VNS) is proposed to approximate the Pareto front for large instances. The proposed heuristic was tested over a large set of artificial instances of the problem. Computational results over small-sized instances show that the heuristic is competitive with the ǫ-constraint method. Also, computational tests over large-sized instances were carried out in order to study how the characteristics of the instances impact the algorithm performance. The second problem consists of planning a selective delivery schedule of multiple products. The problem is modeled as a multi-product split delivery capacitated team orienteering problem with incomplete services, and soft time windows. The problem is modeled through a mixed integer linear programming formulation and approximated by means of a multi-start Adaptive Large Neighborhood Search (ALNS) metaheuristic. Computational results show that the multi-start metaheuristic reaches better results than its classical implementation in which a single solution is build and then improved. Finally, an Orienteering Problem (OP) with mandatory visits and conflicts, is formulated through five mixed integer linear programming models. The main difference among them lies in the way they handle the subtour elimination constraints. The models were tested over a large set of instances of the problem. Computational experiments reveal that the model which subtour elimination constraints are based on a single-commodity flow formulation allows CPLEX 12.6 to obtain the optimal solution for more instances than the other formulations within a given computation time limit. Contributions: The main contributions of this thesis are: • The introduction of the bi-objective TPP with deliveries since few bi-objective versions of the TPP have been studied in the literature. Furthermore, to the best of our knowledge, there is only one more work that takes into account deliveries in a TPP. • The design and implementation of a hybrid heuristic based on relinked local search and VNS to solve the bi-objective TPP with deliveries. Additionally, we provide guidelines for the application of the heuristic when different characteristics of the instances are observed. • The design and implementation of a multi-start adaptive large neighborhood search to solve a selective delivery schedule problem. • The experimental comparison among different formulations for an OP with mandatory nodes and conflicts

Задача командного спортивного орієнтування з урахуванням часових вікон

Магістерська дисертація: 110 с., 20 рис., 22 табл., 1 додаток, 84 джерела. Актуальність. Всесвітня туристська організація (World Tourism Organization, UNWTO) визначає впровадження нововведень в туризмі однією з основних функцій туристичного маркетингу. Тому, використання інформаційних технологій з метою розвитку туризму, є актуальною задачею на сьогоднішній день. В зв’язку з цим, широкого розповсюдження набули персоналізовані електронні туристичні путівники (Personalized Electronic Tourist guides, PETs), до функціональності яких відноситься задача побудови туристичних маршрутів. При її розв’язанні математична модель може відрізнятись з огляду на те, які умови предметної області враховуються. В даній роботі математичною моделлю виступає задача Командного спортивного орієнтування з часовими вікнами (Team Orienteering Problem with Time Windows, TOPTW). Оскільки час реагування для програмного забезпечення є важливою ознакою, розробка ефективного алгоритму поставленої задачі на сьогоднішній день є актуальною задачею. Тому, дана робота присвячена дослідженню та удосконаленню розв’язування TOPTW. Мета роботи і задачі дослідження. Метою є максимізація сумарної корисності побудованих туристичних маршрутів заданої тривалості з врахуванням часових періодів відвідування туристичних місць. Для досягнення поставленої мети необхідно вирішити такі завдання: − провести аналіз відомих результатів розв’язування задачі TOPTW; − розробити метод (модифікацію існуючого методу) розв’язання задачі з використанням технологій паралельного програмування; − розробити алгоритмічне забезпечення задачі TOPTW; − розробити програмну реалізацію алгоритму(ів); − провести дослідження ефективності розробленого алгоритмічного забезпечення. Об’єкт дослідження – процес складання туристичних маршрутів. 4 Предмет дослідження – задача командного спортивного орієнтування з часовими вікнами. Методи дослідження, застосовані в роботі, базуються на метаевристичних алгоритмах. Наукова новизна одержаних результатів полягає у модифікації алгоритму повторюваного локального пошуку, порівнянні його з алгоритмом іматійного відпалу, використанні технологій паралельного програмування для модифікації алгоритмів повторюваного локального пошуку і алгоритму імітаційного відпалу для розв’язання задачі задачі TOPTW. Зв'язок роботи з науковими програмами, планами, темами. Робота виконувалась у філії кафедри автоматизованих систем обробки інформації та управління Національного технічного університету України «Київський політехнічний інститут ім. Ігоря Сікорського» в рамках науково-дослідної теми Інституту кібернетики ім. В. М. Глушкова НАН України: «Розробити математичний апарат, орієнтований на створення інтелектуальних інформаційних технологій розв’язування проблем комбінаторної оптимізації та інформаційної безпеки» (шифр теми: ВФ.180.11). Публікації. Результати роботи опубліковані у матеріалах науково практичної конференції «Інформатика та обчислювальна техніка-ІОТ-2017» [82], міжнародної науково-практичної конференції «Актуальні питання сьогодення» [83].Master dissertation:110 p., 20 fig., 22 tab., 1 appendix, 84 sources. Relevance. The World Tourism Organization (UNWTO) identifies the introduction of tourism innovations as one of the main functions of tourism marketing. Therefore, the use of information technology for the development of tourism is an actual task to date. Because of that, personalized Electronic Tourist Guides (PETs), which encapsulate tourist trip design problem (TTDP), have become widespread. When TTDP is solved, the mathematical model may differ in terms of what the terms of the subject area are taken into account. In this paper, the problem of Team Orienteering Problem with Time Windows (TOPTW) is the mathematical model. As the response time for the software is an important feature, developing an effective algorithm for the task to date is an actual task. Therefore, this work is devoted to the research and improvement of the solution methods of TOPTW. Purpose and objectives of the study. The purpose is to maximize the total usefulness of the built tourist routes of a given duration, taking into account the time periods of visiting tourist places. To achieve this goal it is necessary to solve the following tasks: − to analyze known results of solving TOPTW; − to develop a method (modification of the existing method) for solving a problem using parallel programming technologies; − to develop algorithms for TOPTW; − to develop software implementation of the algorithms; − to study the effectiveness of the developed algorithms. The object of study – the process of designing tourist routes. Purpose of the study –team orienteering problem with time windows. The scientific novelty of the obtained results is in modifying the Iterated Local Search algorithm, comparing it with the algorithm of Simulated annealing, using parallel programming technologies for modifying the Iterated Local Search algorithms and the Simulated annealing algorithm for solving TOPTW. 6 Relationship of work with scientific programs, plans, themes. The work has been carried out at the branch of the Department of Computer-Aided Management and Data Processing Systems of The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" within the framework of the research topic of the Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine: "To develop a mathematical apparatus focused on the creation of intelligent information technologies for solving combinatorial optimization and information security problems" (topic code: VF.180.11). Publications. The results of the work are published in [82, 83]

Optimização do processo de recolha de resíduos: desenvolvimento de ferramentas de investigação operacional para o problema de orientação de equipas com multi-restrições

Tese de Doutoramento em Engenharia Industrial e de Sistemas.Nas últimas décadas, a gestão dos resíduos sólidos urbanos (RSU) tornou-se uma atividade de elevada importância, pois o controlo eficaz da produção desses resíduos é essencial para a existência humana ser sustentável na era moderna. A reciclagem dos resíduos de embalagens domésticas (RED) contribui bastante para o controlo e redução da geração de RSU. A reciclagem dos RED é possível através da sua separação dos outros tipos de RSU e deposição em pontos específicos que em Portugal são denominados ecopontos e possuem três tipos de contentores: papelões, embalões e vidrões. A gestão do processo de recolha de RED é realizada por empresas especializadas. A gestão adequada do processo de recolha é fundamental para se efetuarem recolhas de forma eficaz e com o menor custo possível, gerindo vários recursos como veículos, ecopontos (e contentores), pessoas (condutores e ajudantes), e tempo disponível (turnos, prazos, etc.). O foco de investigação desta tese de doutoramento incidiu no estudo do processo de recolha de resíduos, mais especificamente a recolha de RED para reciclagem. A recolha de dados e informação sobre este processo foi feita na empresa Braval que efetua recolha de RED em seis concelhos do distrito de Braga, em Portugal. No âmbito do processo de recolha de RED foi identificado um problema de encaminhamento de veículos (PEV) de grande complexidade. Desenvolveram-se ferramentas para a resolução do PEV com vista à otimização do processo de recolha de RED, as quais permitem efetuar: 1) a previsão do estado de enchimento dos contentores de ecopontos e determinação do ritmo a que se depositam resíduos nos contentores; 2) a otimização das rotas de recolha; 3) o agendamento de rotas e de contentores a recolher; 4) a aplicação de métodos de análise de decisão multi-critério (ADMC) para identificar soluções adequadas quando se alteram objetivos e é necessário cumprir certos critérios. As metodologias adoptadas para se efetuar a previsão da geração de RED foram os métodos de regressão linear e de redes neuronais artificiais. Foram propostos vários fatores com potencial para explicar a geração de RED. Com base nesses fatores e nas metodologias adoptadas, foram desenvolvidos modelos que permitem prever o número de recolhas anuais e mensais a efetuar para cada contentor de cada ecoponto. As experiências realizadas permitiram construir modelos capazes de prever as recolhas mensais e anuais para papelões, embalões e vidrões com elevada precisão. Foi também possível caracterizar os fatores mais contributivos para a geração RED. Na tarefa de otimização de rotas desenvolveram-se modelos específicos para caracterizar o PEV identificado, partindo do modelo geral do problema de orientação de equipas, (TOP, team orieenteering problem). Desenvolveram-se quatro novos modelos com base no TOP que não constavam na literatura. Para resolver os modelos do TOP e de outras variantes, foram desenvolvidos algoritmos genéticos do tipo geracional e celular. Foram realizadas experiências e avaliou-se o desempenho em instâncias de teste públicas do TOP, e das variantes TOPTW e CTOP. Os AG alcançaram resultados competitivos comparando com outros métodos do atual estado da arte em termos de qualidade de solução e rapidez de cálculo. Obtiveram-se valores superiores aos máximos conhecidos na literatura para sete instâncias públicas do TOP e para uma do CTOP. De um modo geral, os AG celulares apresentaram um desempenho superior aos AG geracionais nos testes realizados. Desenvolveu-se um método de agendamento de recolhas com base no ritmo de enchimento dos contentores, em que se estabelecem prioridades para o nível de urgência de recolha de cada contentor. O método de agendamento foi testado com dados reais da empresa Braval, e os resultados apontam para uma possível redução significativa das distâncias percorridas, antevendo reduções promissoras no consumo de combustível. Também se averiguou que para o mesmo período de agendamento, e considerando as reduções nos custos operacionais, é possível recolher mais papelões e vidrões, mantendo-se o mesmo número de embalões. A implementação do módulo de ADMC para o sistema de apoio à decisão foi executada com recurso ao software beSmart, o qual inclui métodos como o SMART, AHP e ValueFn. A utilização do beSmart e a aplicação dos métodos de ADMC a problemas reais de recolha de RED permitiu validar a sua utilidade utilizando os dados da empresa Braval. As ferramentas desenvolvidas nesta investigação permitem dar resposta a diferentes problemas que condicionam a otimização do processo de recolha de RED, e juntas constituem um sistema de apoio à decisão.In the last decades, the management of municipal solid waste (MSW) became an activity of high importance, since an effective control over waste production is essential to enable a sustainable human existence in the modern age. The recycling of household packaging waste (HPW) greatly accounts for control and reduction of MSW generation. The recycling of HPW is possible due to its previous separation from other waste streams within MSW, and further depositing in specific collection points that in Portugal are called ecopontos, which usually include three types of containers: papelões (for paper and cardboard), embalões (for plastic and metal) and vidrões (for glass). The management of the HPW collection process is performed by specialized companies. An adequate and efficient management of the HPW collection process is crucial in order to perform effective collections with the lowest cost possible, managing several resources such as vehicles, ecopontos (and containers), people (drivers and helpers), and the time available (work shifts, deadlines, schedules, etc.). The research focus of this doctoral thesis was on studying the waste collection process, more specifically the collection of HPW for recycling. Information and data about this process was obtained from Braval, a company that collects HPW in six municipalities that belong to the district of Braga, in Portugal. Within the scope of the HPW collection process, a vehicle routing problem (VRP) of great complexity was identified. In order to solve the identified VRP while aiming to optimize the HPW collection process, the following tools were developed: 1) a forecasting method to predict the filling level of the containers at each ecoponto and to determine the filling rate of each container; 2) a route optimization algorithm able to handle several variations of the VRP; 3) a scheduling method for HPW collections; 4) a multi-criteria decision analysis (MCDA) module based on specific software that embeds MCDA methods that are used to identify proper solutions when objectives change and certain criteria must be met. Regarding the methodologies used to forecast HPW generation, two methods were employed: linear regression and artificial neural networks. Several factors with potential to explain HPW generation were proposed. Based on those factors and the adopted methodologies, forecasting models were developed to predict the number of yearly and monthly collections for each container of each ecoponto. The performed experiments enabled the construction of models capable of predicting yearly and monthly collections for all types of container with high level of accuracy. In addition, the experiments revealed which factors have most impact on waste filling rates

Machine learning for improving heuristic optimisation

Heuristics, metaheuristics and hyper-heuristics are search methodologies which have been preferred by many researchers and practitioners for solving computationally hard combinatorial optimisation problems, whenever the exact methods fail to produce high quality solutions in a reasonable amount of time. In this thesis, we introduce an advanced machine learning technique, namely, tensor analysis, into the field of heuristic optimisation. We show how the relevant data should be collected in tensorial form, analysed and used during the search process. Four case studies are presented to illustrate the capability of single and multi-episode tensor analysis processing data with high and low abstraction levels for improving heuristic optimisation. A single episode tensor analysis using data at a high abstraction level is employed to improve an iterated multi-stage hyper-heuristic for cross-domain heuristic search. The empirical results across six different problem domains from a hyper-heuristic benchmark show that significant overall performance improvement is possible. A similar approach embedding a multi-episode tensor analysis is applied to the nurse rostering problem and evaluated on a benchmark of a diverse collection of instances, obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four particular instances. Genetic algorithm is a nature inspired metaheuristic which uses a population of multiple interacting solutions during the search. Mutation is the key variation operator in a genetic algorithm and adjusts the diversity in a population throughout the evolutionary process. Often, a fixed mutation probability is used to perturb the value at each locus, representing a unique component of a given solution. A single episode tensor analysis using data with a low abstraction level is applied to an online bin packing problem, generating locus dependent mutation probabilities. The tensor approach improves the performance of a standard genetic algorithm on almost all instances, significantly. A multi-episode tensor analysis using data with a low abstraction level is embedded into multi-agent cooperative search approach. The empirical results once again show the success of the proposed approach on a benchmark of flow shop problem instances as compared to the approach which does not make use of tensor analysis. The tensor analysis can handle the data with different levels of abstraction leading to a learning approach which can be used within different types of heuristic optimisation methods based on different underlying design philosophies, indeed improving their overall performance

Machine learning for improving heuristic optimisation

Heuristics, metaheuristics and hyper-heuristics are search methodologies which have been preferred by many researchers and practitioners for solving computationally hard combinatorial optimisation problems, whenever the exact methods fail to produce high quality solutions in a reasonable amount of time. In this thesis, we introduce an advanced machine learning technique, namely, tensor analysis, into the field of heuristic optimisation. We show how the relevant data should be collected in tensorial form, analysed and used during the search process. Four case studies are presented to illustrate the capability of single and multi-episode tensor analysis processing data with high and low abstraction levels for improving heuristic optimisation. A single episode tensor analysis using data at a high abstraction level is employed to improve an iterated multi-stage hyper-heuristic for cross-domain heuristic search. The empirical results across six different problem domains from a hyper-heuristic benchmark show that significant overall performance improvement is possible. A similar approach embedding a multi-episode tensor analysis is applied to the nurse rostering problem and evaluated on a benchmark of a diverse collection of instances, obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four particular instances. Genetic algorithm is a nature inspired metaheuristic which uses a population of multiple interacting solutions during the search. Mutation is the key variation operator in a genetic algorithm and adjusts the diversity in a population throughout the evolutionary process. Often, a fixed mutation probability is used to perturb the value at each locus, representing a unique component of a given solution. A single episode tensor analysis using data with a low abstraction level is applied to an online bin packing problem, generating locus dependent mutation probabilities. The tensor approach improves the performance of a standard genetic algorithm on almost all instances, significantly. A multi-episode tensor analysis using data with a low abstraction level is embedded into multi-agent cooperative search approach. The empirical results once again show the success of the proposed approach on a benchmark of flow shop problem instances as compared to the approach which does not make use of tensor analysis. The tensor analysis can handle the data with different levels of abstraction leading to a learning approach which can be used within different types of heuristic optimisation methods based on different underlying design philosophies, indeed improving their overall performance