Using micro genetic algorithm for solving scheduling problems

Job Shop Scheduling Problem (JSSP) and Timetable scheduling are known to be computationally NP–hard problems. There have been many attempts by many researchers to develop reliable scheduling software, however, many of these software have only been tested or applied on an experimental basis or on a small population with minimal constraints. However in actual model JSSP, the constraints involved are more complicated compared to classical JSSP and feasible schedule must be suggested within a short period of time. In this thesis, an enhanced micro GA, namely micro GA with local search is proposed to solve an actual model JSSP. The scheduler is able to generate an output of a set of feasible production plan not only at a faster rate but which can generate a plan which can reduce the makespan as compare to those using manual. Also, in this thesis, the micro GA is applied to the timetabling problem of Faculty of Electrical Engineering Universiti Teknologi Malaysia which has more than 3,000 students. Apart from having more students, the faculty also offers various different type s of specialized courses. Various constraints such as elective subjects, classrooms capacity, multiple sections students, lecturer, etc have to be taken into consideration when designing the solution for this problem. In this thesis , an enhanced micro GA is proposed for timetable scheduling in the Faculty to overcome the problems. The enhanced micro GA algorithm is referred to as distributed micro GA which has local search to speed up the scheduling process. Comparisons are made with simple GA methods such that a more optimal solution can be achieved. The proposed algorithm is successfully implemented at the Faculty meeting a variety of constraints not achievable using manual methods

Algoritmo Genético aplicado al problema de programación en procesos tecnológicos de maquinado con ambiente Flow Shop

Debido a las limitaciones de las técnicas de optimización convencionales, en el siguiente trabajo se presenta una metaheurística basada en un algoritmo genético (AG), para resolver problemas de programación de tipo flow shop, con el objetivo de minimizar el tiempo de finalización de todos los trabajos, más conocido como makespan. Este problema, considerado de difícil solución, es típico de la optimización combinatoria y se presenta en talleres con tecnología de maquinado, donde existen máquinas-herramientas convencionales y se fabrican diferentes tipos de piezas que tienen en común una misma ruta tecnológica (orden del proceso). La solución propuesta se probó con problemas clásicos publicados por otros autores, obteniéndose resultados satisfactorios en cuanto a la calidad de las soluciones encontradas y el tiempo de cómputo empleado

Optimizing Time Utilization of FMS

The aim of the research is to solve the problem of simultaneous production on the flexible manufacturing system with different combination of product types and quantities that will give maximal utilization of production system. The presumption for good utilization of FMS (Flexible Manufacturing System) is in forming of working order with such product type structure that will make possible of production processing with minimal time load of complete production system. Working order structure from the point of product types and quantities is dictated by market demands that are known earlier. Because the structure of particular working order is not harmonized with the exploitation characteristics of FMS, we are faced with problem how to realize working order in such conditions as well as how to achieve main goal: shorter machining cycle with less time occupation of production system. The method based on two phases for solving problem of control working order realization is presented in the work. In the first phase the selection of optimal combination of process plans which gives minimal time load of production system through simultaneous production of different products and their quantities is given. In the second phase the order of part production and the order of particular operations processing is optimized. The optimization problem in both phases of control is solved by application of genetic algorithm approach. The software for computing and optimizing of processing order on FMS is developed

An analysis on selection methods and multirecombination in evolutionary search when solving the job shop scheduling problem

Evolutionary algorithms (EAs) can be used as optimisation mechanisms. Based on the model of natural evolution, they work on populations of individuals instead of on single solutions. In this way, the search is performed in a parallel manner. During the last decades, there has been an increasing interest in evolutionary algorithms to solve scheduling problems. One important feature in these algorithms is the selection of individuals. Selection is the operation by which individuals (i.e. their chromosomes) are selected for mating. To emulate natural selection, individuals with higher fitness should be selected with higher probability, and thus it is one of the operators where the fitness plays an important role. There are many different models of selection (some are not biologically plausible). Commonly, proportional, ranking, tournament selection and stochastic universal sampling are used. EAs considered in this work are improved with a multiplicity feature to solve the job shop scheduling problems (JSSP). The algorithm applied here, multiple crossovers on multiple parents (MCMP), considers more than two parents for reproduction with the possibility to generate multiple children. This approach uses a permutation representation for the chromosome. The objective of this work is to compare the algorithms performance using different selection mechanisms and to analyse the different crossover methods developed to apply MCMP with a permutation representation.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

An analysis on selection methods and multirecombination in evolutionary search when solving the job shop scheduling problem

Evolutionary algorithms (EAs) can be used as optimisation mechanisms. Based on the model of natural evolution, they work on populations of individuals instead of on single solutions. In this way, the search is performed in a parallel manner. During the last decades, there has been an increasing interest in evolutionary algorithms to solve scheduling problems. One important feature in these algorithms is the selection of individuals. Selection is the operation by which individuals (i.e. their chromosomes) are selected for mating. To emulate natural selection, individuals with higher fitness should be selected with higher probability, and thus it is one of the operators where the fitness plays an important role. There are many different models of selection (some are not biologically plausible). Commonly, proportional, ranking, tournament selection and stochastic universal sampling are used. EAs considered in this work are improved with a multiplicity feature to solve the job shop scheduling problems (JSSP). The algorithm applied here, multiple crossovers on multiple parents (MCMP), considers more than two parents for reproduction with the possibility to generate multiple children. This approach uses a permutation representation for the chromosome. The objective of this work is to compare the algorithms performance using different selection mechanisms and to analyse the different crossover methods developed to apply MCMP with a permutation representation.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Hybrid multiobjective genetic algorithm for integrated dynamic scheduling and routing of jobs and automated guided vehicles in flexible manufacturing systems

The dynamic continues trend of adoption and improvement inventive automated technologies is one of the main competing strategies of many manufacturing industries. Effective integrated operations management of Automated Guided Vehicle (AGV) system in Flexible Manufacturing System (FMS) environment results in the overall system performance. Routing AGVs was proved to be NP-Complete and scheduling of jobs was also proved to be NP hard problems. The running time of any deterministic algorithms solving these types of problems increases very rapidly with the size of the problem, which can be many years with any computational resources available presently. Solving AGVs conflict free routing, dispatching and simultaneous scheduling of the jobs and AGVs in FMS in an integrated manner is identified as the only means of safeguarding the feasibility of the solution to each sub-problem. Genetic algorithm has recorded of huge success in solving NP-Complete optimization problems with similar nature to this problem. The objectives of this research are to develop an algorithm for integrated scheduling and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm, ensure the algorithm validity and improvement on the performance of the developed algorithm. The algorithm generates an integrated scheduling and detail paths route while optimizing makespan, AGV travel time, mean flow time and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The integrated algorithms use two genetic representations for the individual solution entire sub-chromosomes. The first three sub-chromosomes use random keys to represent jobs sequencing, operations allocation on machines and AGV dispatching, while the remaining sub-chromosomes are representing particular routing paths to be used by each dispatched AGV. The multiobjective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance history. The algorithm used weight mapping crossover (WMX) and Insertion Mutation (IM) as genetic operators for sub-chromosomes represented with priority-based representation. Parameterized uniform crossover (PUX) and migration are used as genetic operators for sub-chromosomes represented using random-key based encoding. Computational experiments were conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. First scenario uses static consideration, the second scenario uses dynamic consideration with machine failure recovery. Sensitivity analysis and convergence analysis was also conducted. The results show the effectiveness of the proposed algorithm in generating the integrated scheduling, AGVs dispatching and conflict-free routing. The comparison of the result of the developed integrated algorithm using two benchmark FMS scheduling algorithms datasets is conducted. The comparison shows the improvement of 1.1% and 16% in makespan of the first and the second benchmark production dataset respectively. The major novelty of the algorithm is an integrated approach to the individual sub-problems which ensures the legality, and feasibility of all solutions generated for various sub-problems which in the literature are considered separately

Algoritmos evolutivos avanzados como soporte del proceso productivo

El mundo de los negocios actuales está sufriendo muchos cambios, ya no basta con generar reportes y realizar una correcta planificación. Se deben incluir herramientas de optimización para crear soluciones de negocios adaptativas como por ejemplo para límites de créditos, precios y descuentos, y scheduling. Esto redundará en beneficios para la empresa ya sea en la disponibilidad de tecnología de avanzada como también en la disminución de los costos asociados a la toma de decisiones óptimas, también incrementará la capacidad para aprender de experiencias previas y para adaptar a cambios en el mercado. En estos últimos años se han realizados muchos estudios de investigación respecto de la aplicación de las técnicas de computación evolutiva para la solución de problemas de scheduling. La principal ventaja de las técnicas evolutivas es su habilidad para proveer buenas soluciones a problemas extremadamente complejos usando tiempos razonables. En este trabajo se hace un revisión de las clases y características de algoritmos evolutivos así como también algunas mejoras introducidas a los mismos. Entre estas últimas se pueden incluir múltiple crossover, multiplicidad de padres y prevención de incesto. Asimismo se presentan algunas variantes de algoritmos evolutivos planteados para la resolución de un problema particular de scheduling como lo es el problema de job shop scheduling.Facultad de Informátic

Um sistema de apoio a decisão para o planejamento operacional de empresas de transporte rodoviario urbano de passageiros

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro TecnologicoProposição de um sistema de apoio à decisão para o planejamento operacional de empresas de transporte rodoviário urbano de passageiros. Proposição de modelos para alocação de veículos e condutores e para a geração de escalas de trabalho. Análise de dois casos resolvidos com a aplicação do sistema proposto

Permanente Optimierung dynamischer Probleme der Fertigungssteuerung unter Einbeziehung von Benutzerinteraktionen

Trotz enormen Forschungsaufwands erhalten die Entscheider in der Fertigungssteuerung nur rudimentäre Rechnerunterstützung. Diese Arbeit schlägt ein umfassendes Konzept für eine permanent laufende algorithmische Feinplanung vor, die basierend auf einer Analyse des Optimierungspotentials und -bedarfs intelligent mit den Entscheidern kollaboriert und zeitnah auf Fertigungsereignisse reagiert. Dynamische Simulationen mit Unternehmensdaten bestätigen die Praxistauglichkeit des Konzepts

Uma contribuição para o escalonamento da produção baseado em métodos globalmente distribuídos

Tese Doutoramento em Engenharia e Gestão IndustrialO escalonamento da produção é uma função que pode contribuir fortemente para a capacidade competitiva das empresas produtores de bens e serviços. Pode, simplificadamente ser definido com a função de afectar tarefas a meios de produção ao longo do tempo. Resolver um problema de escalonamento envolve várias actividades. Primeiro é necessário descrever o problema e identificálo numa dada classe. Depois, é necessário encontrar um método eficiente apropriado à sua resolução e usá-lo para resolver o problema, disponibilizando os dados necessários e tratando os resultados de forma conveniente. Muitas vezes as pessoas envolvidas na resolução de problemas de escalonamento não conhecem os métodos ou não sabem como ter acesso a eles, ou mesmo não sabem como formalmente identificar o problema numa classe. O trabalho de investigação aqui relatado pretende contribuir para facilitar a compreensão dos problemas e melhorar o processo de escalonamento na indústria, apresentando contribuições no domínio do escalonamento da produção em duas envolventes principais: uma a um nível teórico, conceptual e outra ao nível prático da resolução de problemas. Ao nível conceptual contribui para uma ontologia de problemas de escalonamento da produção e conceitos relacionados, tais como ambiente de escalonamento de produção, tarefas, postos de trabalho, métodos de resolução, implementações e soluções, providenciando um enquadramento comum para a compreensão a partilha de conhecimento acerca destes conceitos. Ao nível da resolução dos problemas de escalonamento, faz-se um estudo alargado sobre a forma da sua resolução. Na concretização deste estudo desenvolve-se um sistema web de apoio ao escalonamento da produção (SWAEP), cuja arquitectura e funcionalidades são implementadas através de um demonstrador desenvolvido e implementado com base na tecnologia XML e na arquitectura de rede P2P, formando uma comunidade virtual de escalonamento da produção. Este demonstrador permite testar e avaliar a viabilidade das premissas e hipóteses subjacentes a este trabalho de investigação das quais é de realçar a pretensão de melhorar o processo de escalonamento de empresas industriais usando um sistema web de escalonamento da produção baseados em conhecimento globalmente distribuído e acedido via Internet e intranets. Esta filosofia de suporte ao escalonamento industrial pode considerar-se inovadora, porquanto difere das soluções existentes nas empresas ou actualmente acessíveis via Internet, por serem predominantemente centralizadas e pouco abrangentes.Production Scheduling is an important function strongly contributing to the competitiveness of industrial and service companies. It may be defined as the activity of allocating tasks to production resources, during a certain period of time. Solving a production scheduling problem involves several activities, starting with a clear description of it, with its identification within a class of problems. The next task is to find at least one suitable and efficient method, if it exists, for solving the problem, followed by running implementations of the method with the required set of input data and getting results in some adequate or desirable form. Often the people who have the problem to solve don’t know about the methods or even the existing problem classification. The research work here reported aims at facilitating the understanding of scheduling problems and improving the scheduling processes in companies. For this it presents contributions at two complementary levels. First, at the conceptual level it contributes for an ontology of scheduling problem related concepts, such as problem, scheduling environment, job, processor, method, implementation and solution, providing a common setting for knowledge sharing about these concepts. At the problem resolution level it comprises a web based scheduling system, with a distributed knowledge base, for sharing knowledge about scheduling and for aiding to solve scheduling problems. A demonstrator of this system is developed, implementing its architecture and functionalities, using XML technology and a P2P computer network forming a virtual community for production scheduling. The demonstrator is used for testing and evaluating the main idea behind this work namely that of sharing methods globally available for improving the scheduling process at companies by accessing these methods through the Internet. This scheduling strategy can be considered innovative having into account that most of the available scheduling systems are mostly centralized systems implemented at companies or accessed through the Internet and of limited scope