New Solution Approaches for Scheduling Problems in Production and Logistics

The current cumulative PhD thesis consists of six papers published in/submitted to scientific journals. The focus of the thesis is to develop new solution approaches for scheduling problems encountering in manufacturing as well as in logistics. The thesis is divided into two parts: “ma-chine scheduling in production” and “scheduling problems in logistics” each of them consisting three papers. To have most comprehensive overview of the topic of machine scheduling, the first part of the thesis starts with two systematic review papers, which were conducted on tertiary level (i.e., re-viewing literature reviews). Both of these papers analyze a sample of around 130 literature re-views on machine scheduling problems. The first paper use a subjective quantitative approach to evaluate the sample, while the second papers uses content analysis which is an objective quanti-tative approach to extract meaningful information from massive data. Based on the analysis, main attributes of scheduling problems in production are identified and are classified into sever-al categories. Although the focus of both these papers are set to review scheduling problems in manufacturing, the results are not restricted to machine scheduling problem and the results can be extended to the second part of the thesis. General drawbacks of literature reviews are identi-fied and several suggestions for future researches are also provided in both papers. The third paper in the first part of the thesis presents the results of 105 new heuristic algorithms developed to minimize total flow time of a set of jobs in a flowshop manufacturing environ-ment. The computational experiments confirm that the best heuristic proposed in this paper im-proves the average error of best existing algorithm by around 25 percent. The first paper in second part is focused on minimizing number of electric tow-trains responsi-ble to deliver spare parts from warehouse to the production lines. Together with minimizing number of these electric vehicles the paper is also focused to maximize the work load balance among the drivers of the vehicles. For this problem, after analyzing the complexity of the prob-lem, an opening heuristic, a mixed integer linear programing (MILP) model and a taboo-search neighborhood search approach are proposed. Several managerial insights, such as the effect of battery capacity on the number of required vehicles, are also discussed. The second paper of the second part addresses the problem of preparing unit loaded devices (ULDs) at air cargos to be loaded latter on in planes. The objective of this problem is to mini-mize number of workers required in a way that all existing flight departure times are met and number of available places for building ULDs is not violated. For this problem, first, a MILP model is proposed and then it is boosted with a couple of heuristics which enabled the model to find near optimum solutions in a matter of 10 seconds. The paper also investigates the inherent tradeoff between labor and space utilization as well as the uncertainty about the volume of cargo to be processed. The last paper of the second part proposes an integrated model to improve both ergonomic and economic performance of manual order picking process by rotating pallets in the warehouse. For the problem under consideration in this paper, we first present and MILP model and then pro-pose a neighborhood search based on simulated annealing. The results of numerical experiment indicate that selectively rotating pallets may reduce both order picking time as well as the load on order picker, which leads to a quicker and less risky order picking process

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Sessenta anos de Shop Scheduling : uma revisão sistemática da literatura

Orientador : Prof. Dr. Cassius Tadeu ScarpinDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa: Curitiba, 09/02/2017Inclui referências : f. 449-492Resumo: Desde o seminal artigo de Johnson em 1954, a Programação da Produção em Shop Scheduling tem se tornado uma área relevante dentro da Pesquisa Operacional e, atualmente, duzentos trabalhos tangentes à temática são publicados anualmente. Dentre os artigos aqui citados tem-se aqueles que se dedicam à apresentação e síntese do estado da arte desse assunto, intitulados artigos de revisão. Quando tais artigos são elaborados a partir de um conjunto objetivo de critérios, relativos à categorização dos artigos selecionados, tem-se a Revisão Sistemática da Literatura (RSL). O presente trabalho realiza uma RSL em Shop Scheduling, a partir da análise de cada ambiente fabril que o compõe. Fez-se o escrutínio de 560 artigos, à luz de um conjunto de métricas, que constitui a estrutura basilar da proposta de nova taxonomia do Shop Scheduling, complementar à notação de Graham, objetivo fulcral do presente trabalho. Além disso, utilizou-se uma representação em redes dos resultados obtidos em algumas das métricas empregadas, como a característica dos itens, algo outrora inaudito em estudos de revisão desse assunto. Ademais, outro ponto relevante desse estudo repousa na identificação de campos pouco explorados, de modo a colaborar com a pesquisa futura neste tomo. Palavras-chave: Shop Scheduling. Revisão Sistemática da Literatura. Taxonomia. Representação em Redes.Abstract: Since Johnson's seminal article in 1954, Shop Scheduling in Production Scheduling has become a relevant area within Operational Research, and currently hundreds of tangential works on the subject are published annually. Among the articles cited here are those dedicated to the presentation and synthesis of the state of the art of this subject, which are entitled review articles. When these articles are elaborated from an objective set of criteria, regarding the categorization of the selected articles, we have the Systematic Review of Literature (SLR). The present work performs a SLR in Shop Scheduling, based on the analysis of each manufacturing environment that composes it. There were 560 articles scrutinized based on a set of metrics, which is the basic structure of the proposed new Taxonomy of Shop Scheduling, complementary to Graham's notation, the main objective of this work. In addition to that a network representation of the results was obtained in some of the metrics used, such as the job characteristics, something previously unheard of in review studies of this subject. Moreover, another relevant point of this study lies in the identification of less explored fields in order to collaborate with future research in this matter. Keywords: Shop Scheduling. Systematic Literature Review. Taxonomy. Network Representation

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms

Diseño de una metodología de programación de producción para la reducción de costos en un flow shop híbrido flexible mediante el uso de algoritmos genéticos. Aplicación a la industria textil

La industria textil posee configuración productiva flow shop híbrido flexible, además de una serie de particularidades que hacen que los modelos estándares de programación de producción no sean aplicables. Se ha demostrado la naturaleza N-P completo del problema, por lo que el uso de meta heurísticas está bien justificado. Considerando la importancia de la reducción de los costos de fabricación en la industria textil colombiana, se propone una nueva metodología de programación de producción basada en algoritmos genéticos, que tiene presente algunas de las complejidades de la industria textil (tiempos de montaje dependientes de la secuencia, máquinas paralelas no relacionadas, cumplimiento de fechas de entrega) y permite la reducción de sus costos de producción. Al aplicarla a un problema basado en la industria textil colombiana se obtuvo una mejora promedio del 22,39% y 22,36% con respecto al método SPT y a un método aleatorio, respectivamente. Asimismo se reduce casi en un 100% el incumplimiento de fechas de entrega. Se concluye que la metodología es efectiva y que puede extenderse su aplicación a otros sectores industriales con configuración flow shop híbrido flexible. Futuros trabajos podrían considerar otras complejidades como los lotes de transferencia variables, la entrada dinámica y la maleabilidad, o aplicar la metodología a otro tipo de industrias con esta configuración productivaAbstract : Textile industry can be described by the productive configuration denominated Hybrid Flow Shop, and has a number of characteristics that make the standard scheduling models not applicable. It has been proved the NP-complete nature of the problem, so that the use of meta-heuristics is well justified. Considering the importance of reducing manufacturing costs in Colombian textile industry, a new production scheduling methodology based on genetic algorithms is proposed, which take into account some of the complexities presented in the textile industry (sequence dependent setup times, unrelated parallel machines, compliance with due dates) and allows the reduction of production costs. When the methodology was applied to a Colombian textile industry-based problem, an average improvement of 22.39% and 22.36% in comparison with the SPT method and random method, respectively, were obtained. It was also reduced almost in 100% the failure to due dates. It is concluded that the methodology is effective and can extend its application to other industries with a hybrid flow shop configuration. Future work could consider other complexities such as variable transfer batches, dynamic input and malleability, or apply the methodology to other industries in this productive configurationMaestrí

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Multiple objective decision support framework for configuring, loading and reconfiguring manufacturing cells

The potential advantages of Cellular Manufacturing Systems (CMS) are very well known in industry. However it is also shown that their performance is very sensitive to changing production requirements. The detrimental effects of changing production requirements on the performance of CMS can be alleviated by "implementing better manufacturing cell designs", "employing effective part loading strategies" and "reconfiguration". This thesis proposes a decision support framework that provides solution strategies for manufacturing cell design, cell loading and reconfiguration problems. There are three main modules in the proposed framework, named as cell formation, loading and reconfiguration. Each module can handle multiple objectives and integrates several planning and design functions, by considering the capabilities of manufacturing resources. Reconfiguration decisions are made explicitly in the proposed framework by answering the questions "when to reconfigure?" and "how to reconfigure?”. In order to answer these questions, the modules of the proposed framework are interconnected. The cell formation module creates the initial set of cells. The loading module makes the 'part to cell assignment' and the scheduling in each production period. The reconfiguration module regenerates manufacturing cells, if the loading module can not find a satisfactory solution. The cell formation module solves the part-machine cell formation problem by simultaneously considering multiple objectives and constraints. Overlapping machine capabilities and generic part process plans are taken into account in the model formulation. A new approach for the evaluation of machine capacities is also presented. Results of the comparative study show that the proposed cell formation method gives better results than several other cell-formation procedures. The manufacturing cells are formed with improved capacity utilisation levels and reduced extra machine requirements. The method is also more likely to produce independent manufacturing cells with higher flexibility. The loading module solves the 'part to cell assignment' and 'cell scheduling' problems simultaneously for cellular manufacturing applications. Alternative parts to cell and machine assignments are considered by making use of generic part process plans in the model formulation. A parametric simulation model is developed to determine cell schedules for a given part assignment scenario. The proposed loading system can assess performance of the CMS in each production period. Therefore a decision can be made about its reconfiguration. It is also shown that the efficiency of CMSs facing changing production requirements can be improved and/or sustained by using the proposed loading strategy. The reconfiguration module takes the existing cell configuration as the current solution and generates a new solution from it, to enhance its performance. The model is objective driven and considers multiple objectives and constraints within a goal programming framework. The virtual cell concept is applied as the reconfiguration strategy. In the virtual cell approach the physical locations of machines are not changed, only cell memberships of machines are updated after reconfiguration. The results of the test studies showed that it is possible to improve the performance of CMS by reconfiguring it using virtual cells. The cell formation, loading and reconfiguration problems issues discussed in this thesis are combinatorially complex multiple objective optimisation problems. Additionally simulation is used to evaluate several of the objective functions used in the modelling of loading and reconfiguration problems. Classical optimisation algorithms have various limitations in solving such problems. Therefore Tabu Search (TS) based multiple objective optimisation algorithms are developed. The proposed TS algorithms are general-purpose and can also be used to solve other multiple objective optimisation problems. The results obtained from several test problems show the proposed TS algorithms to be very effective in solving multiple objective optimisation problems. More than 500/0 improvement in solution quality is obtained in some test problems

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field