Bounded dynamic programming approach to minimize makespan in the blocking flowshop problem with sequence dependent setup times

This paper aims at presenting an algorithm for solving the blocking flow shop problem with sequence dependent setup times (BFSP-SDST) with minimization of the makespan. In order to do so, we propose an adapted Bounded Dynamic Programming (BDP-SN) algorithm as solution method, since the problem itself does not present a significant number of sources in the state-of-art references and also because Dynamic Programming and its variants have been resurfacing in the flowshop literature. Therefore, we apply the modified method to two sets of problems and compare the results computationally and statistically for instances with a MILP and a B&B method for at most 20 jobs and 20 machines. The results show that BDP-SN is promising and outperforms both MILP and B&B within the established time limit. In addition, some suggestions are made in order to improve the method and employ it in parallel research regarding other branches of machine scheduling

Aproximações heurísticas para um problema de escalonamento do tipo flexible job-shop

Mestrado em Engenharia e Gestão IndustrialEste trabalho aborda um novo tipo de problema de escalonamento que pode ser encontrado em várias aplicações do mundo-real, principalmente na indústria transformadora. Em relação à configuração do shop floor, o problema pode ser classificado como flexible job-shop, onde os trabalhos podem ter diferentes rotas ao longo dos recursos e as suas operações têm um conjunto de recursos onde podem ser realizadas. Outras características de processamento abordadas são: datas possíveis de início, restrições de precedência (entre operações de um mesmo trabalho ou entre diferentes trabalhos), capacidade dos recursos (incluindo paragens, alterações na capacidade e capacidade infinita) e tempos de setup (que podem ser dependentes ou independentes da sequência). O objetivo é minimizar o número total de trabalhos atrasados. Para resolver o novo problema de escalonamento proposto um modelo de programação linear inteira mista é apresentado e novas abordagens heurísticas são propostas. Duas heurísticas construtivas, cinco heurísticas de melhoramento e duas metaheurísticas são propostas. As heurísticas construtivas são baseadas em regras de ordenação simples, onde as principais diferenças entre elas dizem respeito às regras de ordenação utilizadas e à forma de atribuir os recursos às operações. Os métodos são designados de job-by-job (JBJ), operation-by-operation (OBO) e resource-by-resource (RBR). Dentro das heurísticas de melhoramento, a reassign e a external exchange visam alterar a atribuição dos recursos, a internal exchange e a swap pretendem alterar a sequência de operações e a reinsert-reassign é focada em mudar, simultaneamente, ambas as partes. Algumas das heurísticas propostas são usadas em metaheurísticas, nomeadamente a greedy randomized adaptive search procedure (GRASP) e a iterated local search (ILS). Para avaliar estas abordagens, é proposto um novo conjunto de instâncias adaptadas de problemas de escalonamento gerais do tipo flexible job-shop. De todos os métodos, o que apresenta os melhores resultados é o ILS-OBO obtendo melhores valores médios de gaps em tempos médios inferiores a 3 minutos.This work addresses a new type of scheduling problem which can be found in several real-world applications, mostly in manufacturing. Regarding shop floor configuration, the problem can be classified as flexible job-shop, where jobs can have different routes passing through resources and their operations have a set of eligible resources in which they can be performed. The processing characteristics addressed are release dates, precedence constraints (either between operations of the same job or between different jobs), resources capacity (including downtimes, changes in capacity, and infinite capacity), and setup times, which can be sequence-dependent or sequence-independent. The objective is to minimise the total number of tardy jobs. To tackle the newly proposed flexible job-shop scheduling problem (FJSP), a mixed integer linear programming model (MILP) is presented and new heuristic approaches are put forward. Three constructive heuristics, five improvement heuristics, and two metaheuristics are proposed. The constructive heuristics are based on simple dispatching rules, where the main differences among them concern the used dispatching rules and the way resources are assigned. The methods are named job-by-job (JBJ), operation-by-operation (OBO) and resource-by-resource (RBR). Within improvement heuristics, reassign and external exchange aim to change the resources assignment, internal exchange and swap intend changing the operations sequence, and reinsert-reassign is focused in simultaneously changing both parts. Some of the proposed heuristics are used within metaheuristic frameworks, namely greedy randomized adaptive search procedure (GRASP) and iterative local search (ILS). In order to evaluate these approaches, a new set of benchmark instances adapted from the general FJSP is proposed. Out of all methods, the one which shows the best average results is ILS-OBO obtaining the best average gap values in average times lower than 3 minutes

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

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

Four decades of research on the open-shop scheduling problem to minimize the makespan

One of the basic scheduling problems, the open-shop scheduling problem has a broad range of applications across different sectors. The problem concerns scheduling a set of jobs, each of which has a set of operations, on a set of different machines. Each machine can process at most one operation at a time and the job processing order on the machines is immaterial, i.e., it has no implication for the scheduling outcome. The aim is to determine a schedule, i.e., the completion times of the operations processed on the machines, such that a performance criterion is optimized. While research on the problem dates back to the 1970s, there have been reviving interests in the computational complexity of variants of the problem and solution methodologies in the past few years. Aiming to provide a complete road map for future research on the open-shop scheduling problem, we present an up-to-date and comprehensive review of studies on the problem that focuses on minimizing the makespan, and discuss potential research opportunities

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

No Optimisation Without Representation: A Knowledge Based Systems View of Evolutionary/Neighbourhood Search Optimisation

Centre for Intelligent Systems and their ApplicationsIn recent years, research into ‘neighbourhood search’ optimisation techniques such as simulated annealing, tabu search, and evolutionary algorithms has increased apace, resulting in a number of useful heuristic solution procedures for real-world and research combinatorial and function optimisation problems. Unfortunately, their selection and design remains a somewhat ad hoc procedure and very much an art. Needless to say, this shortcoming presents real difficulties for the future development and deployment of these methods. This thesis presents work aimed at resolving this issue of principled optimiser design. Driven by the needs of both the end-user and designer, and their knowledge of the problem domain and the search dynamics of these techniques, a semi-formal, structured, design methodology that makes full use of the available knowledge will be proposed, justified, and evaluated. This methodology is centred around a Knowledge Based System (KBS) view of neighbourhood search with a number of well-defined knowledge sources that relate to specific hypotheses about the problem domain. This viewpoint is complemented by a number of design heuristics that suggest a structured series of hillclimbing experiments which allow these results to be empirically evaluated and then transferred to other optimisation techniques if desired. First of all, this thesis reviews the techniques under consideration. The case for the exploitation of problem-specific knowledge in optimiser design is then made. Optimiser knowledge is shown to be derived from either the problem domain theory, or the optimiser search dynamics theory. From this, it will be argued that the design process should be primarily driven by the problem domain theory knowledge as this makes best use of the available knowledge and results in a system whose behaviour is more likely to be justifiable to the end-user. The encoding and neighbourhood operators are shown to embody the main source of problem domain knowledge, and it will be shown how forma analysis can be used to formalise the hypotheses about the problem domain that they represent. Therefore it should be possible for the designer to experimentally evaluate hypotheses about the problem domain. To this end, proposed design heuristics that allow the transfer of results across optimisers based on a common hillclimbing class, and that can be used to inform the choice of evolutionary algorithm recombination operators, will be justified. In fact, the above approach bears some similarity to that of KBS design. Additional knowledge sources and roles will therefore be described and discussed, and it will be shown how forma analysis again plays a key part in their formalisation. Design heuristics for many of these knowledge sources will then be proposed and justified. This methodology will be evaluated by testing the validity of the proposed design heuristics in the context of two sequencing case studies. The first case study is a well-studied problem from operational research, the flowshop sequencing problem, which will provide a through test of many of the design heuristics proposed here. Also, an idle-time move preference heuristic will be proposed and demonstrated on both directed mutation and candidate list methods. The second case study applies the above methodology to design a prototype system for resource redistribution in the developing world, a problem that can be modelled as a very large transportation problem with non-linear constraints and objective function. The system, combining neighbourhood search with a constructive algorithm which reformulates the problem to one of sequencing, was able to produce feasible shipment plans for problems derived from data from the World Health Organisation’s TB programme in China that are much larger than those problems tackled by the current ‘state-of-the-art’ for transportation problems

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