A branch and bound algorithm for the job-shop scheduling problem

Brucker, Peter; Jurisch, Bernd; Sievers, Bernd

doi:10.1016/0166-218x(94)90204-6

Cited by 397 publications

(211 citation statements)

References 10 publications

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“…A great variety of issues have been addressed, including single machine scheduling, parallel machine scheduling, flow shop and flexible flow shop scheduling, job shop scheduling, open shop scheduling and many different models, deterministic or stochastic, have been built (Allahverdi 2015). Many approaches have been proposed, from the early mathematical programming (Blazewicz, Dror, and Weglarz 1991) and branch-and-bound technique (Brucker, Jurisch, and Sievers 1994) for smallsize problems to various approximation methods such as local search (Vaessens, Aarts, and Lenstra 1996) and AI (Wang 2005;Çaliş and Bulkan 2015), etc. Especially, because of the dynamic nature of manufacturing systems, dynamic scheduling, which can effectively tackle various disturbances, has received much attention since the 1950s (Johnson 1954).…”

Section: Workhop Schedulingmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

211

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For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.

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Section: Workhop Schedulingmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

211

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“…Estas propriedades definem a maneira como a alteração da ordem de uma tarefa em um sequenciamento pode influenciar na solução corrente. As propriedades definidas por Grabowski, Nowicki e Zdrzalka [5] também são utilizadas na resolução do problema de Job Shop [1], [6] e [3].…”

Section: Introductionunclassified

Algoritmos Branch e Bound para o problema de sequenciamento em uma única m´máquina

Ferreira¹,

Previero²

2015

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Resumo: Neste trabalho apresentamos o problema de minimização do makespan para o sequenciamento de tarefas em umaúnica máquina. Para este problema, cada tarefa está associada a um tempo de execução, um instante de disponibilidade de entrada no sistema e o tempo em que a tarefa fica no sistema após o seu processamento. Para a resolução do problema foram utilizados dois algoritmos baseados no método Branch e Bound e os resultados foram comparados. O primeiro algoritmo utiliza as estratégias de Carlier [2] e o segundo as de Grabowski, Nowicki e Zdrzalka [5]. O limitante inferior para o Branch e Boundé obtido através do Algoritmo Preemptivo de Jackson.Palavras-chave: Sequenciamento em umaúnica máquina, escalonamento de tarefas 1 Introdução O problema de sequenciamento em uma máquina (PSUM) consiste em ordenar um conjunto de tarefas para serem processadas em umaúnica máquina de modo a minimizar o tempo de conclusão daúltima tarefa a sair do sistema. Neste problema, cada tarefa está associada a um tempo de execução na máquina, um instante de disponibilidade de entrada no sistema e o tempo em que a tarefa fica no sistema após o seu processamento. Uma tarefa está finalizada assim que sair do sistema. Após o seu início, uma tarefaé processada até o seu término, sem interrupção.O problema definido acima tem várias aplicações em sistemas de produção. Os resultados obtidos para o PSUM não só fornecem o conhecimento para o problema em si, como também podem proporcionar heurísticas a ambientes mais complexos, como o Job Shop ou o problema de sequenciamento de máquinas em paralelo. Para o Job Shop, podemos usar o PSUM para obter limitantes. Neste caso, uma máquinaé escolhida e através da relaxação das restrições de outras máquinas obtemos limitantes para o problema. Isso nos ajuda a resolver uma das principais dificuldades do método Branch e Bound queé a ausência de bons limitantes inferiores para podar aárvore de enumeração tão cedo quanto o possível.O

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“…This is the case of the work by Brucker and Thiele (1996), for example, which relies on an earlier solutions introduced in Brucker et al (1994). Another example is the more recent work of Vela et al (2009) and González et al (2009a), which proposes effective heuristic procedures based on genetic algorithms and local search.…”

Section: Introductionmentioning

confidence: 99%

Solving job shop scheduling with setup times through constraint-based iterative sampling: an experimental analysis

Oddi

Rasconi

Cesta

et al. 2011

Ann Math Artif Intell

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This paper presents a heuristic algorithm for solving a job-shop scheduling problem with sequence dependent setup times and min/max separation constraints among the activities (SDST-JSSP/max). The algorithm relies on a core constraintbased search procedure, which generates consistent orderings of activities that require the same resource by incrementally imposing precedence constraints on a temporally feasible solution. Key to the effectiveness of the search procedure is a conflict sampling method biased toward selection of most critical conflicts and coupled with a non-deterministic choice heuristic to guide the base conflict resolution process. This constraint-based search is then embedded within a larger iterative-sampling search framework to broaden search space coverage and promote solution optimization. The efficacy of the overall heuristic algorithm is demonstrated empirically both on a set of previously studied job-shop scheduling benchmark problems with sequence dependent setup times and by introducing a new benchmark with setups and generalized precedence constraints.

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A branch and bound algorithm for the job-shop scheduling problem

Cited by 397 publications

References 10 publications

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Algoritmos Branch e Bound para o problema de sequenciamento em uma única m´máquina

Solving job shop scheduling with setup times through constraint-based iterative sampling: an experimental analysis

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