Review on flexible job shop scheduling

Xie, Jin; Gao, Liang; Peng, Kunkun; Li, Xinyu; Li, Haoran

doi:10.1049/iet-cim.2018.0009

Cited by 144 publications

(55 citation statements)

References 171 publications

(190 reference statements)

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“…Since the early 1960s, the job shop scheduling problem (JSP) has been considered as one of the most important NPhard combinatorial optimization problems. The flexible job shop scheduling problem (FJSP), which is an extension of JSP, increases the flexibility and complexity of scheduling (Xie et al 2019). The real production environment, such as production conditions and customer requirements, becomes more and more complex with the increasing popularity of personalized requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Most of them, nevertheless, ignored the important influence of fixtures, such as loading and unloading time, which is an essential part in the production time and should be therefore not neglected. Among recent studies, most scheduling studies only focus on the machine resource (Wu and Wu 2017;Xie et al 2019;Li et al 2019;Nesello et al 2018). Jobs, however, need to be processed with not only machines but also other types of resource, such as fixtures and measuring tools.…”

Section: Introductionmentioning

confidence: 99%

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An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

Peng

Xiao

et al. 2020

J Intell Manuf

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Many manufacturing systems need more than one type of resource to co-work with. Commonly studied flexible job shop scheduling problems merely consider the main resource such as machines and ignore the impact of other types of resource. As a result, scheduling solutions may not put into practice. This paper therefore studies the dual resource constrained flexible job shop scheduling problem when loading and unloading time (DRFJSP-LU) of the fixtures is considered. It formulates a multi-objective mathematical model to jointly minimize the makespan and the total setup time. Considering the influence of resource requirement similarity among different operations, we propose a similarity-based scheduling algorithm for setup-time reduction (SSA4STR) and then an improved non-dominated sorting genetic algorithm II (NSGA-II) to optimize the DRFJSP-LU. Experimental results show that the SSA4STR can effectively reduce the loading and unloading time of fixtures while ensuring a level of makespan. The experiments also verify that the scheduling solution with multiple resources has a greater guiding effect on production than the scheduling result with a single resource.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

Peng

Xiao

et al. 2020

J Intell Manuf

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“…Job-Shop Scheduling Problem (JSSP) [1][2][3][4] is a common problem of optimization in the field of computer science, where resources are distributed at different times by the most suitable jobs. A Job Shop can be described as a work location comprised of several individual general purpose work stations that are responsible in performing several varied tasks/jobs.…”

Section: Introductionmentioning

confidence: 99%

“…Presently, many Artificial Intelligence techniques have been used in dynamic task scheduling. Genetic algorithms, Artificial neural networks, Fuzzy logic are the most prominent [2,8] techniques that can be found in the literature. Reinforcement Learning is a recently emerged technology and a compelling paradigm which has been experimented to solve the issue of optimal dynamic task scheduling.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning in Dynamic Task Scheduling: A Review

2020

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Scheduling is assigning shared resources over time to efficiently complete the tasks over a given period of time. The term is applied separately for tasks and resources correspondingly in task scheduling and resource allocation. Scheduling is a popular topic in operational management and computer science. Effective schedules ensure system efficiency, effective decision making, minimize resource wastage and cost, and enhance overall productivity. It is generally a tedious task to choose the most accurate resources in performing work items and schedules in both computing and business process execution. Especially in real-world dynamic systems where multiple agents involve in scheduling various dynamic tasks is a challenging issue. Reinforcement Learning is an emergent technology which has been able to solve the problem of the optimal task and resource scheduling dynamically. This review paper is about a research study that focused on Reinforcement Learning techniques that have been used for dynamic task scheduling. The paper addresses the results of the study by means of the state-of-theart on Reinforcement learning techniques used in dynamic task scheduling and a comparative review of those techniques.

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“…Одной из важнейших задач, возникающих в процессе функционирования перенастраиваемого производства, является задача оптимизации порядка исполнения заказов. Решение этой задачи позволяет добиться минимизации затрат по переналадке производственного оборудования, процессов и условий [20][21][22][23][24][25][26]. В наиболее простой свой форме она сводится к задаче коммивояжера (Travelling Salesman Problem -TSP [27]).…”

Section: Introductionunclassified

Experimental research of the application of modern combinatorial optimization solvers to the accompanying manufacturing optimization problem

Belousov¹,

Ivanko²

2019

Vestn. Udmurt. Univ. Mat. Mekh. Komp’yut. Nauki

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В работе формализуется задача оптимизации сопутствующего производства на гибких или реконфигурируемых производствах. В рассматриваемой постановке на входе задан набор обязательных изделий, требуется решить две взаимосвязанные подзадачи: 1) для каждого изделия из набора обязательных сформировать группу дополнительных изделий, которые могут быть произведены без изменения состояния производства, и 2) определить порядок переналадок производства между группами дополнительных изделий, а также «точки входа и выхода» в каждую из групп. В настоящей работе указанные подзадачи рассматриваются последовательно: первая подзадача сведена к задаче поиска клики максимального веса в ориентированном графе, вторая-к кластерной задаче коммивояжера. В ходе масштабных вычислительных экспериментов изучен выигрыш от применения эффективных современных методов решения обеих подзадач в сравнении с жадным решением, моделирующим рациональные действия человека-оператора в условиях большой размерности исходной комплексной задачи и ограниченного времени, имеющегося для ее решения. Ключевые слова: оптимизация сопутствующего производства, гибкие и перенастраиваемые производства, клика максимального веса, кластерная задача коммивояжера.

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Review on flexible job shop scheduling

Cited by 144 publications

References 171 publications

An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

Reinforcement Learning in Dynamic Task Scheduling: A Review

Experimental research of the application of modern combinatorial optimization solvers to the accompanying manufacturing optimization problem

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