Flexible Job Shop Scheduling With Operators in Aeronautical Manufacturing: A Case Study

Borreguero-Sanchidrián, Tamara; Pulido, Raúl; García-Sánchez, Álvaro; Ortega-Mier, Míguel

doi:10.1109/access.2017.2761994

Cited by 22 publications

(14 citation statements)

References 25 publications

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“…In the DOE, three factors with two levels are used to construct 2 3 = 8 scenarios in the training set. Six factors with two levels are used to construct 2 6 = 64 test scenarios in the test set. The discrete event simulation model of the MO-DFJSP is used to evaluate fitness of the evolved SPs.…”

Section: B Simulation Modelmentioning

confidence: 99%

“…But the total dominance varies greatly depending on the different experimental configurations. Therefore, we use 2 6 full factorial design to investigate the effect of each factor. The main effects plot of six factors for total dominance are shown in Figure 10.…”

Section: C(p B) =mentioning

confidence: 99%

“…The large deviation of the performance difference is caused by the relative difference between the results of these two types of SP, but in absolute terms, both of them are small. In addition, there are nine scenarios (6,13,29,38,40,41,45,46,61) that the benchmark SP perform better than the evolved SP (#2) under the objective of T max . Because the T max target is a maximum objective rather than an average objective, it may change drastically with the change of the random number seed in the simulation.…”

Section: Insights Into the Evolved Spsmentioning

confidence: 99%

“…Thus, an operation can be processed on more than one machine and each of which has the same function. Therefore, it is similar to the flexible job shop scheduling problem [6].…”

Section: Introductionmentioning

confidence: 99%

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Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling

2019

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Nowadays, real-time scheduling is one of the key issues in cyber-physical system. In real production, dispatching rules are frequently used to react to disruptions. However, the man-made rules have strong problem relevance, and the quality of results depends on the problem itself. The motivation of this paper is to generate effective scheduling policies (SPs) through off-line learning and to implement the evolved SPs online for fast application. Thus, the dynamic scheduling effectiveness can be achieved, and it will save the cost of expertise and facilitate large-scale applications. Three types of hyper-heuristic methods were proposed in this paper for coevolution of the machine assignment rules and job sequencing rules to solve the multi-objective dynamic flexible job shop scheduling problem, including the multi-objective cooperative coevolution genetic programming with two sub-populations, the multi-objective genetic programming with two sub-trees, and the multi-objective genetic expression programming with two chromosomes. Both the training and testing results demonstrate that the CCGP-NSGAII method is more competitive than other evolutionary approaches. To investigate the generalization performance of the evolved SPs, the nondominated SPs were applied to both the training and testing scenarios to compare with the 320 types of man-made SPs. The results reveal that the evolved SPs can discover more useful heuristics and behave more competitive than the man-made SPs in more complex scheduling scenarios. It also demonstrates that the evolved SPs have a strong generalization performance to be reused in new unobserved scheduling scenarios. INDEX TERMS Scheduling, flexible job shop, hyper-heuristic, multi-objective, genetic programming. NOMENCLATURE MO-DFJSP multi-objective dynamic flexible job shop scheduling problem MAR machine assignment rule JSR job sequencing rule SP scheduling policy GEP genetic expression programming CCGP cooperative coevolution genetic programming with two sub-populations TTGP genetic programming with single population that a GP individual contains two sub-trees NSGAII nondominated sorting genetic algorithm II SPEA2 strength Pareto evolutionary algorithm 2

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Section: B Simulation Modelmentioning

confidence: 99%

Section: C(p B) =mentioning

confidence: 99%

Section: Insights Into the Evolved Spsmentioning

confidence: 99%

“…Thus, an operation can be processed on more than one machine and each of which has the same function. Therefore, it is similar to the flexible job shop scheduling problem [6].…”

Section: Introductionmentioning

confidence: 99%

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Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling

2019

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“…Make-to-order (MTO) companies are increasingly sensitized with concepts from other industries, such as flexibility, quality and the ability to adapt to consumer demand (Sultana & Ahmed, 2014). Customers demand more variety and more reliable products, as well as shorter delivery times (Romagnoli, 2015;Borreguero-Sanchidrian, Pulido, Garcia-Sanchez & Ortega-Mier, 2018). Therefore, the managers of MTO organisations are pressured to reduce inventory levels and achieve maximum use of their resources.…”

Section: Introductionmentioning

confidence: 99%

A strategic approach for bottleneck identification in make-to-order environments: A drum-buffer-rope action research based case study

Aiastui

Apaolaza

Mediavilla

2020

JIEM

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Purpose: This study is focused on integrating a strategic perspective for bottleneck selection and exploitation according to the DBR methodology. The researchers developed a systematic process of four steps for the implementation of the first two steps of TOC-DBR in Make-to-Order production systems. This process was derived from the seminal work from Goldratt, introduced key insights from Resource Based View (RBV) and Practice Based View (PBV) strategic perspectives and included original contributions from the authors in understanding what purpose should be fulfilled, what decisions should be made and how the four steps should be applied.Design/methodology/approach: Given the practical nature of the research project, action research (AR) is an appropriate methodological approach, since AR aims to contribute to academic research while helping solve real-world problems.Findings: The proposed systematic process has been successfully tested in the field on a Make To Order case company. Thus, it has been answered the research question regarding systematically selecting a bottleneck and exploiting it to enhance the competitive advantage/firm performance. In fact, the new way of selecting and exploiting its bottleneck improved the results of the company, in particular business turnover and profitability. Additionally, the research process complied with the necessary criteria to assure the research quality required for AR.Originality/value: The key contributions within the systematic process (on Make-to-Order companies) are two: (1) the criteria to select the bottleneck, far beyond a load versus capacity perspective (i.e., providing a strategic perspective that was inspired by the main concepts from the Resource Based View regarding the contribution of strategic resources to sustain competitive position) and (2) the detailed discussion on how to exploit the bottleneck, which was aligned with the Practice Based View and recognised that practices could also provide superior performance to organisations.

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Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in Smart Factories Using Dual Deep Q Networks

Lin,

Peng,

Chen

et al. 2024

Mobile Netw Appl

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Flexible Job Shop Scheduling With Operators in Aeronautical Manufacturing: A Case Study

Cited by 22 publications

References 25 publications

Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling

Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling

A strategic approach for bottleneck identification in make-to-order environments: A drum-buffer-rope action research based case study

Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in Smart Factories Using Dual Deep Q Networks

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