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Job shop rescheduling with rework and reconditioning in Industry 4.0: an event-driven approach

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Abstract

In this paper, we investigate the impact of rescheduling policies in the event of both rework and reconditioning in job shop manufacturing systems. Since these events occur in unplanned and disrupting manner, to address them properly, it is required to manage real-time information and to have flexible reaction capacity. These capabilities, of data acquisition and robotics, are provided by Industry 4.0 Technologies. However, to take full advantage of those capabilities, it is imperative to have efficient decision-making processes to deliver adequate corrective actions. In this sense, we propose an event-driven rescheduling approach. This approach consists of an architecture that integrates information acquisition, optimization process, and rescheduling planning. We study the performance of the system with several algorithms with two performance criteria, namely, (i) relative performance deviation (RPD) in terms of objective function and (ii) schedule stability. We also propose a hybrid policy that combines full rescheduling regeneration with stability-oriented strategies aimed to balance both criteria. We conducted extensive computational tests with instances from the literature under different scenarios. The results show that a sophisticated algorithm can obtain better quality schedules in terms of the objective function but at the expense of sacrificing stability. Finally, we analyze and discuss the results and provide insights for its use and implementation.

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Availability of data and material

The instances are available in the supplementary material.

Code availability

The code is custom code.

Notes

  1. Although this section can be skipped without loss of continuity, we leave it here as important aspects of the modeling approach regarding rework are presented.

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Acknowledgements

Gonzalo Mejía would like to express his gratitude to Universidad de La Sabana for the time and resource usage for this project.

Funding

Daniel A. Rossit and Gonzalo Mejía have been funded by CYTED under the project Red Iberoamericana Industria 4.0, grant number 319RT0574.

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Authors and Affiliations

  1. Faculty of Engineering, Universidad de La Sabana, Campus Universitario Puente del Común, Chía, Colombia

    Gonzalo Mejía

  2. Facultad de Ingeniería, Pontificia Universidad Javeriana, Bogotá, Colombia

    Carlos Montoya & Stevenson Bolívar

  3. Departamento de Ingeniería, Universidad Nacional del Sur, Bahía Blanca, Argentina

    Daniel Alejandro Rossit

  4. INMABB, Universidad Nacional del Sur and CONICET, Bahía Blanca, Argentina

    Daniel Alejandro Rossit

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  1. Gonzalo Mejía
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  2. Carlos Montoya
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  3. Stevenson Bolívar
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  4. Daniel Alejandro Rossit
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Correspondence to Gonzalo Mejía.

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Mejía, G., Montoya, C., Bolívar, S. et al. Job shop rescheduling with rework and reconditioning in Industry 4.0: an event-driven approach. Int J Adv Manuf Technol 119, 3729–3745 (2022). https://doi.org/10.1007/s00170-021-08163-3

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