Identification of the critical reaction times for re-scheduling flexible job shops for different types of unexpected events

Psarommatis, Foivos; Gharaei, Ali; Kiritsis, Dimitris

doi:10.1016/j.procir.2020.03.038

Cited by 30 publications

(4 citation statements)

References 16 publications

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“…The proposed methodology utilises two key parameters for each event action: the response time (RT) and the delay response time (DRT) (Psarommatis et al, 2020a). RT refers to the minimum time that is required from the moment of the event until the time that the mitigation action is ready to be released to the shopfloor, whereas DRT refers to the maximum time that an event action can be delayed until its release to the shopfloor becomes mandatory.…”

Section: Events Management Algorithmmentioning

confidence: 99%

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

et al. 2021

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Nowadays, the manufacturing industry is constantly changing. Production systems must operate in a highly dynamic environment where unexpected events could occur and create disruption, making rescheduling inevitable for manufacturing companies. Rescheduling models are fundamental to the robustness of production processes. This paper proposes a model to address rescheduling caused by unexpected events, aiming to achieve the zero-defect manufacturing (ZDM) concept. The goal of the model is to incorporate traditional and ZDM–oriented events into one methodology to calculate when the next rescheduling will be performed to effectively react to unexpected events. The methodology relies on the definition of two key time parameters for each event type: event response time (RT) and event delay response time (DRT). Based on these parameters, an event management algorithm is designed to identify the optimal rescheduling solution. The DRT parameter is calculated based on a multi-parametric dynamic formula to capture the dynamics of production. Moreover, ANOM, and ANOVA methods are used to analyse the behaviour of the developed method and to assess the level of robustness of the proposed approach. Finally, a case study based on real production scenarios is conducted, a series of simulation experiments are performed, and comparisons with other rescheduling policies are presented. The results demonstrate the effectiveness of the proposed event management algorithm for managing rescheduling.

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Section: Events Management Algorithmmentioning

confidence: 99%

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

et al. 2021

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“…This research area remains practically unexplored with very few contributions. Psarommatis et al [27] recognize production order rescheduling in contemporary job shops as an inevitable and critical phenomenon, and center their study on identifying the critical reaction time for the events that trigger it so that productivity and costs remain within acceptable ranges by considering that four related factors may lead to confusion and loss of productivity: on the one hand, new order events, faulty parts and machine breakdown; on the other hand, the number of daily re-schedulings. Psarommatis et al [28] base their research on the more frequent need to reschedule the job-shop in ZDM because this strategy imposes that all events during production have their counteraction to mitigate these events and focus their research on improving the quality of the solution in line with this in flexible JSSP by a metaheuristic method, namely Tabu search.…”

Section: The Zdm Strategy In the Job-shop Environmentmentioning

confidence: 99%

“…disrupt normal manufacturing system operations and, subsequently, impose risks, extra costs and less efficient systems. Thus, changes are made to the current schedule and, therefore, re-scheduling is required to include correction actions and to allow manufacturing systems to optimally run in its optimal way [27]. By introducing the DT, further convergence between the physical and virtual spaces of jobshop scheduling can be achieved, which enables dynamic scheduling by triggering timely rescheduling whenever needed [24].…”

Section: Proposalmentioning

confidence: 99%

Smart Digital Twin for ZDM-based job-shop scheduling

Serrano-Ruiz

Mula

2021

2021 IEEE International Workshop on Metrology for Industry 4.0 &Amp; IoT (MetroInd4.0&IoT)

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The growing digitization of manufacturing processes is revolutionizing the production job-shop by leading it toward the Smart Manufacturing (SM) paradigm. For a process to be smart, it is necessary to combine a given blend of data technologies, information and knowledge that enable it to perceive its environment and to autonomously perform actions that maximize its success possibilities in its assigned tasks. Of all the different ways leading to this transformation, both the generation of virtual replicas of processes and applying artificial intelligence (AI) techniques provide a wide range of possibilities whose exploration is today a far from negligible sources of opportunities to increase industrial companies' competitiveness. As a complex manufacturing process, production order scheduling in the job-shop is a necessary scenario to act by implementing these technologies. This research work considers an initial conceptual smart digital twin (SDT) framework for scheduling job-shop orders in a zero-defect manufacturing (ZDM) environment. The SDT virtually replicates the job-shop scheduling issue to simulate it and, based on the deep reinforcement learning (DRL) methodology, trains a prescriber agent and a process monitor. This simulation and training setting will facilitate analyses, optimization, defect and failure avoidance and, in short, decision making, to improve job-shop scheduling.

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“…In that regard, there are two Sustainability 2023, 15, 12251 2 of 20 different approaches for ZDM implementation: (i) product-oriented ZDM and (ii) processoriented ZDM [12]. The literature of the last decade shows a movement towards productoriented approaches, but without proof that they are superior to the process-oriented approaches [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Analysis for Mapping Product-Oriented and Process-Oriented Zero-Defect Manufacturing (ZDM) in the Industry 4.0 Era

Psarommatis

May

2023

Sustainability

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Quality is a key aspect in the era of Industry 4.0. Zero-defect manufacturing (ZDM) as the latest quality assurance approach. It can be implemented in two different approaches: the product-oriented and the process-oriented ZDM. It is important to know how and when to consider adopting one approach over the other. To achieve that there is the need for analyzing the differences of the two ZDM approaches. However, the current literature lacks a detailed analysis and comparison of these two approaches to ZDM implementation. Earlier studies on the topic have adopted one of these approaches over the other without evaluating how it fits with specific cases. The literature of the last decade indicates a movement towards product-oriented approaches, but it has not shown proof why product oriented was used over process oriented. Guided by these gaps, this research work creates a model for quantifying the effects of the implementation of both the product-oriented and process-oriented ZDM approaches. The proposed model considers all the critical parameters that affect the problem and serves as an assisting tool to engineers during the design or re-configure manufacturing systems, for choosing the most efficient ZDM approach for their specific cases. The robustness of the model was analyzed using the design of experiments method. The results from both the designed experiments and an industrial use case illustrate that in most cases, product-oriented ZDM performs better than the process-oriented approach. Nevertheless, in our analysis, we also highlight strong interactions between some factors that make the selection between product-oriented and process-oriented ZDM difficult and complex.

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Identification of the critical reaction times for re-scheduling flexible job shops for different types of unexpected events

Cited by 30 publications

References 16 publications

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

Smart Digital Twin for ZDM-based job-shop scheduling

A Systematic Analysis for Mapping Product-Oriented and Process-Oriented Zero-Defect Manufacturing (ZDM) in the Industry 4.0 Era

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