Abstract

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.

Highlights

  • IntroductionThe manufacturing industry has seen constant growth and change. The fourth industrial revolution (Industry 4.0) and relevant advanced technologies have reshaped modern manufacturing systems and enhanced their ability to meet customers’ higher expectations, such as for more customised products in a shorter time (Khan and Turowski, 2016; Zhou et al, 2017)

  • Over the years, the manufacturing industry has seen constant growth and change

  • The current study proposed an events management algorithm to dynamically calculate the rescheduling time

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Summary

Introduction

The manufacturing industry has seen constant growth and change. The fourth industrial revolution (Industry 4.0) and relevant advanced technologies have reshaped modern manufacturing systems and enhanced their ability to meet customers’ higher expectations, such as for more customised products in a shorter time (Khan and Turowski, 2016; Zhou et al, 2017). The frequent changes in the industry environment require production systems to perform in highly dynamic and stochastic scenarios (Ouelhadj and Petrovic, 2009). Under these circumstances, unexpected events may occur, causing the initial schedule to be changed because it does not fit the new scenario (Barták and Vlk, 2017). In such cases, rescheduling is mandatory to mitigate the impact of disruptions and recover the original solution (Salido et al, 2017).

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