Field-synchronized Digital Twin framework for production scheduling with uncertainty

Elisa Negri,Marco Macchi,Jaskaran Singh,Jay Lee,Vibhor Pandhare,Laura Cattaneo

doi:10.1007/s10845-020-01685-9

Abstract

Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the industrial equipment in real-time and take them into account when elaborating the best production schedules. To this end, the article proposes a proof-of-concept of a simheuristics framework for robust scheduling applied to a Flow Shop Scheduling Problem. The framework is composed of genetic algorithms for schedule optimization and discrete event simulation and is synchronized with the field through a Digital Twin (DT) that employs an Equipment Prognostics and Health Management (EPHM) module. The contribution of the EPHM module inside the DT-based framework is the real time computation of the failure probability of the equipment, with data-driven statistical models that take sensor data from the field as input. The viability of the framework is demonstrated in a flow shop application in a laboratory environment.

Highlights

Modern day industries need to compete for profitability and customer satisfaction in a challenging environment with ris-Industry 4.0 encompasses multiple evolving technology umbrellas, one of which is Cyber-Physical Systems (CPS)
Looking at the Digital Twin development in manufacturing, the proposed synchronized simulation can be inserted in the classification by (Kritzinger et al 2018) as a Digital Shadow, according to the capabilities shown by the experiments and implementation presented in this article: the proposed framework does not feedback into the field of decision-making autonomously; the operator is instead asked to manually choose the preferred schedule among the best ones proposed by the human–machine interface
The present article brings forward an innovative framework for robust scheduling that embeds traditional instruments such as simulation and metaheuristics (GA) with Industry 4.0 data-driven capabilities

Summary

Introduction

Modern day industries need to compete for profitability and customer satisfaction in a challenging environment with ris-Industry 4.0 encompasses multiple evolving technology umbrellas, one of which is Cyber-Physical Systems (CPS). Open and standardized communication protocols, and computational convenience have led to its development (Baheti and Gill 2011; Lee et al 2015b; Leitão et al 2016). Integrating CPS with production, logistics, maintenance, and other services in the current industrial practice holds the potential to transform today’s factories into Industry 4.0-based factories. This can foster significant economic growth and high responsiveness to everchanging operating conditions, leading to major evolutions in the decision-making processes. The present article focuses on production scheduling. It is an activity which has sustained

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