An IoT-Enabled Real-Time Dynamic Scheduler for Flexible Job Shop Scheduling (FJSS) in an Industry 4.0-Based Manufacturing Execution System (MES 4.0)

Abstract

The most challenging aspect identified in this study revolves around effectively managing machine breakdowns to ensure uninterrupted production. This paper presents a real-time dynamic scheduling model that addresses the challenges of the Flexible Job Shop Scheduling Problem (FJSSP) while considering the occurrence of random machine breakdowns. An improved hybrid metaheuristic and rule-based multi-strategy technique has been proposed that regenerates an optimized dynamic schedule when a random machine is interrupted. The proposed technique establishes that the presence of real-time system updates from IoT devices will improve scheduling decisions. The proposed methodology’s efficacy is showcased through an extensive computational investigation encompassing 9 benchmark problems and a real-world case study, considering three performance objectives (Robustness, Stability and Compound Effectiveness). The results have been compared with three related techniques from literature. The proposed technique gives better results in most cases and can be adopted in increasing the performance of Manufacturing Execution Systems in an Industry 4.0 setup (MES 4.0).

Keywords

• Industry 4.0
• digital transformation
• manufacturing execution systems (MES)
• job shop scheduling problem (JSSP)
• flexible job shop scheduling problem (FJSSP)
• dynamic job shop scheduling problem (DJSSP)