Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

Abstract

Numerous organizations are striving to maximize the profit of their businesses by the effective implementation of competitive advantages including cost reduction, quick delivery, and unique high-quality products. Effective production-scheduling techniques are methods that many firms use to attain these competitive advantages. Implementing scheduling techniques in high-mix low-volume (HMLV) manufacturing industries, especially in Industry 4.0 environments, remains a challenge, as the properties of both parts and processes are dynamically changing. As a reaction to these challenges in HMLV Industry 4.0 manufacturing, a newly advanced and effective real-time production-scheduling decision-support system model was developed. The developed model was implemented with the use of robotic process automation (RPA), and it comprises a hybrid of different advanced scheduling techniques obtained as the result of analytical-hierarchy-process (AHP) analysis. The aim of this research was to develop a method to minimize the total production process time (total make span) by considering the results of risk analysis of HMLV manufacturing in Industry 4.0 environments. The new method is the combination of multi-broker (MB) optimization and a genetics algorithm (GA) that uses general key process indicators (KPIs) that are easy to measure in any kind of production. The new MB–GA method is compatible with industry 4.0 environments, so it is easy to implement. Furthermore, MB–GA deals with potential risk during production, so it can provide more accurate results. On the basis of survey results, 16% of the asked companies could easily use the new scheduling method, and 43.2% of the companies could use it after a little modification of production.

Developing a Decision Support System for Exam Scheduling Problem Using Genetic Algorithm

“…In the first stage, all viable paths are generated through an enumeration algorithm; then, in the second stage, an Integer Programming model was designed to select the optimum routes from the appropriate set of routes, and an interactive DSS was developed [29]. Kocsi et al (2020), the aim of the studies is to develop a method that will minimize the total production process time by taking into account the risk analysis results of production in the Industry 4.0 environment. A hybrid approach has been proposed to solve the problem, and a real-time production scheduling DSS model has been developed [30].…”

“…Kocsi et al (2020), the aim of the studies is to develop a method that will minimize the total production process time by taking into account the risk analysis results of production in the Industry 4.0 environment. A hybrid approach has been proposed to solve the problem, and a real-time production scheduling DSS model has been developed [30]. Ko et al (2010) conducted a study on an appropriate production planning problem to ensure effective resource utilization and developed a GA-based DSS to help production managers organize their production plans [31].…”

Lab Scale Implementation of Industry 4.0 for an Automatic Yogurt Filling Production System—Experimentation, Modeling and Process Optimization

“…Due to the powerful features of Industry 4.0, industry 4.0-based smart real-time production controlling and scheduling was discussed in several scientific works [11,[30][31][32][33][34][35][36][37]. For example, Rossit et al (2019) introduced a smart scheduling mechanism to mitigate the production incidents, interruption, and instability induced by the real-time autonomous behavior of the entities of the production system.…”

“…The aim of the model is to minimize the total production process time (i.e., makespan). The developed model encompasses a hybrid of different scheduling techniques, where the best alternative will be selected using an analytical hierarchy process (AHP) analysis [30]. Kianpour et al (2021) Developed an Industry 4.0-based job shop scheduling model.…”

To digitalize or not? Navigating and merging human- and technology perspectives in production planning and control

“…Further, certain production characteristics can make the use of I4.0 technologies complex, e.g., implementing fitting scheduling in high-mix low-volume manufacturing remains a challenge, as the properties of both parts and processes are dynamically changing [16]. Among criteria for real-time decision-support system for scheduling are IoT, inventory levels in real time, sensors, and actuators [16]. Big data analytics still have a low implementation level in manufacturing, emphasizing companies' uncertainties for technical requirements and anticipated benefits of I4.0 [17].…”