Inustry 4.0-Driven Development of Optimization Algortihms: A Systematic Overview
The Fourth Industrial Revolution means the digital transformation of production systems. Cyber-physical systems allow for the horizontal and vertical integration of these production systems as well as the exploitation of the benefits via optimization tools. This article reviews the impact of Industry 4.0 solutions concerning optimization tasks and optimization algorithms, in addition to the identification of the new R&D directions driven by new application options. The basic organizing principle of this overview of the literature is to explore the requirements of optimization tasks, which are needed to perform horizontal and vertical integration. This systematic review presents content from 900 articles on Industry 4.0 and optimization as well as 388 articles on Industry 4.0 and scheduling. It is our hope that this work can serve as a starting point for researchers and developers in the field.
Estimation of machine setup and changeover times by survival analysis
The losses associated with changeovers are becoming more significant in manufacturing due to the high variance of products and requirements for just-in-time production. The study is based on the single minute exchange of die (SMED) philosophy, which aims to reduce changeover times. We introduced a method for the analysis of these losses based on models that estimate the product- and operator-dependent changeover times using survival analysis. The root causes of the losses are identified by significance tests of the utilized Cox regression models. The resulting models can be used to design a performance management system that considers the stochastic nature of the work of the operators. An anonymized manufacturing example related to the setup of crimping and wire cutting machines demonstrates the applicability of the method.
Real-Time Locating System in Production Management
Real-time monitoring and optimization of production and logistics processes significantlyimprove the efficiency of production systems. Advanced production management solutions requirereal-time information about the status of products, production, and resources. As real-time locatingsystems (also referred to as indoor positioning systems) can enrich the available information, thesesystems started to gain attention in industrial environments in recent years. This paper providesa review of the possible technologies and applications related to production control and logistics,quality management, safety, and efficiency monitoring. This work also provides a workflow to clarifythe steps of a typical real-time locating system project, including the cleaning, pre-processing, andanalysis of the data to provide a guideline and reference for research and development of indoorpositioning-based manufacturing solutions.
Fuzzy activity time-based model predictive control of open station assembly lines
The sequencing and line balancing of manual mixed-model assembly lines are challenging tasks due to the complexity and uncertainty of operator activities. The control of cycle time and the sequencing of production can mitigate the losses due to non-optimal line balancing in the case of open-station production where the operators can work ahead of schedule and try to reduce their backlog. The objective of this paper is to provide a cycle time control algorithm that can improve the e ciency of assembly lines in such situations based on a specially mixed sequencing strategy. To handle the uncertainty of activity times, a fuzzy model-based solution has been developed. As the production process is modular, the fuzzy sets represent the uncertainty of the elementary activity times related to the processing of the modules. The optimistic and pessimistic estimates of the completion of activity times extracted from the fuzzy model are incorporated into a model predictive control algorithm to ensure the constrained optimization of the cycle time. The applicability of the proposed method is demonstrated based on a wire-harness manufacturing process with a paced conveyor, but the proposed algorithm can handle continuous conveyors as well. The results confirm that the application of the proposed algorithm is widely applicable in cases where a production line of a supply chain is not well balanced and the activity times are uncertain.
RFID-based Task Time Analysis for Shop Floor Optimization
According to the concept of Industry 4.0, shop ﬂoor control and optimization should be more and more autonomous and integrated. In the age of digital transformation, human operators are still applied in manufacturing processes, so the uncertainty of their tasks times cannot be ignored during scheduling and line balancing. To provide accurate and realtimeinformationabouttheoperators,weproposeanRFID-based task time analysis system. We demonstrate that the empirical density distribution functions of the tasks times can be convolved to generate stochastic model-based optimal solutions of simple assembly line balancing (SALPB) and bin-packing (BP) based scheduling problems.
Multilayer Network-Based Production Flow Analysis
A multilayer network model for the exploratory analysis of production technologies is proposed. To represent the relationship between products, parts, machines, resources, operators, and skills, standardized production and product-relevant data are transformed into a set of bi- and multipartite networks. This representation is beneﬁcial in production ﬂow analysis (PFA) that is used to identify improvement opportunities by grouping similar groups of products, components, and machines. It is demonstrated that the goal-oriented mapping and modularity-based clustering of multilayer networks can serve as a readily applicable and interpretable decision support tool for PFA, and the analysis of the degrees and correlations of a node can identify critically important skills and resources. The applicability of the proposed methodology is demonstrated by a well-documented benchmark problem of a wire-harness production process. The results conﬁrm that the proposed multilayer network can support the standardized integration of production-relevant data and exploratory analysis of strongly interconnected production systems.
Scalable co-Clustering using a Crossing Minimization ‒ Application to Production Flow Analysis
Dynamic cycle time setting and line balancing are the most signiﬁcant problems in modular manufacturing. Industry 4.0 and IIoT (Industrial Internet of Things) based production management systems connect decentralized production units and information sources to increase productivity and ﬂexibility. We developed an IIoT based solution to ensure a real-time connection between products and assembly lines. The proposed dynamic cycle time setting algorithm takes into account the varying complexity of the product based on the real-time information provided by smart wireless sensors and an Indoor Positioning System (IPS). In this paper, we overview Industry 4.0 based assembly line management solutions, present the developed IIoT based infrastructure, and demonstrate the applicability of the proposed cycle time setting algorithm in a simulation example motivated by an industrial open station conveyor.