The ‘Internet of Things’ enabled real-time scheduling for remanufacturing of automobile engines

Abstract

Typical challenges that managers of remanufacturing face are composed of the lack of timely, accurate, and consistent information of remanufacturing resources. Therefore, it is difficult to implement real-time production scheduling for the shop floor. To address this problem, the authors applied the concept of the 'Internet of Things' to the remanufacturing of automobile engines to form an Internet of Manufacturing Things environment. Under the Internet of Manufacturing Things, an identification technology for disassembled engine parts was designed, and the real-time status of the remanufacturable resources can be monitored. Based on the captured remanufacturing information, a real-time production scheduling method was developed, and a mathematical model was developed to achieve cost reduction, dynamic management of remanufacturable resources, and energy consumption decrease. To obtain an optimal solution, a Pareto-based optimization method was used. Finally, a case study was performed to analyze the effectivity of the proposed method. The results showed that the remanufacturing cost and energy consumption were reduced by 34% and 34% respectively, and the worker load rate was more balanced. These improvements can contribute to more sustainable development and greener production within the remanufacturing industry, especially for remanufacturing of automobile engines.

Exploring Organizational Sustainability of Industry 4.0 under the Triple Bottom Line: The Case of a Manufacturing Company

“…One source cites the re-use of materials in a remanufacturing process enabled by IoT applications. The data produced by the IoT sensors are analyzed with a mathematical model to achieve costs reduction and to manage the remanufactured resources dynamically [45].…”

A Systems Dynamics Enabled Real-Time Efficiency for Fuel Cell Data-Driven Remanufacturing

“…Real-time efficiency, however, involves the understanding of remanufacturing efficiency during each operation. This is, thus, data-driven, [71], and has been a challenge for remanufacturers. Data challenges in remanufacturing include the lack of time and the collection of accurate and consistent information on remanufacturing resources, [41].…”

“…Data challenges in remanufacturing include the lack of time and the collection of accurate and consistent information on remanufacturing resources, [41]. Without this, it is difficult to implement real-time production scheduling on the remanufacturing shop-floor [71], as well as analyze real-time remanufacturing efficiency. Several scholars, (Baker & Scudder 1990, [72]; Frantzen et al, 2011, [73]; Chen et al, 2015, [74]) recognized that lack of real-time data leads to a significant increase in the cost, energy consumption and delivery tardiness.…”

“…To address these problems real-time data collection is proposed. [71] The battery management system, (BMS) which holds the rechargeable energy system and fuel cells in electric vehicles is able to collect data using sensors. This method can improve data collection and data efficiency as well as overall remanufacturing efficiency.…”

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

“…With these technologies being adopted, real-time-data-based traceability, visibility, and interoperability can be realized to improve the performance of shop floor planning, monitoring, and control. At present, by extending the IoT technologies such as RFID to manufacturing environment [28]- [30], real-time data have become more accessible and ubiquitous, contributing to a big data environment [31] [32]. Thus, in a real-world manufacturing environment, the real-time data streams coming from IoT make it possible for one to discard the existing scheduling approaches and adopt the multi-agent-based dynamic scheduling techniques.…”