A MPN-based scheduling model for IoT-enabled hybrid flow shop manufacturing. Issue 4 (October 2016)

• Record Type: Journal Article
• Authors: Wang, Meilin; Zhong, Ray Y.; Dai, Qingyun; Huang, George Q.
• Is Part Of: Advanced engineering informatics. Volume 30:Issue 4(2016:Oct.)
• Page Start: 728
• Page End: 736
• Publication Date: 2016-10
• Subjects: Hybrid flow shop -- Internet of Things (IoT) -- Nonequal-parallel-machine -- Petri Net -- Manufacturing flow shop; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2016.09.006
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: The MPN model can reflect smart objects behaviors enabled by IoT devices. An ACO-based solution is proposed to avoid the stagnation. The comparison result presents the outperformance of average computational cost. The outperformance attributes to the limited values and their ratio. Abstract: As the wide use of Internet of Things (IoT) technology in manufacturing, production decision-makings are revitalized due to the real-time activities and behaviors happened in flow shops. This paper introduces a manufacturing Petri Net-based scheduling model for IoT-enabled hybrid flow shop manufacturing. The proposed model switches the schedule of Token sequences in MPN to the trigger sequence of transition. Thus the real-time production behaviors in IoT-enabled manufacturing flow shops could be modelled. Several contributions are significant. Firstly, the proposed model is able to reflect the real-time behaviors of various smart manufacturing objects enabled by IoT devices so that optimal schedules could be achieved. Secondly, an ant colony optimization (ACO)-based solution is proposed with the confinement of the minimum and maximum pheromone value to avoid the stagnation of the search caused by the pheromone concentration. Thirdly, the comparison of the proposed model and classic ant system presents the outperformance of average computational cost. The outperformance attributes to the limitation of the maximum and minimum of pheromone as well as their ratio so that it has better efficiency. Key findings and observations are summarized as managerial implications which could be used for guiding practitioners to implement the decision-making in IoT-enabled manufacturing flow shops.