Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30073
Towards Developing a Self-Explanatory Scheduling System Based on a Hybrid Approach

Authors: Jian Zheng, Yoshiyasu Takahashi, Yuichi Kobayashi, Tatsuhiro Sato

Abstract:

In the study, we present a conceptual framework for developing a scheduling system that can generate self-explanatory and easy-understanding schedules. To this end, a user interface is conceived to help planners record factors that are considered crucial in scheduling, as well as internal and external sources relating to such factors. A hybrid approach combining machine learning and constraint programming is developed to generate schedules and the corresponding factors, and accordingly display them on the user interface. Effects of the proposed system on scheduling are discussed, and it is expected that scheduling efficiency and system understandability will be improved, compared with previous scheduling systems.

Keywords: Constraint programming, Factors considered in scheduling, machine learning, scheduling system.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1124435

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 779

References:


[1] Michael Pinedo. Planning and scheduling in manufacturing and services, Springer, 2005.
[2] Thomas Haynes, Sandip Sen, Neeraj Arora, and Rajani Nadella, “An automated meeting scheduling system that utilizes user preferences”, In Proceedings of the first international conference on Autonomous agents, . ACM, 1997, pp. 308–315
[3] Peter G Higgins, “Interaction in hybrid intelligent scheduling”, International Journal of Human Factors in Manufacturing, pp. 185–203. 1996.
[4] Kjetil Fagerholt, “A computer-based decision support system for vessel fleet scheduling - experience and future research”, Decision Support Systems, pp. 35–47, 2004.
[5] Haluk Demirkan and Dursun Delen, “Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud”, Decision Support Systems, pp. 412–421, 2013.
[6] Xiu Li, Jingdong Song, and Biqing Huang, “A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics”, The International Journal of Advanced Manufacturing Technology, pp. 1–13, 2015.
[7] MG Karlaftis and EI Vlahogianni, “Statistical methods versus neural networks in transportation research: Differences, similarities and some insights”, Transportation Research Part C: Emerging Technologies, pp. 387–399, 2011.
[8] MY Rafiq, G Bugmann, and DJ Easterbrook, “Neural network design for engineering applications”, Computers & Structures, pp. 1541–1552, 2001.
[9] Francesca Rossi, Peter van Beek, and Walsh Toby, Handbook of constraint programming. Elsevier, 2006.