A Cloud-based Decision Support System Framework for Order Planning and Tracking
This paper presents a cloud-based decision support system framework for order planning and tracking in a distributed manufacturing environment. Under this framework, computational intelligent techniques are employed to generate order planning decisions while RFID and cloud computing technologies are utilized to capture real-time production records and make remote production order tracking. On the basis of this framework, a pilot system was developed and implemented in a distributed manufacturing company, which reported distinct reductions in production costs and increases in production efficiency. The system framework is also easy-to-extend to integrate wider operations processes in supply chain.
KeywordsOrder tracking Order planning Intelligent optimization RFID Cloud computing
Unable to display preview. Download preview PDF.
This work was funded by the Sichuan University (Grant No. SKYB201301) and the National Natural Science Foundation of China (Grant Nos. 71020107027, 71172197).
- 1.Smitha KK, Thomas T, Chitharanjan K (2012) Cloud based e-governance system: A survey. Procedia Engineering 38:3816–3823Google Scholar
- 2.Xu X (2012) From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing 28:75–86Google Scholar
- 3.Lai W, Tam T, Chan S (2012) Knowledge cloud system for network collaboration: A case study in medical service industry in China. Expert Systems with Applications 39:12205–12212Google Scholar
- 4.Sultan N (2010) Cloud computing for education: A new dawn? International Journal of Information Management 30:109–116Google Scholar
- 5.Dolgui A, Prodhon C (2007) Supply planning under uncertainties in MRP environments: A state of the art. Annual Reviews in Control 31:269–279Google Scholar
- 6.Wang L, Keshavarzmanesh S, Feng H et al (2009) Assembly process planning and its future in collaborative manufacturing: A review. The International Journal of Advanced Manufacturing Technology 41:132–144Google Scholar
- 7.Wazed M, Ahmed S, Nukman Y (2010) A review of manufacturing resources planning models under different uncertainties: State-of-the-art and future directions. South African Journal of Industrial Engineering 21:17–33Google Scholar
- 8.Sakallı Ü, Baykoc, Ö F, Birgören B (2010) A possibilistic aggregate production planning model for brass casting industry. Production Planning & Control 21:319–338Google Scholar
- 9.Engin O, Ceran G, Yilmaz MK (2011) An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems. Applied Soft Computing 11:3056–3065Google Scholar
- 10.Hu XF,Wu EF, Bao JS et al (2010) A branch-and-bound algorithm to minimize the line length of a two-sided assembly line. European Journal of Operational Research 206:703–707Google Scholar
- 11.Guo ZX, Wong WK, Leung SYS et al (2012) Applications of artificial intelligence in the apparel industry: A review. Textile Research Journal 81:1871–1892Google Scholar
- 12.Axsater S (2005) Planning order releases for an assembly system with random operation times. OR Spectrum 27:459–470Google Scholar
- 13.Chen Z, Pundoor G (2006) Order assignment and scheduling in a supply chain. Operations Research 54:555–572Google Scholar
- 14.Guo ZX, Wong WK, Leung SYS (2013) A hybrid intelligent model for order allocation planning in make-to-order manufacturing. Applied Soft Computing 13:1376–1390Google Scholar