By Qing Duan, Krishnendu Chakrabarty, Jun Zeng
This booklet presents a accomplished set of optimization and prediction strategies for an firm details method. Readers with a history in operations study, process engineering, records, or info analytics can use this booklet as a connection with derive perception from info and use this information as advice for creation administration. The authors establish the main demanding situations in company details administration and current effects that experience emerged from modern learn during this area. insurance comprises subject matters starting from activity scheduling and source allocation, to workflow optimization, method time and standing prediction, order admission rules optimization, and company service-level functionality research and prediction. With its emphasis at the above themes, this ebook offers an in-depth examine company info administration strategies which are wanted for higher automation and reconfigurability-based fault tolerance, in addition to to acquire data-driven suggestions for powerful decision-making.
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Extra info for Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System
The fulfillment path B processes a single-part product. The order-level stages Serializing and Packaging are common to all products. 24 2 Production Simulation Platform Product 1---Fulfillment Path A SERIALIZING Order Order Level Decomposition SERIALIZING COVER PRINTING LAMINATING IMAGE PROCESSING(IP) BINDING PACKAGING Product Level Order Level BINDING PACKAGING BOOK PRINTING Product Level IMAGE PROCESSING(IP) Part Level BOOK PRINTING Product 2---Fulfillment Path B Product n---Fulfillment Path X Fig.
Kusiak, M. Shahbaz, M. Srinivas, Data mining in manufacturing: a review. J. Manuf. Sci. Eng. 128(4), 969–976 (2005) 56. N. Bolloju, M. Khalifa, E. Turban, Integrating knowledge management into enterprise environments for the next generation decision support. Decis. Support Syst. 33(2), 163–176 (2002) 57. H. Aytug, S. Bhattacharyya, G. Koehler, J. Snowdon, A review of machine learning in scheduling. IEEE Trans. Eng. Manage. 1 Background and Motivation Research on productivity improvement is gaining accelerated attention in enterprise industry in recent years.
Each task can only have one resource assigned to it. Random variable X is the execution time of a single-part product. The expectation EŒX of X can be obtained by Eq. 3) is only applicable in the case of a chain of sequential tasks such as a single-part product. Derivation and Proof of Eq. 3) are presented in Appendix A. 2 Problem Description and Formulation 35 Part assembly Part manufacture W1 task task task task WH Y X Fig. 3 Manufacturing process for a multi-part product The manufacturing process for a multi-part product is shown in Fig.