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LPCI-585: Process and Computational Intelligence Report a Broken Link

While some processes in a company are relatively simple and straightforward, others are complex and could, if designed intelligently, lead to higher productivity and save a company time and money. Process intelligence deals with the design of intelligent processes. In order to design such intelligent processes, techniques from artificial intelligence, business intelligence and computational intelligence (CI) can be used. In this course, we focus on the introduction of CI techniques to design intelligent processes

Required Readings/ Assignments

Lesson 1

Reading 1: Schmutter, P. (2002). A taxonomy for Artificial and Computational Intelligence . In Object-Oriented Ontogenetic Programming: Breeding Computer Programs that Work Like Multicellular Creatures, MSc Thesis, pp. 72-78, University of Dortmund, Germany.
Reading 2: Rudas, I.J., & Fodor, J. (2008). Intelligent systems. International Journal of Computers, Communications and Control 3 (Suppl. issue - ICCCC 2008), 132-138.
Reading 3: Hall, L. O.  (2008). Computational intelligence. AccessScience, McGraw-Hill.

Lesson 2

Reading 4: Jain, A.K. (1996). Artificial neural networks: a tutorial. Computer 29(3), 31-44.
Reading 5: Gershenson, C. (2003). Artificial Neural Networks for beginners. Online tutorial.
Reading 6: Yazgana, H.R., Borana, S., & Goztepeb, K.  (2009). An ERP software selection process with using artificial neural network based on analytic network process approach. Expert Systems with Applications 36(5), 9214–9222.

Lesson 3

Reading 7: Hinchey, M.G., Sterritt, R., & Rouff, C. (2007). Swarms and Swarm Intelligence. Computer 40 (4), 111-113.

Provides an overview of swarm optimization.
Reading 8: Poli, R., Kennedy, J., & Blackwell, T.  (2007). Particle swarm optimization—An overview. Swarm Intelligence 1 (1), 33-57.

[Note: Section 5 is not required and can be omitted.]
Reading 9: Tsai, C.Y., & Yeh, S.W. (2008). A multiple objective particle swarm optimization approach for inventory classification. International Journal of Production Economics 114(2), 656-666.

Describes an example of application of PSO in the context of process intelligence.
Reading 10: Dorigo, M., & Stützle, T. (2010). Ant colony optimization: Overview and recent advances. Handbook of Metaheuristics. International Series in Operations Research & Management Science 146, pp. 227-263.

Provides an overview of ACO.
Reading 11: Comuzzi, M., Vanderfeesten, I., & Wang, T. (2013). Optimized cross-organizational business process monitoring: Design and enactment. Information Sciences 244 (Sept.), 107-118.

Provides an example of application of ACO in the context of process intelligence.

Lesson 4

Reading 12: Obitko, M. (1998). Introduction to Genetic Algorithms. Online tutorial.
Reading 13: Srinivas, M., & Patnaik, L.M. (1994). Genetic algorithms: A survey. Computer 27(6), 17-26.
Reading 14: Beasley, D., Bull, D.R., & Martin, R.R. (1993). An overview of genetic algorithms—Part 1, Fundamentals. University Computing 15(2), 58-69.
Reading 15: Ko, C-H, & Wang, S-F. (2011). Precast production scheduling using multi-objective genetic algorithms. Expert Systems with Applications 38(2011), 8293-8302.

Lesson 5

Reading 16: Brule, J.F. (1985). Fuzzy systems—A tutorial.
Reading 17: Anonymous. (2010) A short fuzzy logic tutorial.
Reading 18: Aziz, S.A., & Parthiban, J. (1996). Fuzzy logic.
Reading 19: Lu, K.Y., & Sy, C.C. (2009). A real-time decision-making of maintenance using fuzzy agent. Expert Systems with Applications 36(2, part 2), 2691-2698.