Abstract
This chapter presents mechanisms for real-time optimization of dynamic and uncertain matching processes, and control of distributed decision-makers in the system. Several numerical examples are presented for illustrating the mechanics and applications of each method. The purpose is to demonstrate the fundamentals and major classes of real-time optimization techniques, show how to model distributed matching processes via multi-agent systems and task administration protocols , and discuss major challenges of such dynamic and distributed systems associated with “AI”—artificial intelligence; analytics and informatics.
The original version of this chapter was revised: For detailed information please see erratum. The erratum to this chapter is available at 10.1007/978-3-319-46070-3_9
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-46070-3_9
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Notes
- 1.
Modern storage allocation procedures suggest locating products with higher affinity (i.e., products that are ordered with each other more frequently) close to each other, in order to save material handling time. The affinity of products is calculated based on historical demand data and using data mining heuristics (see, e.g., Li et al. 2015).
- 2.
- 3.
Note that without the assumption of S-shaped movement, the problem is a multiple traveling salesman problem (see Bektas 2006).
- 4.
Note that best matching with IP is a quadratic assignment problem, which can be solved by various exact (e.g., branch-and-bound), heuristics/metaheuristics (e.g., genetic algorithm, tabu search). Details of the ACO have not been presented for the sake of brevity. (Review Chap. 5 for more detailed explanation of ACO.).
- 5.
Note that the optimal length of ∆ can be defined based on the tradeoff shown in Fig. 6.3.
- 6.
The shaded cells refer to the processed/in-process regions—the matching decisions associated with these regions cannot be changed.
- 7.
- 8.
For detailed information about the collaborative control theory and its design principles, interested readers are encouraged to read “Revolutionizing Collaboration through e-Work, e-Business, & e-Service” by Nof et al. (2015).
- 9.
The presented heuristics are based on a recent work by Moghaddam and Nof (2015a) on generalized best matching, and can be extended to other instances of best matching in a similar manner.
- 10.
In all the four adaptation heuristics, it is assumed that β (and each solution, in general) is encoded as an array of length \( \left| I \right| \), where the entries represent the elements of set J matched to each \( i \in I \) (see Fig. 5.5 in Chap. 5).
- 11.
- 12.
- 13.
This problem is also known in literature as the generalized assignment problem (see Pentico 2007).
- 14.
Note that this is just an example, and interaction protocols can be designed in many different ways. Interested readers are encouraged to read the seminal article on Contract Net Protocols by Smith (1980), which introduces the basics of collaborative task sharing in multi-agent systems.
- 15.
This time-based definition implies that all characteristics of task i are (or can be) subject to change over time, and thus are continuously monitored and updated by task agent \( \Omega ^{i} \).
- 16.
Note that the time lapse between calculating \( p_{i \to j} (t) \) and \( p_{j \to i} (t) \) is disregarded for simplicity of presentation and calculation.
- 17.
- 18.
The example is adapted from Choi et al. (2001).
- 19.
See Chapter 5 for more information on genetic operators.
References
Bektas, T. (2006). The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega, 34, 209–219.
Ben-Tal, A., Ghaoui, L., & Nemirovski, A. (2009). Robust optimization. Princeton: Princeton University Press.
Chachuat, B., Srinivasan, B., & Bonvin, D. (2009). Adaptation strategies for real-time optimization. Computers & Chemical Engineering, 33, 1557–1567.
Chituc, C. M., & Nof, S. Y. (2007). The Join/Leave/Remain (JLR) decision in collaborative networked organizations. Computers Industrial Engineering, 53, 173–195.
Choi, S. P. M., Liu, J., & Chan, S. P. (2001). A genetic agent-based negotiation system. Computer Networks, 37, 195–204.
Frankish, K., & Ramsey, W. M. (2014). The cambridge handbook of artificial intelligence. Cambridge: Cambridge University Press.
Holl, S., Zimmermann, O., Palmblad, M., Mohammed, Y., & Hofmann-Apitius, M. (2014). A new optimization phase for scientific workflow management systems. Future Generation Computer Systems, 36, 352–362.
Jennings, N. R., Sycara, K., & Wooldridge, M. (1998). A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems, 1, 7–38.
Jeong, W. (2006). Fault-tolerant timeout communication protocols for distributed micro-sensor network systems. Ph.D. Thesis (Purdue University, West Lafayette).
Jeong, W. (2009). Sensors and sensor networks. In the Springer Handbook of Automation (Nof, Ed.), Ch. 20.
Jeong, W., & Nof, S. Y. (2009). A collaborative sensor network middleware for automated production systems. Computers & Industrial Engineering, 57, 106–113.
Kang, H. (1994). Development of information exchange protocols for distributed inspection integration. M.S. Thesis. School of Industrial Engineering, Purdue University.
Ko, H. S., & Nof, S. Y. (2012). Design and application of task administration protocols for collaborative production and service systems. International Journal of Production Economics, 135, 177–189.
Li, J., Moghaddam, M., & Nof, S. Y. (2015). Dynamic storage assignment with product affinity and ABC classification—a case study. International Journal of Advanced Manufacturing Technology,. doi:10.1007/s00170-015-7806-7
Liu, Y., & Nof, S. Y. (2004). Distributed micro flow-sensor arrays and networks (DMFSN/A): Design of architectures and communication protocols. International Journal of Production Research, 42, 3101–3115.
Moghaddam, M., & Nof, S. Y. (2015a). Best matching with interdependent preferences—Implications for capacitated cluster formation and evolution. Decision Support Systems, 79, 125–137.
Moghaddam, M., & Nof, S. Y. (2015b). Real-time administration of tool sharing by best matching to enhance assembly lines balance ability and flexibility. Mechatronics, 31, 147–157.
Moghaddam, M., & Nof, S. Y. (2016). Real-time optimization and control mechanisms for collaborative demand and capacity sharing. International Journal of Production Economics, 171, 495–506.
Nof, S. Y. (2000). Intelligent collaborative agents. In C. Moore, et al. (Eds.), Encyclopedia of science and technology (pp. 219–222). New York: McGraw Hill.
Nof, S. Y. (2003). Design of effective e-Work: Review of models, tools, and emerging challenges. Production Planning and Control, 14, 681–703.
Nof, S. Y. (2007). Collaborative control theory for e-Work, e-Production, and e-Service. Annual Reviews in Control, 31, 281–292.
Nof, S.Y. (2009). Springer handbook of automation. Berlin: Springer.
Nof, S. Y., Ceroni, J., Jeong, W., & Moghaddam, M. (2015). Revolutionizing Collaboration through e-Work, e-Business, and e-Service. Berlin: Springer.
Pentico, D. W. (2007). Assignment problems: a golden anniversary survey. European Journal of Operational Research, 176, 774–793.
Pistikopoulos, E. N., Dua, V., Bozinis, N. A., Bemporad, A., & Morari, M. (2002). On-line optimization via off-line parametric optimization tools. Computers & Chemical Engineering, 26, 175–185.
Sahinidis, N. V. (2004). Optimization under uncertainty: State-of-the-art and opportunities. Computers & Chemical Engineering, 28, 971–983.
Smith, R. G. (1980). The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers C-29, 1104–1113.
Van Dyke Parunak, H. (1997). “Go to the ant”: Engineering principles from natural multi-agent systems. Annals of Operations Research, 75, 69–101.
Velásquez, J. D., & Nof, S. Y. (2008). Integration of machine-vision inspection information for best matching of distributed components and suppliers. Computers in Industry, 59, 69–81.
Wald, A. (1945). Statistical decision functions which minimize the maximum risk. The Annals of Mathematics, 46, 265–280.
Yoon, S. Y., & Nof, S. Y. (2011). Affiliation/dissociation decision models in demand and capacity sharing collaborative network. International Journal of Production Economics, 130, 135–143.
Zhang, Y., Monder, D., & Fraser Forbes, J. (2002). Real-time optimization under parametric uncertainty: A probability constrained approach. Journal of Process Control, 12, 373–389.
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Moghaddam, M., Nof, S.Y. (2017). Dynamic and Distributed Matching. In: Best Matching Theory & Applications. Automation, Collaboration, & E-Services, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-46070-3_6
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