This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalised flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamical allocation of safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators' availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the case study considered.
@article{bwmeta1.element.bwnjournal-article-amcv26i3p641bwm, author = {Juan M. Grosso and Carlos Ocampo-Martinez and Vicen\c c Puig}, title = {Reliability-based economic model predictive control for generalised flow-based networks including actuators' health-aware capabilities}, journal = {International Journal of Applied Mathematics and Computer Science}, volume = {26}, year = {2016}, pages = {641-654}, language = {en}, url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv26i3p641bwm} }
Juan M. Grosso; Carlos Ocampo-Martinez; Vicenç Puig. Reliability-based economic model predictive control for generalised flow-based networks including actuators' health-aware capabilities. International Journal of Applied Mathematics and Computer Science, Tome 26 (2016) pp. 641-654. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv26i3p641bwm/
[000] Ahuja, R., Magnanti, T. and Orlin, J. (1993). Network Flows: Theory, Algorithms, and Applications, Prentice Hall, Englewood Cliffs, NJ. | Zbl 1201.90001
[001] Betts, J. (2011). A robust approximation for setting target inventory levels in a constrained production environment, Procedia Computer Science 4: 1262-1271.
[002] Billings, R. and Jones, C. (2008). Forecasting Urban Water Demand, 2nd Edn., American Water Works Association, Denver, CO.
[003] Blanchini, F., Miani, S. and Ukovich, W. (2000). Control of production-distribution systems with unknown inputs and system failures, IEEE Transactions on Automatic Control 45(6): 1072-1081. | Zbl 0988.90013
[004] Blanchini, F., Rinaldi, F. and Ukovich, W. (1997). Least inventory control of multistorage systems with non-stochastic unknown inputs, IEEE Transactions on Robotics and Automation 13(5): 633-645.
[005] Chamseddine, A., Theilliol, D., Sadeghzadeh, I., Zhang, Y. and Weber, P. (2014). Optimal reliability design for over-actuated systems based on the MIT rule: Application to an octocopter helicopter testbed, Reliability Engineering & System Safety 132: 196-206.
[006] Christopher, M. (2005). Logistics & Supply Chain Management: Creating Value-Adding Networks, 3rd Edn., Pearson Education Limited, Dorchester.
[007] Ellis, M., Durand, H. and Christofides, P. (2014). A tutorial review of economic model predictive control methods, Journal of Process Control 24(8): 1156-1178.
[008] Ford, L.R. and Fulkerson, D.R. (1962). Flows in Networks, Princeton University Press, Princeton, NJ. | Zbl 0106.34802
[009] Gallestey, E., Stothert, A., Antoine, M. and Morton, S. (2002). Model predictive control and the optimization of power plant load while considering lifetime consumption, IEEE Transactions on Power Systems 17(1): 186-191.
[010] Gertsbakh, I. (2010). Reliability Theory: With Applications to Preventive Maintenance, Springer, Berlin. | Zbl 0959.62088
[011] Goetschalckx, M. (2011). Advanced supply chain models, in F. Hillier and C. Price (Eds.), Supply Chain Engineering, International Series in Operations Research & Management Science, Vol. 161, Springer US, New York, NY, pp. 615-670.
[012] Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P. and Sun, Y. (2009). A review on degradation models in reliability analysis, 4th World Congress on Engineering Asset Management, Athens, Greece, pp. 369-384.
[013] Grosso, J. (2015). On Model Predictive Control for Economic and Robust Operation of Generalized Flow-based Networks, Ph.D. thesis, Universitat Politècnica de Catalunya, Barcelona, http://hdl.handle.net/10803/288218.
[014] Grosso, J., Ocampo-Martinez, C. and Puig, V. (2012). A service reliability model predictive control with dynamic safety stocks and actuators health monitoring for drinking water networks, 51st IEEE Annual Conference on Decision and Control (CDC), Maui, HI, USA, pp. 4568-4573.
[015] Grosso, J., Ocampo-Martinez, C., Puig, V. and Joseph, B. (2014). Chance-constrained model predictive control for drinking water networks, Journal of Process Control 24(5): 504-516.
[016] Guida, M. and Giorgio, M. (1995). Reliability analysis of accelerated life-test data from a repairable system, IEEE Transactions on Reliability 44(2): 337-346.
[017] Guide, V. and Srivastava, R. (2000). A review of techniques for buffering against uncertainty with MRP systems, Production Planning & Control 11(3): 223-233.
[018] Hsu, F., Vesely, W., Grove, E., Subudhi, M. and Samanta, P. (1991). Degradation modeling: Extensions and applications, Technical report, Brookhaven National Laboratory, Ridge, NY.
[019] Kall, P. and Mayer, J. (2005). Stochastic Linear Programming, International Series in Operations Research & Management Science, No. 80, Springer, New York, NY.
[020] Kanet, J., Gorman, M. and Stößlein, M. (2010). Dynamic planned safety stocks in supply networks, International Journal of Production Research 48(22): 6859-6880. | Zbl 1197.90027
[021] Khelassi, A., Theilliol, D., Weber, P. and Sauter, D. (2011). A novel active fault tolerant control design with respect to actuators reliability, 50th IEEE Conference on Decision and Control and European Control Conference (CDCECC), Orlando, FL, USA, pp. 2269-2274.
[022] Kyriakides, E. and Polycarpou, M. (2015). Intelligent Monitoring, Control, and Security of Critical Infrastructure Systems, Studies in Computational Intelligence, Vol. 565, Springer Verlag, Berlin/Heidelberg.
[023] Letot, C. and Dehombreux, P. (2012). Dynamic reliability degradation based models and maintenance optimization, Proc. 9th National Congress on Theoretical and Applied Mechanics (NCTAM), Brussels, Belgium, pp. 1-9.
[024] Limon, D., Pereira, M., de la Peña, D.M., Alamo, T. and Grosso, J. (2014). Single-layer economic model predictive control for periodic operation, Journal of Process Control 24(8): 1207-1224.
[025] Martorell, S., Sanchez, A. and Serradell, V. (1999). Age-dependent reliability model considering effects of maintenance and working conditions, Reliability Engineering and System Safety 64(1): 19-31.
[026] Negenborn, R. and Hellendoorn, H. (2010). Intelligence in transportation infrastructures via model-based predictive control, in R.R. Negenborn et al. (Eds.), Intelligent Infrastructures, Springer, Dordrecht, pp. 3-24. | Zbl 1279.90018
[027] Nemirovski, A. and Shapiro, A. (2006). Convex approximations of chance constrained programs, SIAM Journal on Optimization 17(4): 969-996. | Zbl 1126.90056
[028] Ocampo-Martinez, C., Puig, V., Cembrano, G., Creus, R. and Minoves, M. (2009). Improving water management efficiency by using optimization-based control strategies: The Barcelona case study, Water Science & Technology: Water Supply 9(5): 565-575.
[029] Ortega, M. and Lin, L. (2004). Control theory applications to the production-inventory problem: A review, International Journal of Production Research 42(11): 2303-2322. | Zbl 1060.90011
[030] Osman, H. and Demirli, K. (2012). Integrated safety stock optimization for multiple sourced stockpoints facing variable demand and lead time, International Journal of Production Economics 135(1): 299-307.
[031] Özer, Ö. (2003). Replenishment strategies for distribution systems under advance demand information, Management Science 49(3): 255-272. | Zbl 1232.90083
[032] Papageorgiou, L. (2009). Supply chain optimisation for the process industries: Advances and opportunities, Computers & Chemical Engineering 33(12): 1931-1938.
[033] Papageorgiou, M. (1984). Optimal control of generalized flow networks, in P. Thoft-Christensen (Ed.), System Modelling and Optimization, Lecture Notes in Control and Information Sciences, Vol. 59, Springer, Berlin/Heidelberg, pp. 373-382.
[034] Pereira, E., Galvao, R. and Yoneyama, T. (2010). Model predictive control using prognosis and health monitoring of actuators, 2010 IEEE International Symposium on Industrial Electronics (ISIE), Bari, Italy, pp. 237-243.
[035] Sampathirao, A., Grosso, J., Sopasakis, P., Ocampo-Martinez, C., Bemporad, A. and Puig, V. (2014). Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona case study, 19th IFAC World Congress, Cape Town, South Africa, pp. 10457-10462.
[036] Sarimveis, H., Patrinos, P., Tarantilis, C. and Kiranoudis, C. (2008). Dynamic modeling and control of supply chain systems: A review, Computers & Operations Research 35(11): 3530-3561. | Zbl 1146.90353
[037] Schoenmeyr, T. and Graves, S. (2009). Strategic safety stocks in supply chains with evolving forecasts, Manufacturing & Service Operations Management 11(4): 657-673.
[038] Schwartz, J. and Rivera, D. (2010). A process control approach to tactical inventory management in production-inventory systems, International Journal of Production Economics 125(1): 111-124.
[039] Strijbosch, L., Syntetos, A., Boylan, J. and Janssen, E. (2011). On the interaction between forecasting and stock control: The case of non-stationary demand, International Journal of Production Economics 133(1): 470-480.
[040] Subramanian, K., Rawlings, J., Maravelias, C., Flores-Cerrillo, J. and Megan, L. (2013). Integration of control theory and scheduling methods for supply chain management, Computers & Chemical Engineering 51(0): 4-20.
[041] Weber, P., Boussaid, B., Khelassi, A., Theilliol, D. and Aubrun, C. (2012). Reconfigurable control design with integration of a reference governor and reliability indicators, International Journal of Applied Mathematics and Computer Science 22(1): 139-148, DOI: 10.2478/v10006-012-0010-0. | Zbl 1273.93058