Business Information Systems for the Cost/Energy Management of Water Distribution Networks: A Critical Appraisal of Alternative Optimization Strategies

  • Antonio CandelieriEmail author
  • Bruno G. Galuzzi
  • Ilaria Giordani
  • Riccardo Perego
  • Francesco Archetti
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 339)


The objective of this paper is to show how smart water networks enable new strategies for the energy cost management of the network, more precisely Pump Scheduling Optimization. This problem is traditionally solved using mathematical programming and, more recently, nature inspired metaheuristics. The schedules obtained by these methods are typically not robust both respect to random variations in the water demand and the non-linear features of the model. The authors consider three alternative optimization strategies: (i) global optimization of black-box functions, based on a Gaussian model and the use of the hydraulic simulator (EPANET) to evaluate the objective function; (ii) Multi Stage Stochastic Programming, which models the stochastic evolution of the water demand through a scenario analysis to solve an equivalent large scale linear program; and finally (iii), Approximate Dynamic Programming, also known as Reinforcement Learning. With reference to real life experimentation, the last two strategies offer more modeling flexibility, are demand responsive and typically result in more robust solutions (i.e. pump schedules) than mathematical programming. More specifically, Approximate Dynamic Programming works on minimal modelling assumption and can effectively leverage on line data availability into robust on-line Pump Scheduling Optimization.


Pump Scheduling Optimization Bayesian Optimization Multi-stage stochastic programming Reinforcement Learning 


  1. 1.
    Stewart, R.A., et al.: Integrated intelligent water-energy metering systems and informatics: visioning a digital multi-utility service provider. Environ. Model Softw. 105, 94–117 (2018)CrossRefGoogle Scholar
  2. 2.
    Candelieri, A., Giordani, I., Archetti, F.: Automatic configuration of kernel-based clustering: an optimization approach. In: Battiti, R., Kvasov, D.E., Sergeyev, Y.D. (eds.) LION 2017. LNCS, vol. 10556, pp. 34–49. Springer, Cham (2017). Scholar
  3. 3.
    Candelieri, A., Soldi, D., Archetti, F.: Cost-effective sensors placement and leak localization - the Neptun pilot of the ICeWater project. J. Water Supply Res. Technol. AQUA 64(5), 567–582 (2015)CrossRefGoogle Scholar
  4. 4.
    Shabani, S., Candelieri, A., Archetti, F., Naser, G.: Gene expression programming coupled with unsupervised learning: a two-stage learning process in multi-scale, short-term water demand forecasts. Water 10(2) (2018)CrossRefGoogle Scholar
  5. 5.
    Candelieri, A., et al.: Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization. Comput. Oper. Res. (2018)Google Scholar
  6. 6.
    Candelieri, A., Giordani, I., Archetti, F.: Supporting resilience management of water distribution networks through network analysis and hydraulic simulation. In: 2017 Proceedings of the 21st International Conference on Control Systems and Computer, CSCS 2017, pp. 599–605 (2017)Google Scholar
  7. 7.
    Candelieri, A., Perego, R., Archetti, F.: Bayesian optimization of pump operations in water distribution systems. J. Glob. Optim. 71(1), 213–235 (2018)CrossRefGoogle Scholar
  8. 8.
    Mala-Jetmarova, H., Sultanova, N., Savic, D.: Lost in optimisation of water distribution systems? A literature review of system design. Water 10(3) (2018). Scholar
  9. 9.
    Rossman, L.A.: EPANET 2: users manual, Washington, DC (2000)Google Scholar
  10. 10.
    McCormick, G., Powell, R.S.: Derivation of near-optimal pump schedules for water distribution by simulated annealing. J. Oper. Res. Soc. 55(7), 728–736 (2004)CrossRefGoogle Scholar
  11. 11.
    De Paola, F., Fontana, N., Giugni, M., Marini, G., Pugliese, F.: An application of the harmony-search multi-objective (HSMO) optimization algorithm for the solution of pump scheduling problem. Procedia Eng. 162, 494–502 (2016)CrossRefGoogle Scholar
  12. 12.
    Mockus, J.: Bayesian Approach to Global Optimization: Theory and Applications, vol. 37. Springer, Dordrecht (1989). Scholar
  13. 13.
    Dupacová, J., Consigli, G., Wallace, S.W.: Scenarios for multistage stochastic programs. Ann. Oper. Res. 100(1), 25–53 (2000)CrossRefGoogle Scholar
  14. 14.
    Ghaddar, B., Naoum-Sawaya, J., Kishimoto, A., Taheri, N., Eck, B.: A Lagrangian decomposition approach for the pump scheduling problem in water networks. Eur. J. Oper. Res. 241(2), 490–501 (2015)CrossRefGoogle Scholar
  15. 15.
    D’Ambrosio, C., Lodi, A., Wiese, S., Bragalli, C.: Mathematical programming techniques in water network optimization. Eur. J. Oper. Res. 243(3), 774–788 (2015)CrossRefGoogle Scholar
  16. 16.
    Močkus, J.: On Bayesian methods for seeking the extremum. In: Marchuk, G.I. (ed.) Optimization Techniques 1974. LNCS, vol. 27, pp. 400–404. Springer, Heidelberg (1975). Scholar
  17. 17.
    Archetti, F., Betrò, B.: A probabilistic algorithm for global optimization. Calcolo 16(3), 335–343 (1979)CrossRefGoogle Scholar
  18. 18.
    Kushner, H.J.: A new method of locating the maximum point of an arbitrary multi-peak curve in the presence of noise. J. Basic Eng. 86(1), 97–106 (1964)CrossRefGoogle Scholar
  19. 19.
    Auer, P.: Using confidence bounds for exploitation-exploration trade-offs. JMLR 3, 397–422 (2002)Google Scholar
  20. 20.
    Frazier, P.I.: Knowledge-Gradient Methods for Statistical Learning. Princeton University, Princeton (2009)Google Scholar
  21. 21.
    Goryashko, A.P., Nemirovski, A.S.: Robust energy cost optimization of water distribution system with uncertain demand. Autom. Remote Control 75(10), 1754–1769 (2014)CrossRefGoogle Scholar
  22. 22.
    Puleo, V., Morley, M., Freni, G., Savić, D.: Multi-stage linear programming optimization for pump scheduling. Procedia Eng. 70, 1378–1385 (2014)CrossRefGoogle Scholar
  23. 23.
    Housh, M., Ostfeld, A., Shamir, U.: Limited multi-stage stochastic programming for managing water supply systems. Environ. Model Softw. 41, 53–64 (2013)CrossRefGoogle Scholar
  24. 24.
    Candelieri, A., Perego, R., Archetti, F.: Intelligent pump scheduling optimization in water distribution networks. In: 12th International Conference, LION 12, Kalamata, Greece (2018)Google Scholar
  25. 25.
    Candelieri, A., Archetti, F., Messina, E.: Analytics for supporting urban water management. Environ. Eng. Manag. J. 12(5), 875–881 (2013)CrossRefGoogle Scholar
  26. 26.
    Candelieri, A.: Clustering and support vector regression for water demand forecasting and anomaly detection. Water 9(3) (2017)CrossRefGoogle Scholar
  27. 27.
    Candelieri, A., Soldi, D., Archetti, F.: Short-term forecasting of hourly water consumption by using automatic metering readers data. Procedia Eng. 119(1), 844–853 (2015)CrossRefGoogle Scholar
  28. 28.
    Candelieri, A., Soldi, D., Archetti, F.: Network analysis for resilience evaluation in water distribution networks. Environ. Eng. Manag. J. 14(6), 1261–1270 (2015)CrossRefGoogle Scholar
  29. 29.
    Soldi, D., Candelieri, A., Archetti, F.: Resilience and vulnerability in urban water distribution networks through network theory and hydraulic simulation. Procedia Eng. 119(1), 1259–1268 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Antonio Candelieri
    • 1
    Email author
  • Bruno G. Galuzzi
    • 1
  • Ilaria Giordani
    • 1
  • Riccardo Perego
    • 1
  • Francesco Archetti
    • 1
    • 2
  1. 1.University of Milano-BicoccaMilanItaly
  2. 2.Consorzio Milano RicercheMilanItaly

Personalised recommendations