The Bullwhip effect in water demand management: taming it through an artificial neural networks-based system

被引:3
|
作者
Ponte, Borja [1 ]
Ruano, Laura [1 ]
Pino, Raul [1 ]
de la Fuente, David [1 ]
机构
[1] Univ Oviedo, Polytech Sch Engn, Gijon 33204, Spain
关键词
artificial neural networks; Bullwhip effect; water demand management; FEEDBACK; MODELS;
D O I
10.2166/aqua.2015.087
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The Bullwhip effect (BE) refers to the amplification of the variance of orders and inventories along the supply chain as they move away from the customer. This is considered as the main cause of inefficiencies in the management of a traditional supply chain. However, the BE is not relevant in the classic system of water distribution, based on long-term supply management. Nevertheless, current circumstances have drawn a new context, which has introduced the concept of water demand management, in which efficiency and sustainability are of great importance. Then, the time horizon of management has decreased enormously and the supply time takes on an important role. Therefore, the BE must be considered, as it significantly raises the costs of management. On the one hand, this paper brings evidence that the BE appears in a system of real-time management of water demand. On the other hand, it proposes the application of artificial intelligence techniques for its reduction. More specifically, an advanced forecasting system based on artificial neural networks has been used. The BE is heavily damped.
引用
收藏
页码:290 / 301
页数:12
相关论文
共 50 条
  • [21] Phaseless microwave imaging of dielectric cylinders: An artificial neural networks-based approach
    Fajardo, Jesús E.
    Galván, Julián
    Vericat, Fernando
    Carlevaro, C. Manuel
    Irastorza, Ramiro M.
    [J]. Progress in Electromagnetics Research, 2019, 166 : 95 - 105
  • [22] Performance Assessment of Artificial Neural Networks-Based MPPT Technique for Photovoltaic Systems
    Eleraky, Hadeer Gaber
    Kalas, Ahmed
    Refaat, Ahmed
    Abouzeid, Ahmed Fathy
    [J]. 2024 14TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, ICEENG 2024, 2024, : 48 - 53
  • [23] Research on Networks-based Parts Supplying System Management and Optimization
    Wang Yan-tao
    Xing Yi-fei
    [J]. 2009 IEEE INTERNATIONAL SYMPOSIUM ON IT IN MEDICINE & EDUCATION, VOLS 1 AND 2, PROCEEDINGS, 2009, : 1231 - +
  • [24] Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction
    Salloom, Tony
    Kaynak, Okyay
    Yu, Xinbo
    He, Wei
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2022, 108
  • [25] Fuzzy neural networks-based quality prediction system for sintering process
    Er, MJ
    Liao, J
    Lin, JY
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2000, 8 (03) : 314 - 324
  • [26] A Dynamic Recurrent Neural Networks-Based Recommendation System for Banking Customers
    Avci, Hasan
    Sakar, C. Okan
    [J]. 29TH IEEE CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS (SIU 2021), 2021,
  • [27] Hybrid Model for Water Demand Prediction based on Fuzzy Cognitive Maps and Artificial Neural Networks
    Papageorgiou, Elpiniki I.
    Poczeta, Katarzyna
    Laspidou, Chrysi
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2016, : 1523 - 1530
  • [28] Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models
    Al-Zahrani, Muhammad A.
    Abo-Monasar, Amin
    [J]. WATER RESOURCES MANAGEMENT, 2015, 29 (10) : 3651 - 3662
  • [29] Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models
    Muhammad A. Al-Zahrani
    Amin Abo-Monasar
    [J]. Water Resources Management, 2015, 29 : 3651 - 3662
  • [30] Artificial Neural Networks-Based Approach to Design ARIs Using QSAR for Diabetes Mellitus
    Patra, Jagdish C.
    Singh, Onkar
    [J]. JOURNAL OF COMPUTATIONAL CHEMISTRY, 2009, 30 (15) : 2494 - 2508