Nested algorithms for optimal reservoir operation and their embedding in a decision support platform
暫譯: 最佳水庫運行的嵌套演算法及其在決策支援平台中的嵌入

Blagoj Delipetrev

  • 出版商: CRC
  • 出版日期: 2016-07-18
  • 售價: $3,220
  • 貴賓價: 9.5$3,059
  • 語言: 英文
  • 頁數: 156
  • 裝訂: Paperback
  • ISBN: 1138029823
  • ISBN-13: 9781138029828
  • 相關分類: Algorithms-data-structures
  • 海外代購書籍(需單獨結帳)

商品描述

Reservoir operation is a multi-objective optimization problem, and is traditionally solved with dynamic programming (DP) and stochastic dynamic programming (SDP) algorithms. The thesis presents novel algorithms for optimal reservoir operation, named nested DP (nDP), nested SDP (nSDP), nested reinforcement learning (nRL) and their multi-objective (MO) variants, correspondingly MOnDP, MOnSDP and MOnRL.
The idea is to include a nested optimization algorithm into each state transition, which reduces the initial problem dimension and alleviates the curse of dimensionality. These algorithms can solve multi-objective optimization problems, without significantly increasing the algorithm complexity or the computational expenses. It can additionally handle dense and irregular variable discretization. All algorithms are coded in Java and were tested on the case study of the Knezevo reservoir in the Republic of Macedonia.
Nested optimization algorithms are embedded in a cloud application platform for water resources modeling and optimization. The platform is available 24/7, accessible from everywhere, scalable, distributed, interoperable, and it creates a real-time multiuser collaboration platform.
This thesis contributes with new and more powerful algorithms for an optimal reservoir operation and cloud application platform. All source codes are available for public use and can be used by researchers and practitioners to further advance the mentioned areas.

商品描述(中文翻譯)

儲水庫操作是一個多目標優化問題,傳統上使用動態規劃(Dynamic Programming, DP)和隨機動態規劃(Stochastic Dynamic Programming, SDP)算法來解決。本論文提出了針對最佳儲水庫操作的新算法,分別命名為嵌套動態規劃(nested DP, nDP)、嵌套隨機動態規劃(nested SDP, nSDP)、嵌套強化學習(nested reinforcement learning, nRL)及其多目標(Multi-Objective, MO)變體,分別為MOnDP、MOnSDP和MOnRL。

這些算法的核心思想是在每個狀態轉換中包含一個嵌套優化算法,這樣可以降低初始問題的維度,並減輕維度詛咒的影響。這些算法能夠解決多目標優化問題,而不會顯著增加算法的複雜性或計算開銷。此外,它們還能處理密集和不規則的變數離散化。所有算法均以Java編寫,並在北馬其頓共和國的Knezevo儲水庫案例研究中進行了測試。

嵌套優化算法嵌入於一個雲應用平台,用於水資源建模和優化。該平台提供24/7的服務,隨時隨地可訪問,具備可擴展性、分佈式、互操作性,並創建了一個實時的多用戶協作平台。

本論文為最佳儲水庫操作和雲應用平台貢獻了新的、更強大的算法。所有源代碼均可供公眾使用,研究人員和實踐者可以利用這些代碼進一步推進相關領域的發展。