Water resources planning under budgetary uncertainty: the case in Indonesia

Sutardi, Edwin Tai Chiu Cheng, Ian Goulter

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Due to the limitations of existing stochastic programming techniques for hardling problems of sequential decision making under budgetary uncertainty, a stochastic dynamic programming (SDP) modelling framework is proposed to assist in the planning process under budgetary uncertainty. Probabilities of the funding levels in any time period, defined as a function of time period (stage), level of development, level of previous funding, level of possible decision and level of actual funding, are employed to handle budgetary uncertainty. Sensitivity aanalysis can then be performed to examine the impact of various changes in the subjectively derived probability assignments on the optimal decisions. Application of the model primarily yields an optimal planning policy that recognizes the possibility that the actual funding received is less than the anticipated and therefore the projects being implemented under the anticipated budget are interrupted. However, the model is also able to provide a guide if, for any reason, actual funding at anytime period is greater than that anticipated, as long as the budgetary changes are within the range of the projected levels. Furthermore, use of the SDP approach facilitates the application of a detailed optimisation approach for development of the returns at each stage of the SDP. This second level optimisation model can be used to handle other types of uncertainty and multi-objective issues in the overall approach.
Original languageEnglish
Pages (from-to)201-217
Number of pages17
JournalInternational Journal of Production Economics
Volume25
Issue number1-3
DOIs
Publication statusPublished - 1 Jan 1991
Externally publishedYes

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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