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Updated: Sep 20, 2025
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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning
Abd Elazeem M Abd Elazeem1, Hamiden Abd El-Wahed Khalifa2,3, Dragan Pamucar4
1High Institute of Marketing, Commerce and Information System, Cairo, Egypt.
Computational Intelligence and Neuroscience
|June 10, 2022
Summary
This study introduces an efficient algorithm for interval coefficient linear programming (ICLP) problems, crucial for decision-making under uncertainty. The method incorporates decision-maker preferences for robust solutions in uncertain environments.
Area of Science:
- Operations Research
- Decision Science
Background:
- Real-world problems often involve uncertain environments, necessitating robust modeling techniques.
- Interval coefficient linear programming (ICLP) addresses this by incorporating interval uncertainty.
- Optimal solutions in ICLP models carry inherent risks regarding optimality and feasibility.
Purpose of the Study:
- To develop a novel and efficient algorithm for solving interval coefficient linear programming (ICLP) problems.
- To provide a method that effectively handles decision-making under uncertainty.
- To integrate decision-maker preferences into the solution process for greater satisfaction.
Main Methods:
- A new algorithm is proposed for ICLP problems.
- Novel measures including optimality ratio, feasibility ratio, and normalized risk factor are utilized.
- The algorithm incorporates a utility function and decision-maker input for solution selection.
Main Results:
- The proposed algorithm offers a novel and effective analysis of ICLP problems.
- Numerical examples demonstrate the algorithm's robustness and efficiency compared to existing methods.
- The algorithm successfully handles the inherent risks associated with uncertain environments.
Conclusions:
- The developed algorithm provides an efficient approach to ICLP problems, enhancing decision-making under uncertainty.
- The integration of decision-maker preferences leads to more satisfied and reliable optimal solutions.
- The algorithm's applicability is validated through a Solid Waste Management Planning case study.
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