A Scenario-based Interval Multi-objective Mixed-integer Programming Model for a Water Supply Problem: An Integrated AHP Technique

Ucler N., Gonce Kocken H.

Water Resources Management, vol.37, no.15, pp.5973-5988, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 15
  • Publication Date: 2023
  • Doi Number: 10.1007/s11269-023-03638-2
  • Journal Name: Water Resources Management
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Compendex, Environment Index, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.5973-5988
  • Keywords: AHP, Interval parameters, Mixed-integer programming, Water supply problem
  • Yıldız Technical University Affiliated: Yes


Rapid population growth, industrialization, and lifestyle modernization all increase water demand. However, water supplies are dramatically decreasing due to declining and irregular precipitation and the excessive use and deterioration of existing resources. This situation places tremendous pressure on decision-makers, who must implement plans to create new water supplies in regions likely to experience water shortages in the future. Deciding which projects to implement among various alternatives is challenging with a limited budget. This study aims to create a feasible strategic plan to select the most suitable alternative projects by proposing a multi-objective mixed-integer programming approach to the water supply problem. Considering several criteria, including chance of success, ease of application, nature-friendliness, and project prestige level, the proposed model is integrated using the analytical hierarchical process technique. Decision-makers’ views of the project alternatives are reflected by weights in the model. Also, interval numbers represent the costs of alternatives to handle the problem more realistically. A real-life situation is simulated under various scenarios to test the proposed model. The results show that the proposed integrated model generates more applicable solutions than a classic multi-objective optimization model.