Formulating Expectations for Future Water Availability through Infrastructure Development Decisions in Arid Regions

Water resources development options are usually selected on a least-cost basis. While economic considerations are dominant in choosing projects, there are also a mix of other factors including social demands, political viability, social equity, and environmental considerations that impact final decisions and development of water supply systems. Understanding local priorities in water resource management decisions can allow for forming expectations of future regional water availability through new infrastructure projects. In this research, we propose that future human mediated water availability in arid regions may be assessed by considering key projects that have been identified or proposed by regional experts and organizations. Using Multicriteria Decision Methods as a framework to organize a set of decision criteria and their relative salience, the likelihood of selection (and development) of a project can be determined and used to form expectations of future regional water availability. We apply this approach in a case study of Jordan, and find that large-scale desalination projects—that have been in the planning books for decades—are now most likely to be pursued and implemented in the country. Finally, we discuss strengths, limitations, and the general applicability of this method for assessing future water availability in other arid regions

Value of agreement in decision analysis: concept, measures and application

In multi-criteria decision analysis workshops, participants often appraise the options individually before discussing the scoring as a group. The individual appraisals lead to score ranges within which the group then seeks the necessary agreement to identify their preferred option. Preference programming enables some options to be identified as dominated even before the group agrees on a precise scoring for them. Workshop participants usually face time pressure to make a decision. Decision support can be provided by flagging options for which further agreement on their scores seems particularly valuable. By valuable, we mean the opportunity to identify other options as dominated (using preference programming) without having their precise scores agreed beforehand. The present paper quantifies this Value of Agreement and extends the concept to portfolio decision analysis and criterion weights. The new concept is validated through a case study in recruitment

Improvement of the sustainable olive mill wastewater (OMWW) management in the region of Sousse (Tunisia) via ArcGIS Software

In Tunisia, the evacuation of olive mill wastewater (OMWW) in storage tanks remains the most common way to manage this toxic effluent. Several environmental damages are registered due to the inappropriate locations of this OMWW tanks. Indeed, the storage tanks locations must be carefully selected where hydrogeological, environmental as well as socioeconomic criteria must be simultaneously considered. For this purpose, an integrated approach  based on Geographic Information System (GIS), Global Positioning System (GPS) and multicriteria method (Analytic Hierarchy Process AHP) were developed to asses OMWW tanks sustainable management in Sousse region (Tunisia). The present paper consists to evaluate the current management of OMWW tanks in comparison with the existing standards. The results showed that most of the sites were not in the appropriate zone and did not meet the necessary standards and the minimum requirement to put in a storage tank, as expressed by the exclusion criteria. This strongly emphasizes the seriousness of the deteriorated environmental situation in the region. An awareness of these dangers that threaten public health and have some other associated risks is important. Hard work to resolve this situation is urgently required

Machine learning-driven approach for large scale decision making with the analytic hierarchy process

The Analytic Hierarchy Process (AHP) multicriteria method can be cognitively demanding for large-scale decision problems due to the requirement for the decision maker to make pairwise evaluations of all alternatives. To address this issue, this paper presents an interactive method that uses online learning to provide scalability for AHP. The proposed method involves a machine learning algorithm that learns the decision maker’s preferences through evaluations of small subsets of solutions, and guides the search for the optimal solution. The methodology was tested on four optimization problems with different surfaces to validate the results. We conducted a one factor at a time experimentation of each hyperparameter implemented, such as the number of alternatives to query the decision maker, the learner method, and the strategies for solution selection and recommendation. The results demonstrate that the model is able to learn the utility function that characterizes the decision maker in approximately 15 iterations with only a few comparisons, resulting in significant time and cognitive effort savings. The initial subset of solutions can be chosen randomly or from a cluster. The subsequent ones are recommended during the iterative process, with the best selection strategy depending on the problem type. Recommendation based solely on the smallest Euclidean or Cosine distances reveals better results on linear problems. The proposed methodology can also easily incorporate new parameters and multicriteria methods based on pairwise comparisons.This research was funded by National Funds through the FCT—Portuguese Foundation for Science and Technology, References UIDB/05256/2020 and UIDP/05256/2020

Structuring e-learning multi-criteria decision making problems

Problem structuring is one of the most critical phases of decision making process. A well-posed problem has direct impact on effective decision making, especially when we use the multi-criteria decision making methods. There are different decision making methods that have been used for decision making on e-learning issues in higher education, but the most suitable method for this kind of problems is the Analytic Network Process (ANP). ANP meets all the theoretical requirements of decision making in higher education, but policy makers use it very rarely in practice because of its implementation weaknesses. One of the weaknesses is a lack of support in structuring problem in the form of a network. This paper brings an overview of several problem structuring methods and approaches, such as simple top-down and bottom-up approaches, the PrOACT approach, ISM (Interpretative Structural Modelling), DEMATEL (Decision Making Trial and Evaluation Laboratory) and the PAPRIKA structuring method. It also brings analysis of how those structuring methods and approaches help overcome some of the ANP weaknesses. Finally, we provide some recommendations of how to design a new problem structuring method that fits the ANP needs

A game-theoretic optimisation approach to fair customer allocation in oligopolies

Under the ever-increasing capital intensive environment that contemporary process industries face, oligopolies begin to form in mature markets where a small number of companies regulate and serve the customer base. Strategic and operational decisions are highly dependent on the firms’ customer portfolio and conventional modelling approaches neglect the rational behaviour of the decision makers, with regards to the problem of customer allocation, by assuming either static competition or a leader-follower structure. In this article, we address the fair customer allocation within oligopolies by employing the Nash bargaining approach. The overall problem is formulated as mixed integer program with linear constraints and a nonlinear objective function which is further linearised following a separable programming approach. Case studies from the industrial liquid market highlight the importance and benefits of the proposed game theoretic approach