A STOCHASTIC SIMULATION-BASED HYBRID INTERVAL FUZZY PROGRAMMING APPROACH FOR OPTIMIZING THE TREATMENT OF RECOVERED OILY WATER

In this paper, a stochastic simulation-based hybrid interval fuzzy programming (SHIFP) approach is developed to aid the decision-making process by solving fuzzy linear optimization problems. Fuzzy set theory, probability theory, and interval analysis are integrated to take into account the effect of imprecise information, subjective judgment, and variable environmental conditions. A case study related to oily water treatment during offshore oil spill clean-up operations is conducted to demonstrate the applicability of the proposed approach. The results suggest that producing a random sequence of triangular fuzzy numbers in a given interval is equivalent to a normal distribution when using the centroid defuzzification method. It also shows that the defuzzified optimal solutions follow the normal distribution and range from 3,000-3,700 tons, given the budget constraint (CAD 110,000-150,000). The normality seems to be able to propagate throughout the optimization process, yet this interesting finding deserves more in-depth study and needs more rigorous mathematical proof to validate its applicability and feasibility. In addition, the optimal decision variables can be categorized into several groups with different probability such that decision makers can wisely allocate limited resources with higher confidence in a short period of time. This study is expected to advise the industries and authorities on how to distribute resources and maximize the treatment efficiency of oily water in a short period of time, particularly in the context of harsh environments

Employing Fuzzy AHP in Modeling a Decision Support System for Determining Scholarship Recipients within the University Context

In the realm of university scholarship programs, the process of selecting deserving recipients presents a complex decision-making challenge. This study explores the integration of Fuzzy Analytical Hierarchy Process (AHP) into the modeling of a Decision Support System (DSS) aimed at facilitating the determination of scholarship awardees. The utilization of Fuzzy AHP enables a more comprehensive and nuanced evaluation of candidates by accommodating uncertainties and imprecisions inherent in the decision-making process. This research investigates the application of Fuzzy AHP within the specific context of university scholarship recipient selection. The proposed DSS framework not only enhances the objectivity and transparency of the decision-making process but also contributes to the optimization of resource allocation and the identification of candidates best aligned with the scholarship's objectives. By employing Fuzzy AHP in this decision-support context, universities can effectively address the intricate considerations involved in awarding scholarships, thereby promoting fairness and increasing the likelihood of rewarding the most deserving individuals

Ship Hull Principal Dimensions Optimization Employing Fuzzy Decision-Making Theory

The paper presents an optimization method for the ship hull principal dimensions scheme employing the fuzzy decision-making theory. First of all, the paper establishes the fuzzy decision-making model of the ship hull principal dimensions optimization, and then a series of ship hull principal dimensions schemes are accordingly constructed by employing the variable value method. On the basis of this, the fuzzy decision-making method is employed to evaluate the series ship hull principal dimensions schemes. Finally, the optimal ship hull principal dimensions scheme is obtained. The example demonstration verified the proposed method’s validity for ship hull principal dimensions optimization economic performance

Development of group decision making model under fuzzy environment

Multi criteria group decision making (MCGDM) methods are broadly used in the real-world decision circumstances for homogeneous groups. Some decision-makers’viewpoints at times are more important or reliable than others, or they may differ in terms of the decision-maker experience, education, expertise and other aspects. Thus, a heterogeneous group of decision makers with dissimilar members has to be composed in MCGDM. Multi-dimensional personnel evaluation is one of the most critical decisions to make in order to achieve the organization goals. In many situations, raters may decide on the basis of imprecise information coming from a variety of sources about ratee with respect to criteria. In fact, some criteria are completely quantifiable, some partially quantifiable, and others completely subjective; moreover crisp data is inappropriate to model real-world circumstances. Linguistic labels or fuzzy preferences are therefore, used to deal with uncertain and inaccurate factors involved and seem more reliable in complex group decision situations. In this research, heterogeneous group decision making models under fuzzy environment for multi-dimensional personnel evaluation were proposed to compensate the differences of decision makers’ knowledge such as: education,expertise, experience and other aspects. A new fuzzy group decision making method was developed under the linguistic framework for heterogeneous group decision making that aims at a desired consensus. The method allocates different weights for each decision maker using linguistic terms to express their fuzzy preferences for alternative solutions and for individual judgments. Besides, the classical ordinal approach method under a linguistic framework is developed for heterogeneous group decision making, which allows group members to express their fuzzy preferences in linguistic terms for alternative selection and for individual judgments. Furthermore, a fuzzy extension of technique for order preference by similarity to ideal solution (TOPSIS) method under fuzzy environment was proposed. The method covers heterogeneous group decision making by considering the decision makers’ viewpoint weights. In order to solve the problem of discrepancy between decision making methods’ results, a new optimization method was developed, to aggregate the results’of different decision making models. The four proposed methods were used in a case study. Proposed methods focused on the implementation of fuzzy logic approach in the personnel evaluation system. Furthemore, personnel were evaluated from different points of view (supervisors,colleagues, inferiors and employee him/herself). A fuzzy Delphi method and linguistic terms represented by the fuzzy numbers were developed to elicit qualitative and quantitative criteria and assess criteria weights and relative importance of evaluation group’s viewpoints. Then, the proposed methods’ results were compared to already established methods. The study identified that the results of the proposed methods are closely related to other methods and the selections made by the proposed methods approximately are identical with the other already established methods. The Spearman’s rank correlation coefficient shows highly consistent rankings obtained by the methods. No significant difference in the ranking of the proposed methods and the other established methods was observed. The results of the problems solution based on the aggregated proposed model show that the aggregated model achieved the highest value in the Spearman’s rank correlation compared to the average method and Copeland function. Furthermore, the high Spearman’s rank correlation coefficient between the rankings supports the consistency of the results and similarity of applicability of the methods