Abstract
This paper aims to develop the extension of intersection method for multi-objective optimization under condition of interval number. Based on the linear correlation of partial favourable probability and the corresponding performance indicator, and the assumption of uniform distribution of the actual value of performance indicator within the range of its lower and upper limits in case of interval number, it derives that the actual partial favourable probability of a performance indicator is the arithmetic mean value of the partial favourable probabilities of the arithmetic mean value and the variation value of the interval index of the corresponding performance indicator for each candidate, or their desired sum. Furthermore, according to the rule of algorithm for the total favourable probability quantitatively, all candidates are ranked according to their total favourable probabilities to complete the multi- objective optimization in case of interval number. As applications, the quantitative assessments of multi-criteria selections for effective dwelling house walls, project managers and contractor for construction works are given in detail, satisfied results are obtained.
Highlights
Multi-objective optimization (MOO) is the process of specifying the optimal solution from all feasible alternatives
Jahanshahloo et al proposed an extension of the technique for order preference by similarity to ideal solution (TOPSIS) to deal with decision making problems with interval numbers [3]
Maosheng Zheng, et al.: Extension of Intersection Method for Multi-Objective Optimization in Case of Interval Number and its Application order to deal with such decision problem with uncertain elements, proper approach is in need
Summary
Multi-objective optimization (MOO) is the process of specifying the optimal solution from all feasible alternatives. In. Maosheng Zheng, et al.: Extension of Intersection Method for Multi-Objective Optimization in Case of Interval Number and its Application order to deal with such decision problem with uncertain elements, proper approach is in need. The main feature of the new "intersection" method for MOO is that there is no necessary to make normalization for the decision-making matrix [11], the partial favourable probability can be obtained from the decision-making matrix directly for the performance index with exact value, and the total favourable probability is the overall representative in the viewpoint of probability theory, which is the unique decisive index in the competitive selection process. The extended intersection method for MOO in case of interval number is applied to deal with the following multi-criteria decision-making problems
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