International Journal of Industrial Mathematics
Volume:16 Issue: 1, Winter 2024

A line location problem as a subdivision of facility location problem deals with finding a straight line in the plane, so that sum of weighted distances or maximum weighted distances from the line to the demand points is minimized. This paper provides a novel approach to study the semi-obnoxious median line location problem with Euclidean norm by using data envelopment analysis method. Since the presented procedure would consider lines as decision making units, so efficiency of lines is taken into account instead of their optimality. Moreover, due to the inherent uncertainty of the parameters of the line location problem in the real world such as weights or/and coordinates of demand points, the problem under interval data is studied in viewpoint of data envelopment analysis as well. Furthermore, some propositions and a numerical example are provided to investigate the problem. Finally, conclusion is given.

In this paper, we convert the fuzzy clustering ensemble consensus function problem into an optimization problem based on the reliability-based co-association matrix that minimize distance between co-association matrix of final clustering and co-association matrix of base-clusterings in the ensemble. The optimization problem is a constrained nonlinear objective function and we solve it by sparse sequential quadratic programming (SSQP).

This research will identify the best financial ratios and the best method for forecasting the probability of fraud in the financial statements of approved companies, taking into account the financial significance of decision-making as well as the rise in fraud statistics and its detrimental effects. The statistical sample consisted of 180 companies listed on the stock exchange in Tehran from 2014 to 2021 (532 years of companies -years suspected of fraud and 908 years of non-fraudulent companies). First, by looking at the theoretical underpinnings, 96 financial ratios were extracted, k-nearest neighbor and the Bayesian network, support vector machine, and combined method (bagging) were used to predict fraud in financial statements. The findings reveal that, in general, the methods don't meet the evaluation standards. The gray wolf optimization algorithm, which has an accuracy of 70.60% and a proportionality function value of 0.2940, was thus used to reduce the ratios in order to improve performance. After 31 iterations, 9 appropriate financial ratios were obtained. The effectiveness of the proposed fraud prevention strategies was then assessed again using the extracted financial ratios. The results show that after lowering the financial ratios, all of the proposed methods perform better. The accuracy and efficiency of the proposed methods are respectively 79.25% and 81.70% in the combined method (begging), support vector machine 75.83% and 80.30%, Bayesian network 72.01% and 74.60%, and k- nearest neighbor 74.55%. % and 75.60%, which shows the higher accuracy and efficiency of the combined method (begging) compared to other methods.

The current study aims to introduce the notions of intuitionistic fuzzy multiset finite automata (IFMFA) concerning a given IFMFA M with states Q. For a subset T of Q, we present the notion of intuitionistic fuzzy multiset submachine generated by T. Furthermore, the behavior of IFMFA is studied and explicated by using algebraic techniques. Further, it is shown that the union and the intersection of a family of an IFMFSA are IFMFSA, as well. Subsequently, it is proved that if IFMFA M has a basis, then the cardinality of the basis is unique. Moreover, the language of IFMFA is examined and some theorems are suggested.

DEA is a nonparametric method for calculating the relative efficiency of a DMU that yields to a reference target for an inefficient DMU. However, it is very hard for inefficient DMUs to be efficient by benchmarking a target DMU which has different inputs. Finding appropriate benchmarks based on the similarity of inputs makes it easier for an inefficient DMU to try to be like its target DMUs. But it is rare to discover a target DMU, which is both the most efficient and similar in inputs, in real situation. Therefore, it is necessary to find the most similar and closest real DMU in terms of inputs on the strong efficiency frontier, which has the highest possible output. In this paper, a combination of the Enhanced Russell model and the additive model is proposed as a new model to improve the efficiency of the inefficient DMUs. The proposed model is applied on a dataset of a large Canadian Bank branches. The target introduced by the proposed method is more practical target for the evaluated unit. The inefficient unit can improve its efficiency more easily by this benchmark.