Mansoureh Maadi

Recently, diabetes becomes the widespread and major disease in the world. In this paper, we propose a novel hybrid classifier for diabetic diseases. The proposed hybrid classifier named Logistic Adaptive Network-based Fuzzy Inference System (LANFIS) is a combination of Logistic regression and Adaptive Network-based Fuzzy Inference System. Our proposed intelligent system does not use classifiers to continuous output, does not delete samples with missing values, and does not use insignificant attributes which reduces number of tests required during data acquisition. The diagnosis performance of the LANFIS intelligent system is calculated using sensitivity, specificity, accuracy and confusion matrix. Our findings show that the classification accuracy of LANFIS intelligent system is about 88.05%. Indeed, 3–5% increase in accuracy is obtained by the proposed intelligent system and it is better than fuzzy classifiers in the available literature by deleting all samples to missing values and applying traditional classifiers to different sets of features. © 2017 Faculty of Engineering, Alexandria University

In recent years, new meta-heuristic algorithms have been developed to solve optimization problems. Recently-introduced Cuckoo Optimization Algorithm (COA) has proven its excellent performance to solve different optimization problems. Precedence Constrained Sequencing Problem (PCSP) is related to locating the optimal sequence with the shortest traveling time among all feasible sequences. The problem is motivated by applications in networks, scheduling, project management, logistics, assembly flow and routing. Regarding numerous practical applications of PCSP, it can be asserted that PCSP is a useful tool for a variety of industrial planning and scheduling problems. However it can also be seen that the most approaches may not solve various types of PCSPs and in related papers considering definite conditions, a model is determined and solved. In this paper a new approach is presented for solving various types of PCSPs based on COA. Since COA at first was introduced to solve continuous optimization problems, in order to demonstrate the application of COA to find the optimal sequence of the PCSP, some proposed schemes have been applied in this paper with modifications in operators of the basic COA. In fact due to the discrete nature and characteristics of the PCSP, the basic COA should be modified to solve PSCPs. To evaluate the performance of the proposed algorithm, at first, an applied single machine scheduling problem from the literature that can be formulated as a PCSP and has optimal solution is described and solved. Then, several PCSP instances with different sizes from the literature that do not have optimal solutions are solved and results are compared to the algorithms of the literature. Computational results show that the proposed algorithm has better performance compared to presented well-known meta-heuristic algorithms presented to solve various types of PCSPs so far. © 2017, Springer Science+Business Media, LLC.

In this paper, a new model to evaluate business intelligence (BI) for enterprise systems is presented. Evaluation of BI before making decisions about buying and deployment can be an important decision support system for managers in organizations. In this paper, a simple and practical method is presented that evaluates BI for enterprise systems. In this way, after reviewing different papers in the literature, 34 criteria for BI specifications are determined, and then by applying fuzzy PROMETHEE, different enterprise systems are ranked. To continue to assess the proposed model and as a case study, five enterprise systems were selected and ranked using the proposed model. The advantages of PROMETHEE over other multi-criteria decision making methods and the use of fuzzy theory to deal with uncertainty in decision making is assessed and it is found that the proposed model can be a useful and applied method to help managers make decisions in organizations.

In transportation planning models, decision makers try to estimate the number of trips between two or several origin-destination. In this field decision makers use trip distribution to have a good prediction. Since in the real world trip distribution problems often have more than one objective, multi objective models aredeveloped to cope with a set of conflict goals. In this paper, the goal is optimization of three objective functionssimultaneously, i.e. (1) maximizationof the interactivity of the system, (2) minimization of the generalized costsand (3) minimization of the deviation from the observed year. For optimizing this non-linear model, a Pareto-Simulated Annealing (PSA) method is developed and anew adapted operator to creating neighbors inSA algorithm is described. Ultimately A set of Hong Kong data has been used to test the efficiency of the proposed algorithm in comparison with a genetic algorithm that is applied in the literature. Computational results show the efficiency of proposed method.

Facility Layout problem is concerned with the arrangement of a number of facilities in a given space to satisfy an objective function. The multi-line layout problem assigns a few facilities in the two or more unequal lines into the industrial plant, where the number of facilities is less than the number of locations with no constraint for placing the facilities. Because FLP is a NP-hard problem there are several techniques based on meta heuristics to solve them. This paper proposes a tabu search-based algorithm for solving the multi-line layout problem with the multi-products by minimizing the total material handling cost. This algorithm has not been used in literature to solve multi line layout problems. Computational results show the efficiency of the proposed algorithm compared to other algorithms.

The classical Hierarchical Covering Location Problem (HCLP) is the problem of finding locations of facilities in several levels maximizing the number of covered customers, where the customers are assumed to be covered if they are located within a specific distance from the facility, and not covered otherwise. In the generalized Hierarchical Covering Location Problem (G-HCLP) the service coverage is also generalized in a way that the partial coverage is allowed if the distance from the facility is larger than the specified range although it is located in the covered distance. In G-HCLP customers asking a certain level of services can be served by the facility whose level is equal or higher. In this paper regarding to G-HCLP and a mixed integer programming formulation for this problem, a solution procedure based on ant colony algorithm is suggested, and high quality of solutions in a reasonable computation time is shown in several