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.
AUTHOR KEYWORDS: Business intelligence; Enterprise systems; Fuzzy PROMETHEE; Fuzzy theory; PROMETHEE PUBLISHER: Halmstad University
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.
AUTHOR KEYWORDS: Facility layout problem; Multi-line layout problem; Tabu search INDEX KEYWORDS: Algorithms; Computational complexity; Industrial plants; Manufacture; Materials handling; Plant layout; Tabu search, Computational results; Facility layout problems; Line layout; Material handling costs; Meta heuristics; Multi-products; Objective functions; Search-based algorithms, Computational efficiency PUBLISHER: Computers and Industrial Engineering
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
AUTHOR KEYWORDS: Ant colony algorithm; Hierarchical covering location problem generalized hierarchical covering location problem INDEX KEYWORDS: Algorithms; Integer programming; Manufacture; Sales, Ant colony algorithms; Ant colony based algorithms; Computation time; Covering location problems; Level of Service; Mixed integer programming; Partial coverage; Solution procedure, Ant colony optimization PUBLISHER: Computers and Industrial Engineering
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