R. Tavakkoli-Moghaddam, S. Mahmoodi,
Volume 21, Issue 2 (5-2010)
Abstract
A data envelopment analysis (DEA) method can be regarded as a useful management tool to evaluate decision making units (DMUs) using multiple inputs and outputs. In some cases, we face with imprecise inputs and outputs, such as fuzzy or interval data, so the efficiency of DMUs will not be exact. Most researchers have been interested in getting efficiency and ranking DMUs recently. Models of the traditional DEA cannot provide a completely ranking of efficient units however, it can just distinguish between efficient and inefficient units. In this paper, the efficiency scores of DMUs are computed by a fuzzy CCR model and the fuzzy entropy of DMUs. Then these units are ranked and compared with two foregoing procedures. To do this, the fuzzy entropy based on common set of weights (CSW) is used. Furthermore, the fuzzy efficiency of DMUs considering the optimistic level is computed. Finally, a numerical example taken from a real-case study is considered and the related concept is analyzed.
Hossein Akbaripour, Ellips Masehian,
Volume 24, Issue 2 (6-2013)
Abstract
The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristic and metaheuristic algorithms have a great influence on their effectiveness and efficiency, parameter tuning and calibration has gained importance. In this paper a new approach for robust parameter tuning of heuristics and metaheuristics is proposed, which is based on a combination of Design of Experiments (DOE), Signal to Noise (S/N) ratio, Shannon entropy, and VIKOR methods, which not only considers the solution quality or the number of fitness function evaluations, but also aims to minimize the running time. In order to evaluate the performance of the suggested approach, a computational analysis has been performed on the Simulated Annealing (SA) and Genetic Algorithms (GA) methods, which have been successfully applied in solving respectively the n-queens and the Uncapacitated Single Allocation Hub Location combinatorial problems. Extensive experimental results showed that by using the presented approach the average number of iterations and the average running time of the SA were respectively improved 12 and 10.2 times compared to the un-tuned SA. Also, the quality of certain solutions was improved in the tuned GA, while the average running time was 2.5 times faster compared to the un-tuned GA.
