Maintainability plays a fundamental role for achieving success in software system and it is con-sidered as an important quality characteristics. Maintainability may be predicted efficiently by us-ing soft computing techniques as they provide good results. In this paper similarity- based ap-proach is used with the contribution of fuzzy Analytical Hierarchical Process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) at 2-level hierarchy. Here similarity- based approach illustrates the combine approach of fuzzy AHP and fuzzy TOPSIS. This approach is used to provide the rank of software to select the best one for maintainability estimation. Also, several factors are presented that influence the software maintainability. These factors are taken as criterion and three software products are taken as alternatives.
Public organization’s performance depends on multiple aspects in which there are different polit-ical and public actors involved. In this study, we discuss the innovation performance of public organizations’ in Hefei (Anhui province), China. Our targeted group for this study were public sector employees at different levels within the organizations being considered. We checked the effect of absorptive capacity (ACAP), and dominant logic on public organization’s innovation performance. We found that absorptive capacity and dominant logic had a positive relation-ship with firms’ performance. Furthermore, these predictors were not only improving firm’s per-formance, but also bring innovation into the public organizations.
Software development with minimum effort has become a challenging task for the software de-velopers. Software effort may be defined as the prediction process of the effort required to de-velop any software. Many software effort estimation models have been developed in the past, but it is observed that none of them can be applied successfully in all kinds of projects in differ-ent environments that raise the problem of the software effort estimation model selection. To se-lect the suitable software effort estimation model, many conflicting selection criteria must be con-sidered in the decision process. The present study emphasizes on the development of a fuzzy multi-criteria decision making approach by integrating Fuzzy Set Theory and Weighted Distance Based Approximation. To show the consistency of the proposed approach, methodology valida-tion is also performed by making comparison with existing methodologies and to check the criti-cality of the selection criterion, sensitivity analysis is also performed.
In this paper, a subjective and objective fuzzy-based Analytical Hierarchy Process (AHP) model is proposed. The model which is based on a newly defined evaluation matrix replaces the fuzzy comparison matrix (FCM) in the traditional fuzzy AHP model, which has been found ineffective and time-consuming when criteria/alternatives are increased. The main advantage of the new model is that it is straightforward and completely eliminates the repetitive adjustment of data that is common with the FCM in traditional AHP model. The model reduces the complete dependen-cy on human judgment in prioritization assessment since the weights values are solved automati-cally using the evaluation matrix and the modified priority weight formula in the proposed mod-el. By virtue of a numerical case study, the model is successfully applied in the determination of the implementation priorities of lean practices for a product development environment and com-pared with similar computational methods in the literature.
In this article, we present an acceptance sampling plan for machine replacement problem based on the backward dynamic programming model. Discount dynamic programming is used to solve a two-state machine replacement problem. We plan to design a model for maintenance by consid-ering the quality of the item produced. The purpose of the proposed model is to determine the optimal threshold policy for maintenance in a finite time horizon. We create a decision tree based on a sequential sampling including renew, repair and do nothing and wish to achieve an optimal threshold for making decisions including renew, repair and continue the production in order to minimize the expected cost. Results show that the optimal policy is sensitive to the data, for the probability of defective machines and parameters defined in the model. This can be clearly demonstrated by a sensitivity analysis technique.