نشریه مدیریت صنعتی
سال پنجم شماره 10 (بهار و تابستان 1392)

Due to Project evaluation complexity and resource constraints، the project portfolio optimization is numerous decision making challenges. Hence، many researches have been done to introduce model and methods for portfolio optimization. But most of them have not considered the interaction between projects. Considering the interactions between projects increase complexity of portfolio optimization problem and if these interactions be ignored، the efficiency of decision making process and final portfolio utility maybe influenced dramatically. In this paper، the portfolio optimization problem with considering interactions between the projects is formulated. Then، The portfolio optimization problem with regard the interactions between projects using ICA optimization algorithm was investigated. Interactions at the projects optimization، final solution and its fitness show the important effects. It was shown ICA method is better in comparison with the GA، the PSO and CPSO algorithm technique used before in such problems.

Radial models of DEA to measure the efficiency of DMUs assumed an inefficient unit to be efficient must decrease (increase) proportional all inputs (outputs) as the same. This assumption is absolutely false، unnecessary and unrealistic، therefore، to address this deficiency، and raise awareness of how to use resources، it is so realistic that expect the various inputs have different efficiency. Also، due to uncertainty in the human thinking and judgment، and fuzzy DEA models can play an important role for evaluating the performance of the real issues. In this study، to evaluate the performance of the two inputs and two outputs faculty of Tehran University non-radial fuzzy input efficiency Profiling model was used. After solve of 180 models at various cutting level for two inputs efficiency we obtained input efficiency as fuzzy numbers that ranked by method of Chen and Klein، then the complete ranking of colleges with suggested technique FIEP / AHP results in the Faculty of Letters and Humanities and the Faculty of Law and political Science ranked as the first and the last respectively

An integrative approach of Data Envelopment Analysis (DEA) and Malmquist Productivity indicator for performance analysis of projects in Subsea R&D Center (SRDC) is presented. It is noticeable that، an output based model with constant efficiency was used for data analysis. Furthermore، since the DEA approach is merely used for homogeneous units، it was assumed that all the projects according to objectives in the project based organization، i. e. time، cost and acceptable quality were homogenised. However، results of this research are limited to an exact time and are related to special projects for the associated organization. Findings imply feasibility of using this model for measuring productivity improvement of projects in project based organizations. The innovation of this research may consider to integrative of DEA and Malmquist indicatorfor projects analysis and performance sensitivity analysisas well.

World Class Manufacturing (WCM) is a key concept in the contemporary world that has attracted the attention of many industries. Many of the organizations in order to compete in the global market use world class manufacturing system. There are different views about the philosophy and concepts of WCM features، but what is considered as a key element is to achieve performance consistent with the «best in the world». Many factors that contribute to the successful implementation of world class manufacturing have been identified، but a little research on the evaluation of these factors has been done in developing countries. The main objective of this paper is to present a technique that assesses key factors of the successful implementation of world class manufacturing and shows the impact of these factors on the implementation of world class manufacturing in the form of a numeric index that is attributed to the organization. This technique combines two approaches that are interpretive structural modeling (ISM)، graph theory and matrix approach (GTMA). Also، the numerical index of the technique provides the capable of comparing and ranking organizations in terms of the WCM implementation. To implement this technique in two automobile companies (Iran Khodro and Saipa) important factors in the implementation of world-class Manufacturing have been identified and assessed

The purpose of this study was to evaluate the performance of organization branches in Tehran by using the hybrid method TOPSIS، AHP، BSC، so that the deficiencies of such a subjective and selective attention Leaders (assessors) in the evaluation and integration of yield loss with In terms of the contribution of each group to achieve the goals to provide BSC. In this study، a multi-criteria decision making approach for evaluating the performance of the branches of Organization as a IT company، offers. Methods used in the study is experimental. Data were collected by interviews، questionnaires، and study documents in the organization. The data were analyzed using SPSS and Expert Choice softwares.

The aim of this work to consider average influence of industry average size and primary capital on manufacturing companies’ survival of Mazandaran province in 1360-1390 years. Data and information have been obtained from database in mining and industries office of Mazandaran province and by using eventual- historical analyze method the efficacy of independent variables on companies survival has been determined. It has been used «product limit estimator model» (Kaplan-Mayer) to data analyze. For examining hypotheses، Cox regression semi parametric model (Hazard Function) and comparing survival functions have been used. In result of research، a meaningful relation between average size and primary capital of industry with manufacturing companies’ survival has been confirmed. According to comparing survival functions of industries، it was concluded that average size and primary capital influence in each industry، is different with other industries

In this context and for helping to manufacturing industries in this research an attempt has been made to provide a method to managers of evaluating and ranking of agility strategies by using of a fuzzy inference system which is a branch of artificial intelligence. This research has been performed in three sequential phases. Firstly، some variables، as factors of agility drivers، agility capabilities and agility strategies، have been extracted from literature of research. Agility driver factors of industry، based on importance of attention and responsibility، have been ranked by using fuzzy TOPSIS technique and key drivers have been selected. Secondly، agility capabilities، based on the effect of capability to overcome the key drivers، have been ranked with the use of fuzzy TOPSIS technique and then essential capabilities have been selected. Lastly، the strategies، based on the effect of strategies to improve the essential capabilities، have been evaluated by planning and using a fuzzy inference system.

MUSA is one of the novel techniques in CSA، which lays its foundation on linear goal programming، developed for overcoming prior CSA models’ weaknesses such as coping with ordinal nature of data and low fitness. Employing a simple questionnaire، MUSA develops the interval scale and the level of satisfaction as well as determining its determinants in addition to several fruitful indices. This paper aims to review the contemporary CSA literature while being more elaborated on MUSA’s methodology. According to that، after defining its modeling approach، MUSA compared to ordinary and ordinal regression models in case of banking firm. Findings depicted that MUSA in addition to criterion validity outperforms regression models where as it is vulnerable to asymmetrical data sets. The paper at the end reviews the weak points of multi criteria satisfaction analysis methods، hopping that، marketing scholars and practitioners effectively put them in practice