Parviz Ahmadi; mohamadreza amini; adel Azar
Volume 17, Issue 4 , January 2014, , Pages 65-95
Abstract
The necessity of restructuring university budgeting from program-based to performance-based budgeting (PBB) leads many studies to be conducted. Reviewing the literature, we observed that there is no mathematical model including Dual PBB Structure in University. The purpose of this paper is to construct ...
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The necessity of restructuring university budgeting from program-based to performance-based budgeting (PBB) leads many studies to be conducted. Reviewing the literature, we observed that there is no mathematical model including Dual PBB Structure in University. The purpose of this paper is to construct a PBB model for university so that the budget is allocated not only to plans based on their priorities, but also to faculties based on approved student per Capita by the Ministry of Sciences, Researches and Technology. Considering the various criteria in the university system and because of fuzzy and stochastic uncertainties in the problem parameters, two scenarios were analyzed. 1) Crisp Lower Bound Robust Fuzzy PBB (CLB-RFPPB) model 2) Fuzzy Lower Bound Robust Fuzzy PBB (FLB-RFPPB) model. A significant point in Both of them, is to use performance Indicators--calculated based on data envelopment analysis approach (DEA) regarding a basic model of input-oriented CCR— determined the importance coefficient for each educational groups in order to allocateing budget. In addition, the weights of Goals and the priority of each plan were determined using some paired comparison questionnaires completed by experts. The PBBGP model has 5 goals, 1142 constraints and 994 decision variables. The results of models solution- that are presented on two macro and Operational Levels- and simulation of Crisp and two RFPBB models, Demonstrate high level capabilities of RFPBB models in response to the uncertainty in the parameters of problem and also managing the level of decision risk
AliReza Emami; Saiede Ketabi
Volume 17, Issue 1 , February 2013, , Pages 149-168
Abstract
Decision-making in competitive situations is a challenge for managers. Identifying the strategic success factors (SSF) for each company and determining the company’s position relative to its competitors due to the SSF are critical, which top managers use for survival in the competitive conditions. ...
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Decision-making in competitive situations is a challenge for managers. Identifying the strategic success factors (SSF) for each company and determining the company’s position relative to its competitors due to the SSF are critical, which top managers use for survival in the competitive conditions. The aim of this paper is to create a quantitative method to determine the competitive benchmarking. In this study, average cost of production, company's brand equity, product quality, marketing and sales capabilities, purchasing capabilities, customer satisfaction, production flexibility, engineering capabilities, technical research capabilities, and the number of R & D projects were identified as critical success factors by the experts of Profile and Pipe Steel Industry. The technique for order preference by similarity to ideal solution was used to determine the positive benchmark (as positive ideal solution) and negative benchmark (as negative ideal solution). Then by considering the budget constraint, binary goal programming model was used to maximize the positive deviation between each rival and the negative benchmark, and to minimize the negative deviation between each rival and the positive benchmark. The results of the proposed model showed that which SSF rival should be considered to reach the positive benchmark. Keywords:
Adel Azar; Bijan Nahavandi; Ali Rajabzadeh
Volume 12, Issue 4 , January 2009, , Pages 37-68
Abstract
In both the quality improvement and the design of a product, primarily the engineering characteristics affecting product performance are identified and improved to optimize customer needs (CNs). Especially, the limited resources, increased market competition and product complexity require a customer-driven ...
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In both the quality improvement and the design of a product, primarily the engineering characteristics affecting product performance are identified and improved to optimize customer needs (CNs). Especially, the limited resources, increased market competition and product complexity require a customer-driven quality management and product development system to achieve higher customer satisfaction. Quality Function Deployment (QFD) and its House of Quality (HOQ) are used as powerful tools for improving product design and quality, and procuring a customer-driven quality system. In this study, an integrated framework based on fuzzy-QFD and a goal programming model was proposed to determine the technical requirements (TRs) to be considered in designing a product. The coefficients of the mathematical model were obtained from a fuzzy analytic network process (ANP) approach. Furthermore, the proposed analytic procedure should take into account the multi-objective nature of the problem, and thus, incorporate other goals such as cost, extendibility, technological feasibility and competitiveness of TRs. Finally, the model, a zero–one goal programming methodology, includes the important levels of TRs deriven using the fuzzy ANP, cost budget, feasibility level, extendibility level , and competitiveness level as system constraints to determine the TRs to be considered in designing the product. An application for YEKTA washing machine powder in PAKNAM Company (Tehran, Iran) producing detergent products was presented to illustrate the proposed framework.
Alireza Torkashvand; Adel Azar
Volume 10, Issue 1 , April 2006, , Pages 1-23
Abstract
Each of the performance assessment models is an instrument that after implementing it can give the decision makers different information. So it is inevitable to use these models to answer the questions and problems that has been arizen in decision makers’ mind.
Therefore, in this article we want to ...
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Each of the performance assessment models is an instrument that after implementing it can give the decision makers different information. So it is inevitable to use these models to answer the questions and problems that has been arizen in decision makers’ mind.
Therefore, in this article we want to explain the mathematic model of the suitable Data Envelopment Analysis, grading efficiency of instruction groups, the weak and strength points of each group and the situation of optimum use of accessible sources in the human sienceses faculty from the view point of DEA. Since the different models of DEA have been developed during last years to be used in different areas, one of the most important steps before evaluating surveyed units is choosing a model or models suitable with them.
This article presents a collection of consecutive steps, in a conceptual framework in order to choose correctly a performance evaluation model. These stages should be performed one after the other, otherwise the credits of evaluation model can scrafeh due to the natural weak nesses of DEA method.
