Year:2008 | Volume:41 | Issue:7 (109)|Papers Count:14

In engineer world design of experiment is of significant important tools for modification and improvement of production process performance .In definition of experiments،we are usually interested in determination which process variables are effective in response. Basically the next procedure is optimization،it means defining important factors which results in best possible answer. For effective variables definition we may use DOE techniques, Design of experiments (DOE) is a strategic planning, conducting, analyzing and interpreting experiments so that valid conclusions can be drawn efficiently and economically. It has proved to be very effective for improving the process yield, process performance, and process variability .for optimization procedure we can use RSM and operation research (OR) techniques. Response surface methodology (RSM) is an empirical modeling approach using polynomials as local approximations to the true input/output relationship. This empirical approach is often adequate for process improvement in an industrial setting. By concise design of experiments, the objective is to optimize a response to (output variable) that is influenced by several independent variables (input variables). In that an experiment is series of tests, called runs, in which change are made in the input variables in order to identify the reasons for changes in the output response. operation research (OR) have been applied to real-word decision –making problems and MODM (Multiple Objective Decision Making) techniques have been applied to solve practical problems such as academic planning, systems reliability, transportation planning and so on. In these techniques, fuzzy set theory has been applied in linear and non-linear programming, integer programming, dynamic and net work programming and so on. It helps to improve over simplified (crisp) models and provides more robust and flexible models for real-word complex systems, especially those involving human aspects. In this paper, as the first case study, identification of effective factors on product quality and duration of pride 141 rear quarter draw mold is carried out by DOE and as two factorial design 23 and 24 .Also as the second case study, identification of effective factors in improvement of plastic injection molding process and determination of dependent variables are investigated in limitation of purposes which are pursued in DOE and in the shape of a factorial design ,and after that acceptable factors quantities and dependent and independent variables is defined and calculated by RSM, MODM and FMODM (Fuzzy Multiple Objective Decision Making) arguments (in both case studies).

This study considers scheduling in Hybrid flow shop environment with unrelated parallel machines for minimizing mean of job's tardiness and mean of job's completion times. This problem does not study in the literature, so far. Flexible flow shop environment is applicable in various industries such as wire and spring manufacturing, electronic industries and production lines. After modeling the problem as a mixed integer programming model, three heuristics named Cluster, H1 and H2 is proposed for solving it. The first heuristic (Cluster) algorithm utilizes clustering methods for determining sequence of jobs. The second and third heuristics (H1 and H2) determine sequence of jobs with using SPT and EDD rules, respectively. The experimental results of study show that Cluster algorithm outperforms H1 and H2. 

With respect to uncertainties in projects and the need to optimum allocation of resources, Risk management (RM) is considered as an important phase of the project management (PM) process. As well, ranking risk’s (RR) of the project is the critical step of the risk analysis phase in a RM. Risk ranking is the process of prioritizing the risks with the use of some criterions. In most standard methods only two indexes, probability of occurrence and consequence of risks, are being used in the risk analysis and risk ranking. Some other indexes such as “uncertainty of estimates” and “ability to response” have been used by some other researchers. There are both qualitative and quantitative methods to rank different risks of a project. Within this article, various MADM methodologies, as quantitative approaches, are studied in order to rank the risks of the projects. Each MADM methodology has its own limitations and attributes, and the decision maker cannot use them in all decision-making problems. Using MADM methodology to priority different alternatives of a decision problem needs to consider both the characteristics of the chosen methodology and attributes of the problem itself. Despite, reaching to wrong priorities of the alternatives would be unavoidable. Therefore, different MADM methods are studied from point of using them in a RR problem. Appropriate MADM methods for this purpose are discussed in this paper. It is shown that only compensatory methods can be used in a RR problem. Finally, a real world case study, ranking risks of a power plant renovation project, is presented. To solve the problem, four criteria are considered; including probability of occurrence, consequence of risk, ability to response, and uncertainty of estimates. The TOPSIS algorithm is implemented to solve the problem in the case study. It is suggested to implement more studies to find the most suitable MADM method for the purpose of ranking project risks.

In this paper, a supplier-retailer transportation system is investigated as a two-echelon environment. There is a single location in each echelon; the unique supplier at the first echelon has to replenish the retailer's warehouse at the second echelon. By the way, the shortage situation should be avoided. For this situation, a model is provided based on the traditional EOQ model. The inventory costs, ordering costs, transportation cost, etc. are considered in this model. Multistage shipment during each ordering period with a specific number of vehicles is allowed in the proposed model. The model's decisions involved in managing the system include design decision (i.e., optimized number of required vehicles), as well as operation decision (i.e., optimized order quantity and number of trips and transportation stages). A solution algorithm is proposed for the proposed model and implemented with C#.Net which is available and applicable on the website, namely www.PedramSahba.com. A numerical example and sensitivity analysis are presented for exposing the model and algorithm capability and then verifying and validating the model.

Nowadays, the problem of the flexibility in any organization has essential importance and the associated investigation with the organizational efficiency and performance is considered. In real-world situation, the process of decision making is often based on linguistic and mental data, so applying fuzzy logic in modeling at this point of view is convenient. In this paper, a fuzzy approach is applied for a developed organizational flexibility model considering both organizational efficiency and flexibility. In fuzzy environment, this model makes real conditions for the problem. This problem is solved in certain and fuzzy conditions and the associated results are presented.

Contractor evaluation and selection is one of the most important decision making problems that many regulations and methods have been introduced for it. In Iran, "Planning and Management Organization" is responsible for classification and prequalification of contractors and due to this responsibility, this organization edit the Contractors Classification and Prequalification Regulation regularly. This regulation should be applied in Public sector and project owners should fulfill their tenders with respect to the article of tenders by law. In general, the current system is a multistage process in which, the best contractor will be selected according to its propositional price for tenders, in final step. In this paper, a multi criteria decision making model is introduced which can consider all of quantitative and qualitative factors that may affect on contractor selection in tenders. Six general criteria are considered in this model as the most effective factors on selecting a contractor; some of these criteria have their own sub criteria. A Fuzzy MADM method (by using linguistic variables) is used to rank the contractors and to select the best contractor in tenders.

Electrical peak load forecasting has long been of attention to researchers and academics due to its significant role in effective and economic operation of power utilities. Peak load forecasting (PLF) for the subsequent day has a fundamental role in electrical power system operation, unit commitment and energy scheduling. Developing an accurate and robust peak load forecasting methodology can lead to a more accurate forecasting of electricity consumption. Furthermore, an accurate peak load forecast can significantly reduce the cost of operating power systems. Therefore, researchers have used various techniques in the past for peak load forecasting. Some have used time-series and linear regression models in PLF. In such methodologies, the relationship between independent variables and the dependent variable (the forecast) is determined through a mathematical equation which is usually linear. However, modeling the complex correlation between the load and input variables like weather conditions and differences between days of the week makes such methods quite difficult to use. Throughout the last two decades, a great deal of research has been devoted to using artificial neural networks (ANN) for PLF. ANN techniques, due to their high capability in non-linear modeling, have found widespread use in general forecasting and particularly in PLF. This paper proposes a hybrid neural network model which combines a self organizing map (SOM) and a feed forward neural network for daily electrical peak load forecasting. Since electrical peak load is strongly influenced by weather conditions, similar consumption patterns can be observed during the year. Thus, classifying data into somewhat similar clusters can lead to noise reduction and therefore higher accuracy. Different types of classifications have been proposed in the literature of PLF. However, most of the proposed methods seem to be intuitive with no justifiable reasoning. In this paper, we propose a new approach for clustering data by using a self-organizing map. Two SOM have been used successively; the first decreases the noise resulted by the temperature differences, dividing the days of the year into five categories. Then the second categorizes the data in each cluster to reduce the noise produced by the peak differences and to distinguish weekdays. The outcome of these two SOM is the categorization of the days of a year into 12 clusters. A feed forward neural network (FFNN) has been developed for each cluster to forecast the PLF. The application of the principal component analysis (PCA) reduced the dimensions of the network’s inputs and led to simpler architecture. Some influencing factors such as temperature, relative humidity, wind speed and cloud cover were introduced into the model. The daily peak load data in this research were extracted from Tehran Regional Electric Utility Company and the proposed hybrid model (PHM) was applied to these data. To evaluate the effectiveness of the PHM, forecasting has been performed by developing an FFNN that uses the un-clustered data, hybrid recurrent neural networks which consider only temperature, feed forward neural networks which use cluster data, and linear regression models. The results proved the superiority and effectiveness of the PHM.

Neural networks because of their abilities are used to patterns recognition. In statistical process control charts, a common cause variation distort expected form of unnatural patterns and so detection of assignable causes efficiently and precisely in a real-time is difficult. Therefore it would be logical to propose models based neural networks for recognition and analysis of patterns in process control charts. Nearly most of investigations of the application of neural networks to control chart patterns recognition solely have emphasized the detection of patterns and have not considered analysis and extraction detailed information which is important for effectively determining the assignable causes. Moreover, some of the patterns generator functions do not represent completely the real world situation. This paper proposes a model for discrimination and analysis of basic and concurrent patterns. This model first recognizes unnatural patterns. Then it estimates their starting point and finally determines the values of corresponding parameters. In design of proposed model, the development of areas of application has been emphasized. Numerical results indicate that the components of proposed model have suitable and effective performance

Nowadays, electricity is one of the most significant infrastructures required for economic development of countries. Hence, predicting electricity demand is very important especially in industry as a major electricity-consuming sector. The majority of studies under the subject of electricity demand have been performed by econometrics and it is not possible to investigate the effect of different policies on electricity demand. But system dynamics has the advantage of providing this possibility. Considering studies accomplished on electricity demand of industry, factors affecting electricity demand have been recognized as value-added, electricity tariff and equipment efficiency used in industry. Since efficiency is a qualitative concept, electricity intensity index has been used to measure efficiency of industries. Considering these factors and dividing electricity consumption to variable and constant consumption and using the concept of electricity intensity, electricity demand model of industry has been attained through system dynamics. The model has been run for years 1997 to 2003 and electricity consumption of model has been compared with real data in these years for validation of the model. Since error of model is small enough, electricity consumption of industry sector has been predicted up to year 2021.

Bargaining Power is one of the important issues in oil trade negotiations. In this paper two effective factors in bargaining power i.e. patient time to deal and outside options of each player have been considered. The necessary relations for exchange of sources in negotiation have been derived.

The paper presents an economical model for double variable acceptance sampling with inspection errors. Taguchi cost function is used as acceptance cost while quality specification functions are normal with known variance. An optimization model is developed for double variables acceptance sampling scheme at the presence of inspection errors with either constant or monotone value functions. The monotone value functions could be descending or ascending exponentially. In the case that inspection errors have exponentially functions, we can find the best value for inspection errors regarding to the sample number and other economical parameters. Finally sensitivity analysis has done on model parameters and some numerical examples are given to demonstrate how the developed model is applied.

The current dynamic and turbulent business environment has forced companies that are competing in global markets to change their traditional methods of conducting business. Recent developments in applying Information Technology (IT) offer the most exciting business opportunities in the marketplace. Organizations must re-evaluate every aspect of their strategies and quickly move to a working mode where the electronic commerce is essential for their success. One of tools that can be used for measuring the diffusion rate of IT is e-readiness assessment. Small and Medium Enterprises (SMEs) are critical to the economies of all countries, including developing ones. They cannot be left behind and many are already demonstrating their entrepreneurship strength by grasping opportunities offered by IT. The concept of e-readiness assessment for SMEs has received limited attention in the literature. This paper first studies e-readiness assessment models proposed for countries and then tries to develop a model for measuring the e-readiness of SMEs (ESME) by an exploratory study. Finally, the proposed model is used for the assessment and comparison for e-readiness of three considered Iranian SMEs.