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Purpose: Today, due to the increase and complexity of customers' needs, the products available in the market do not satisfy the needs of the customers. Hence, the most effective way for an organization to achieve its objectives is to create distinction through both developing new products and bringing about variations in those products. This study, while pursuing the objective of evaluating product development design, aims to propose a multi-objective design. This model analyzes the developed design from two perspectives of general criteria and variation capacity norms. Design/methodology/approach: The current research embarks on identifying general criteria using Balanced Scorecard (BSC). The proposed model offers criteria according to which the required standard measures for the evaluation of the product development design are customized and extracted. After determining the evaluation criteria related to each perspective – general and variation capacity- the mentioned criteria have been validated. Then, by using the existing methods, these criteria have been quantified. Finally, the identified standard measures have been employed in the proposed multi-purpose optimized model. Findings: In this paper, a real case was studied in the Iran Electronics Industries, and the result of the configuration was illustrated. Quantitative values ​​of validated criteria were determined with the help of academic and industry experts and existing methods. After calculating the quantitative values ​​of the criteria for the evaluation of phased array radar development designs, these values were used in the multi-objective optimization model and measured in terms of the amount of risk, revenue, organizational strategy and the amount of effort towards redesign. Finally, by solving the optimization model using GAMZ win 64 25.1.12 software and the weighted sum method, the best development plan was selected. Research limitations/implications: The main limitation of this study is the lack of accurate information in the early stages of product development. In this research, it was assumed that the information provided by the experts and designers of the relevant industry is correct and real. It is suggested to use fuzzy data in future studies so that the result has more reliability. For future research, the following subjects can be attractive and the present study can provide the necessary background for researchers who seek to work on such subjects: i) In the current research, the time parameter was not considered. It is suggested to consider time in future research; ii) to identify non-common components between the two current and future generations, the method of calculating the priority of standardization of components and considering the technical and financial ability of the producer was used. It is suggested to decide according to possible technologies in the future; iii) another perspective can be used to categorize and identify criteria; iv) to calculate reliability and maintainability, the lifetime of systems was assumed to be exponential and with a constant failure rate. It is suggested to use Weibull distribution with variable failure rate for a more realistic calculation, and v) it is suggested to compare the results of this study with other studies. Practical implications: Finding comprehensive criteria to evaluate product development designs and providing an effective mathematical model for evaluation can lead to selecting the best product development designs. Selecting an optimal design can prevent the increase in product modification costs and the effort to redesign and reduce the time to supply new products to the market. Originality/value: Based on the literature review, particularly the internal research, a method that evaluates product development designs with comprehensive criteria including finance, customer, business and internal work, growth and learning, and variation capacity has not been found. Also, the Design for Variety approach has not been used in the initial phases of research and it is applied when the product is made and released to the market.

Purpose: The fundamental function of a cellular manufacturing system (CMS) is based on the definition and recognition of the type of similarity among the parts that should be produced in a planning period. Cell formation (CF) and cell/machine layouts are two major steps in implementing the CMS design. This paper aims to propose a new mathematical nonlinear programming model for cell formation that employs the rectilinear distance concept to determine layout in a continuous space. In the proposed model, the benefits of cellular layout consideration are used, and the objective function computes the cost of cell reconfiguration and the costs of intra-cell and inter-cell material handling movements. Due to its problem complexity, a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm are proposed to solve the problem. To address the efficiency of the linearized model and solution methods, the production information of a real case study is used and 30 test problems in different dimensions are presented. Design/methodology/approach: In this paper, a mathematical programming model of cell formation and cell layout has been proposed in a continuous space, using the concept of rectilinear distance. In the proposed model, production information similar to the production flow between machines, alternative process routing, cells capacity, and the inter-cell and intra-cell transportation costs has been considered. Due to the nature and complexity of the proposed model, two metaheuristic algorithms, i.e., GA and PSO have been also developed for larger problems. Findings: In this paper a real case study in the BATA company was studied and the result of configuration was illustrated. By computing the efficiency of the linearized model and solution methods, the production information of a real case study was used and 30 test problems in different dimensions were presented. Findings highlighted the high efficiency of the genetic algorithm in solving large-scale problems. Research limitations/implications: Issues such as assuming similar dimensions for machines or their constant availability are considered as the limitations of this study. For future research, the following subjects can be attractive and the present study can provide the necessary background for researchers who seek to work on such subjects:Considering unequal dimensions for machines; in the proposed model, machines were considered as squares of equal area with unit dimension. To obtain a more appropriate schema from the space of a job shop, machines’ dimensions can be assumed as the input parameters.Developing probabilistic models and fuzzy model factors (e.g., available machines, operation time, costs, transportation time, and demand for each part) can be considered fuzzy or probabilistic.To make the model more realistic, production data such as setup times and holding inventory between periods can be incorporated. Also, the proposed model can be integrated with the scheduling problem. Practical implications: CMS, which is the most important application of GT, overcomes the inefficiency of traditional approaches by reducing transportation time and distance. A flow shop layout has high efficiency in a mass production system, while a job shop is a very flexible system for producing various parts. Each of these systems does not have any other benefits. The CMS is an approach between the two manufacturing systems and aims to improve flexibility and efficiency to produce manufacturing groups in different sizes. In a CMS, machines and parts assignment to cells must impose a minimum cost on the system. Due to the practical nature of the proposed mathematical model, a real case was studied in which, the production information of the BATA company was used. Originality/value: In this study, a new non-linear mixed-integer programming model was proposed which considered the simultaneous cell formation and intra-cell and inter-cell layouts in a continuous space. The model aimed to determine the cell formation and the intra and inter-cell layout concurrently in a way that the total transportation cost of parts and the reconfiguration cost of cells were minimized. The proposed model attempted at calculating the material handling costs realistically. The material handling cost was computed based on the actual location of machines and cells on the shop floor regarding the dimensions of equal-sized machines. Handling both intra/intercellular materials using batch sizes for transferring parts was taken into account in calculating the transportation cost. The transportation cost was calculated based on the rectilinear travel distance, according to the center-to-center interval among machines.

One of the most important issues concerning the designing a supply chain is selecting the supplier. Selecting proper suppliers is one of the most crucial activities of an organization towards the gradual improvement and a promotion in performance. This intricacy is because suppliers fulfil a part of customer’s expectancy and selecting among them is multi-criteria decision, which needs a systematic and organized approach without which this decision may lead to failure. The purpose of this research is proposing a new method for assessment and rating the suppliers. We have identified several evaluation criteria and attributes; the selection among them was by the Simple Multi-Attribute Rating Technique (SMART) method, then we have specified the connection and the influence of the criteria on each other by DEMATEL method. After that, suppliers were graded by using the Fuzzy Analytical Network Process (FANP) approach and the most efficient one was selected. The innovation of this research is combining the SMART method, DEMATEL method, and Analytical Network Process in Fuzzy state which lead to more exact and efficient results which is proposed for the first time by the researchers of this study.

Control of wellbore pressure during drilling operations has always been important in the oil industry as this can prevent the possibility of well blowout. The present research employs a combination of automatic process control and statistical process control for the first time for the identification, monitoring, and control of both random and special causes in drilling operations. To this end, by using automatic process control, control charts are applied to the output of the controlled process; subsequently, the points which are outside the predefined control limits are identified. This method is capable of using controllable input variables not used in automatic process control, such as changes in the mud weight, to fully control the process. Due to the dynamic nature of the process, adaptive model-based controllers have replaced feedback methods in automatic process control. Control charts have also been used to compare the performance of different automatic process control approaches. Based on this new criterion, the fuzzy adaptive approach is shown to have good performance in automatic process control. The results indicate that this approach can improve the limits of the automatic process control method by using statistical process control for controlling the bit pressure in an acceptable range.

This work used two methods for Monitoring and control of autocorrelated processes based on time series modeling. The first method was the simultaneous monitoring of common and assignable causes. This method included applying five steps of data gathering, normality test, autocorrelation test, model selection and control chart selection on all non-stationary process observations. The second method was a novel one for the separate monitoring and control of common and assignable causes. In this method, the process was divided into the parts with and without assignable causes. The first method was greatly non-stationary due to not separating common and assignable causes. This method also implied that the common causes were hidden in the process. The novel method for the separate monitoring of common and assignable causes could turn the process into a stationary one, leading to identifying, monitoring, and controlling common causes without any interference from the assignable causes. The results showed that, unlike the first method, the second method could be very sensitive to the common causes; it could, therefore, suitably monitor, identify and control both assignable and common causes. The current work was aimed to use control charts to monitor and control the bootomhole pressure during the drilling operation.

Abstract The nature of electro-optical equipment in various industries and the pursuit of the goal of reducing costs demand high reliability on the part of electro-optical systems. In this respect, reliability improvement could be addressed through a reliability allocation problem. Subsystem reliability must be increased such that the requirements as well as defined requisite functions are ensured in accordance with the designers’ opinion. This study is an attempt to develop a multi-objective model by maximizing system reliability and minimizing costs in order to investigate design phase costs as well as production phase costs. To investigate reliability improvement feasibility in the design phase, effective feasibility factors in the system are used and the sigma level index is incorporated in the production phase as the reliability improvement difficulty factor. Thus, subsystem reliability improvement priorities are taken into consideration. Subsystem dependency degree is investigated through the design structure matrix and incorporated into the model’s limitation together with modified criticality. The primary model is converted into a single-objective model through goal programming. This model is implemented on electro-optical systems, and the results are analyzed. In this method, reliability allocation follows two steps. First, based on the allocation weights, a range is determined for the reliability of subsystems. Afterward, improvement is initiated based upon the costs and priorities of subsystem reliability improvement.

Reliability allocation has an essential connection to design for reliability and is an important activity in the product design and development process. In determining the reliability of subsystems or components on the basis of goal reliability, attention must be paid to failure effect, failure information, and improvement opportunities based upon real potentials for reliability improvement. In the light of the fact that ignoring dependent failures inflicts irreversible damage on systems, and that redundant systems are vulnerable to Common Cause Failure (CCF) as well as independent failure, attention must be paid not only to components’ independent failure information, but also to CCF information in conducting reliability allocation for such systems. To consider improved failure rate alone cannot ensure the achievement of the goal reliability in question, because if the CCF occurrence exceeds a certain limit, the system’s reliability will certainly fail to match the goal reliability. This paper is an attempt to develop a method for reliability allocation of series-parallel systems by considering CCF, in such a way that potentials and priorities of reliability improvement are taken into consideration. The proposed method consists of four stages: 1) adding a series component to the redundant system in order to investigate CCF, 2) conducting reliability allocation for series components and the redundant system, 3) conducting reliability allocation for redundant system components, and 4) analyzing the failure rate of system components. The proposed method is run for water pumping systems and the results are evaluated. In this method, in addition to the improved failure rate of system components, the improved rate of CCF is computed, too. This proves instrumental and crucial for system designers in feasibility studies and conceptual design.

Abstract The reliability and safety of any system is the most important qualitative characteristic of a system. This qualitative characteristic is of particular importance in systems whose functions are under various stresses, such as high temperature, high speed, high pressure, etc. A considerable point, which is rarely taken into account when calculating the reliability and safety of systems, is the presence of dependency among subsystems, and this dependency causes various failures in a system, one of the most important of which is the common cause failure (CCF). Failing to consider common cause failures in the calculation of system reliabilities, leads to optimistic estimations of system reliability rates, which results in too much trust in the system. In this paper, first we deal with identifying the reliability of the input of a dynamic positioning system consisting of different environmental sensors and various positioning systems with the aid of PBS and FFBD techniques. Then, we will calculate and allocate the above-mentioned reliability with the aid of a RBD. The common cause failures of different subsystems were considered in calculating the reliability of the previously mentioned system, with the aid of IEC 61508 standard, and then the degree of the effectiveness of common cause failures on the reliability of the studied system, was obtained. Finally, by considering different assumptions for the system under study, it was proved that the less the amount of the reliability of dependent components is, the higher the effectiveness of common cause failures on the system reliability will be

In this research we have presented a local model for implementing systems engineering activities in optimized acquisition of electronic systems in Electronic High-Tech Industrial. In this regard, after reviewing the literature and the use of documents, articles and Latin books, we have collected system acquisition life cycle models from different resources. after considering the criteria of the mentioned industry, we have designed two questionnaires and distributed them among some experts of industry and university. The Population studied here were all of the specialists of mentioned industry and our sample consist of 17 experts in this field. As the main focus of this research is on experts’ opinions, we used purposive sampling. Finally, we present local model using the results of the questionnaires and considering the standards, requirements and system engineering processes during system acquisition stages and then match them with our case study. this research includes some innovations such as: flexibility of model under different conditions, choosing the appropriate positions for applying systems engineering reviews, and also the use of foresight issue in the initial phase of model, which have been neglected in other presented models in previous researches.

This research aims at presenting a consolidated model of data envelopment analysis (DEA) technique and value engineering to select the best manufacturing methods for gate valve covers and ranking the methods using TOPSIS.To do so, efficiency evaluation indices were selected based on the value engineering approach and different manufacturing methods were evaluated using DEA technique.Finally, effective methods were ranked based on TOPSIS. Accordingly, 48 different methods were identified for manufacturing the part. The DEA results showed that only 12 methods have complete efficiency. Meanwhile manufacturing method No. 32 (A216 WCB casting purchased from Chinese market as the raw material, machining by CNC+NC and drilling by radial drill) was ranked the first.Major limitations of the research include time limitations, place limitation, lack of access to the standards adaptability index in different machining and drilling methods, limitation on evaluating all parts of a product, limitation on a technique evaluating efficiency and ranking, and mere satisfying with superior indices in each factor of value engineering. Most previous studies only evaluated efficiency of manufacturing methods based on a single approach.By applying value engineering, which is in fact a combination of three approaches (including quality approach, functional, and cost approaches), the present research provided a far more comprehensive model to evaluate manufacturing methods in industrial.

Increasingly expansion of competition among organizations in current century has caused them to look for the ways for obtaining competitive advantage and to win in the competition arena. Organizational learning is considered as one of the tools of acquiring competitive advantage for managers of the current organizations. This article tries to investigate organizational learning in an industry company. The research data are collected by using organizational questionnaire based on the “marquad” model. The under study community of this research includes all experts of company, sample volume is determined as 284 persons according to sampling procedure of “Morgan” table, in this research after collecting required data the gap between perceptions and expectations of experts is determined by organizational learning constituents. Then critical indicators of learning in organization are determined based on drawing of significance-performance matrix. Results reveal that the most important critical indicators of organizational learning in company are: the methods of knowledge sharing, team working, motivational mechanisms and reward, enactment of facilitating by managers, teaching skills of system thinking, creativity, innovations and experiencing in personal.

In this paper, four different multiple-type dispersion facilities location modeling formulations have been developed, which utilize efficiency measures of Data Envelopment Analysis to find optimal and efficient facility location patterns. While the problems studied previously do not clearly indicate which candidate is elected by a unit, simultaneous DEA approach has been used for analysis. In these models, existing facilities are considered. These models with two objectives, considerabely match the real world problems. Since these four models are multiobjective, a fuzzy goal programming approach has been applied to solve the four problems. In this paper, after introducing the proposed models, an example, with the results of numerical calculations has been presented.

Technology and its various development has evolved rapidly the industries specifically in the field of aviation products during the last decade . Different aviation companies and manufacturing industries put up their efforts to utilize different implements , modern technologies and new organizational methologies to optimize their working process . They intend to fulfill the customers requirements in relation to their products . Naturally it will be achieved through the factors such as the cut in cost and time , enhancement of performance and applying the required and modern technologies. The main emphasis has been switched toward the customers point of view ( such as delivery of the aircraft products per the appointed dates , quality and reliability and functional specifications ) . Providing the proper and quick supply of chain for provision process is as significance as production procedures and processes by incorporating the required Informatin Technology for the right product to deliver as per specified and promised date to the customer . This thesis attamps to recognize the basic provision criteria Informatin Technology alternatives along with their effective impacts in a company . In addition , it also determines the priorities of Informatin Technology influence on supply chain management which in turn optimize the research goals of the enterprise . For this purpose , the resrarch has been prepared with use of questionnaire and paired analogy in an aviation company . In this regard , experts and managers of commercial and supply division , planning and production control , computer (115 persons ) were questioned during the second midyear of 1390 and first midyear of 1391 . Finally it was revealed that all the known major measures of chain provision and information technology impact on each other and validated the conceptual model . Therefore trained human resourses on IT , support of senior executives , hardware and software substructures , commerce electronic and exchange of data with partners should be tacken into account as a model of optimization for application of IT in supply chain management .

One of the main principles of the passive defense is the principle of site selection. In this paper, we propose a multiple objective nonlinear programming model that considers the principle of the site selection in terms of two qualitative and quantitative aspects. The purpose of the proposed model is selection of the place of key production facilities of a system in which not only it observes the dispersion principle but also reduces the system transportation costs. Moreover, the proposed model tries to select the sites that can fulfill other elements of site selection as well as dispersion in a way that it increases the trustworthiness of the selected network. For solving the proposed model we used the Genetic Algorithm integrated with TOPSIS method.

In today’s organizations, performance measurement comes more to the foreground with the advancement in the high technology. Supplier selection is an important issue in supply chain management. In recent years, determining the best supplier in the supply chain has become a key strategic consideration. However, these decisions usually involve several objectives or criteria, and it is often necessary to compromise among possibly conflicting factors. Thus, the multiple criteria decision making (MCDM) becomes a useful approach to solve this kind of problem. In order to use the conceptual framework for measuring performance supplier, a methodology that takes into account both quantitative and qualitative factors and the interrelations between them should be utilized. for leveling an integrated approach of analytic hierarchy process AHP and fuzzy TOPSIS method is proposed to obtain final ranking. The interactions among the criteria are also analyzed before arriving at a decision for the selection of supplier from among six alternatives. Linguistic values are used to assess the ratings and weights for criterion. These linguistic ratings can be expressed in triangular fuzzy numbers. Then, a hierarchy multiple criteria decision-making (MCDM) model based on fuzzy-sets theory including FAHP and FTOPSIS are applied. There are two approaches for aggregating values including relative importance of evaluation criteria with respect to the overall objective and rating of alternatives with respect to each criterion in fuzzy group TOPSIS: First aggregation and Last aggregation. In first aggregation approach weight of each criterion and rating of alternatives with respect to each criterion gained from decision makers are aggregated at first and TOPSIS method then apply to these aggregate values. In last aggregation approach weight of each criterion and rating of alternatives with respect to each criterion gained from decision makers are used in TOPSIS method directly. In this paper use the first method and gained weight of each criterion from AHP. This research has been conducted on manufacturing industry. However the results can also be used for other types of organizations managers that are located in a competitive environment.

One of the main principles of the passive defense is the principle of site selection. In this paper, we propose a multiple objective nonlinear programming model that considers the principle of the site selection in terms of two qualitative and quantitative aspects. The purpose of the proposed model is selection of the place of facilities of a system in which not only it observes the dispersion principle but also reduces the system transportation costs. Moreover, the proposed model tries to select the sites that can fulfill other elements of site selection as well as dispersion in a way that it increases the trustworthiness of the selected network .

The increasing emphasis on supply chain management (SCM) has led researchers to rethink models, constructs, and frameworks for quality management (QM) that have been developed for the field of operations management. In this paper, within the framework of a structural model we investigate how these two supply chain management-related quality practices (customer focus and supplier quality management) lead to improved quality and business performance of a supply chain and examine some of the most important success factors of QM that precede and mediate those relationships. In this study, we use survey data gathered from 100 industrial firms operating in the Iran. Our results provide further support top management commitment’s role in building supply chain linkages that will impact quality performance. In this study, we also find a statistically significant direct relationship between top management commitment and role of quality department that finally impact supply chain performance.

The reliability of designing systems such as electrical and electronic circuits, power generation/ distribution networks and mechanical systems, in which the failure of a component may cause the whole system failure, and even the reliability of cellular manufacturing systems that their machines are connected to as series are critically important. So far approaches for improving the reliability of these systems have been mainly based on the enhancement of inherent reliability of any system component or increasing system reliability based on maintenance strategies. Also in some of the resources, only the influence of the location of systems' components on reliability is studied. Therefore, it seems other approaches have been rarely applied. In this paper, a multi criteria model has been proposed to perform a balance among a system's reliability, location costs, and its system maintenance. Finally, a numerical example has been presented and solved by the Lingo software.

This expository article shows how the maximum likelihood estimation method and the Newton-Raphson algorithm can be used to estimate the parameters of the power-law Poisson process model used to analyze data from repairable systems.

This expository article shows how the maximum likelihood estimation method and the Newton-Raphson algorithm can be used to estimate the parameters of the power-law Poisson process model used to analyze data from repairable systems .