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Showing 5 results for Triangular Fuzzy Number

Mahdi Yousefi Nejad Attari, Mohhamad Reza Bageri, Ensiyeh Neishabouri,
Volume 23, Issue 3 (9-2012)
Abstract

Decision making about outsourcing or insourcing of manufacturing activities is a type of multiple criteria decision making (MCDM) problem, which requires considering quantitative and qualitative factors as evaluation criteria simultaneously. Therefore, a suitable MCDM method can be useful in this area as it can consider the interactions among quantitative and qualitative criteria. The analytic network process (ANP) is a relatively new MCDM method which can deal with different kinds of interactions systematically. Moreover, the Decision Making Trial and Evaluation Laboratory (DEMATEL) method is able to convert the relations between cause and effect of criteria into a visual structural model as well as handling the inner dependences within a set of criteria. However both ANP method and DEMATEL techniques in their original forms are incapable of capturing the uncertainty during value judgment elicitation. To overcome this problem, here, a new and effective model is proposed based on combining fuzzy ANP and fuzzy DEMATEL for decision making about outsourcing or insourcing of manufacturing activities in uncertain conditions. Data from a case study is used to illustrate the usefulness and applicably of the proposed method.
Eng Fateme Zare Baghabad, Dr Hassan Khademi Zare,
Volume 26, Issue 3 (9-2015)
Abstract

In this paper an efficient three- stage algorithm is developed for software production cost and time estimation. First stage includes a hybrid model composed of COCOMO and Function Points methods to increase estimation accuracy. Second stage encompasses paired comparisons matrix of analytical hierarchy process to determine amount of any resources consumed in each step of software production by experts’ opinions. Third stage concludes cost and time tables of production scheduling by using Work break structure (WBS) and network models of project control. In whole of all stages of this paper, triangular fuzzy numbers are used to express uncertainty existed in succession and repetition of each production step, time of beginning, ending, the duration of each task and costs of them. Retrieved results examined by 30 practical projects conclude accuracy of 93 percent for time estimation and 92 percent for cost one. Also suggested algorithm is more accurate than COCOMOІІ 2000 algorithm as 50 percent based on examined problems.

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Rahebe Keshavarzi, Mohammad Hossein Abooie,
Volume 27, Issue 2 (6-2016)
Abstract

Process capability indices (PCIs) can be used as an effective tool for measuring product quality and process performance. In classic quality control there are some limitations which prevent a deep and flexible analysis because of the crisp definition of PCA‟s parameters. Fuzzy set theory can be used to add more flexibility to process capability analyses. In this study, the fuzzy X ba and MRx ba control charts are introduced to monitor continuous production process in triangular fuzzy state. Also, fuzzy PCIs are produced when SLs and measurements are triangular fuzzy numbers (TFN). For this aim, a computer program is coded in Matlab software. The fuzzy control charts is applied in Yazd fiber production plant. The results show that in continuous production processes, the better analysis will be performed by using fuzzy measurements. Also, based on the fuzzy capability indices, we can have a flexible analysis of the process performance.


Mohammad Khalilzadeh, Alborz Hajikhani, Seyed Jafar Sadjadi,
Volume 28, Issue 1 (3-2017)
Abstract

The present paper aims to propose a fuzzy multi-objective model to allocate order to supplier in uncertainty conditions and for multi-period, multi-source, and multi-product problems at two levels with wastages considerations.  The cost including the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects, partial coverage of suppliers in respect of distance and finally, suppliers weights which make the products orders more realistic are considered as the measures to evaluate the suppliers in the proposed model. Supplier's weights in the fifth objective function are obtained using fuzzy TOPSIS technique. Coverage and wastes parameters in this model are considered as random triangular fuzzy number. Multi-objective imperial competitive optimization (MOICA) algorithm has been used to solve the model,. To demonstrate applicability of MOICA, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms and results are analyzed using quantitative criteria and performing parametric. 


Ali Mohtashami, Alireza Alinezhad,
Volume 28, Issue 3 (9-2017)
Abstract

In this article, a multi objective model is presented to select and allocate the order to suppliers in uncertainty condition and in a multi source, multi customer and multiproduct case in a multi period state at two levels of supply chain. Objective functions considered in this study as the measures to evaluate suppliers are cost including purchase, transportation and ordering costs, timely delivering, shipment quality or wastages which are amongst major quality aspects, partial and general coverage of suppliers in respect of distance and finally suppliers weights making the products orders amount more realistic. The major limitations are price discount for products by suppliers which are calculated using signal function. In addition, suppliers weights in the fifth objective function is calculated using fuzzy Topsis technique. Lateness and wastes parameters in this model are considered as uncertain and random triangular fuzzy number. Finally the multi objective model is solved using two multi objective algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Particle Swarm Optimization (PSO) and the results are analyzed using quantitative criteria Taguchi technique was used to regulate the parameters of two algorithms. 



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