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  A fuzzy goal programming-based approach is used to solve a proposed multi-objective linear programming model and simultaneously handle two important problems in cellular manufacturing systems, viz. cell formation and layout design. Considerations of intra-cell layout, the intra-cell material handling can be calculated exactly. The advantages of the proposed model are considering machining cost, inter-cell, intra-cell (forward and backward) material handling, operation sequence and resource constraints on the capacity of machines. To illustrate applicability of the proposed model, an example is solved and computational results are noted.

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 In today’s highly competitive market, the pressure on organizations to find a better way to create and deliver value to customers is mounting. The decision involves many quantitative and qualitative factors that may be conflicting in nature. Here, we present a new model for transportation problem with consideration of quantitative and qualitative data. In the model, we quantify the qualitative data by using the weight assessment technique in the fuzzy analytic hierarchy process. Then, a preemptive fuzzy goal programming model is formulated to solve the proposed model. The software package LINGO is used for solving the fuzzy goal programming model. Finally, a numerical example is given to illustrate that the proposed model may lead to a more appropriate solution. 

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Quality function deployment is a well-known customer-oriented design procedure for translating the voice of customers into a final production. This is a way that higher customer satisfaction is achieved while the other goals of company may also be met. This method, at the first stage, attempts to determine the best fulfillment levels of design requirements which are emanated by customer needs. In real-world applications, product design processes are performed in an uncertain and imprecise environment, more than one objective should be considered to identify the target levels of design requirements, and the values of design requirements are often discrete. Regarding these issues, a fuzzy mixed-integer linear goal programming model with a flexible goal hierarchy is proposed to achieve the optimized compromise solution from a given number of design requirement alternatives .To determine relative importance of customer needs, as an important input data, we apply the well-known fuzzy AHP method. Inspired by a numerical problem, the efficiency of our proposed approach is demonstrated by several experiments. Notably, the approach can easily and efficiently be matched with QFD problems.

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 We are concerned with solving Fuzzy Flexible Linear Programming (FFLP) problems. Even though, this model is very practical and is useful for many applications, but there are only a few methods for its situation. In most approaches proposed in the literature, the solution process needs at least, two phases where each phase needs to solve a linear programming problem. Here, we propose a method to solve the given problem in just one phase using only one problem. Furthermore, using our approach, sensitivity analysis of Fuzzy Flexible Linear Programming (FFLP) problem is simpler. For an illustration of our method, some numerical examples given. In particular, a practical problem is formulated and is solved by our method and several other methods and the obtained results are compared.

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The purpose of this article is to investigate the modes of vehicles based on the type and number of urban travel facilities for passengers. As you know, to divide transportation models based on  goal programming, is to divide all transportation modes for urban station routes by type and region.The main objective of this is to present the best mode (vehicle) of transportation based on travel modeling in transportation areas of urban trips for multi-objective transportation goal programming. In this case, the type of transportation solution is determined in the desired area on the way to the stations, according to which the pollution reduction, travel time reduction, cost reduction, availability, maximum safety and comfort of the means of transportation are reduced, increased or liminated.