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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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This paper presents a structure that empower designing supervisory groups to survey the estimation of real options in projects of enormous scale, incompletely standardized frameworks actualized a couple of times over the medium term. Specific options writing is done using a methodology of planning the design and making prior decisions regarding the arrangements of specific options, with a recreation-based value measure designed to be near-current construction rehearsals and to resolve financial problems in particular cases. To study the case and demonstrate the actual application of this method, drug chain modeling at the tactical level was investigated. The physical and financial flow and their disturbance are simultaneously modulated. In order to complete the financial flow, financial ratios are also entered into the model. Problem uncertainty has been modeled using one of the most recent robust optimization approaches called Robust Possibilistic Programming (RPP) in combination with real options theory. The model was applied to a case study and its results were analyzed and validated by GAMS software. The results show that without violating the limitations of the problem, it shows appropriate decisions to deal with the problem.

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Here, a new mathematical model for cellular manufacturing systems considering three important features of part priority, levels of machine’s technology, and the operator’s skill is developed. Simultaneous consideration of these features provides a more realistic analysis of the problems in cellular manufacturing systems. A model with multiple design features including cell formation, human resources flexibility with different skills, machines flexibility, operational sequence, processing time, and the capacity of machine and manpower is proposed in this article. Ourfocus is on the design of cells to implementtwo dissimilar goals. The first goal is the reduction of inter-cellular movements of parts and workers. The second goal is the creation of efficient cellsby making cell’s quality level identical for produced products so that the production of all the different parts have good quality. Two approaches of augmented ε-constraint and non-dominated sorting genetic algorithm II (NSGA-II) are used to solve this model. By comparison of these two approaches, we realizethat the multi-objective evolutionary optimization algorithm creates a Pareto-optimal front in a reasonable amount of time forlarge-scale problems

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New concepts of -feasibility and -efficiency of solutions for fuzzy mathematical programming problems are used, where  is a vector of distinct satisfaction degrees. Recently, a special kind of fuzzy mathematical programming entitled Fuzzy Flexible Linear programming (FFLP) is attracted many interests. Using the mentioned concepts, we propose a two-phase approach to solve FFLP. In the first phase, the original FFLP problem converts it to a Multi-Parametric Linear Programing (MPLP) problem, and then in phase II using the convenient optimal solution with the higher feasibility degree is concluded. Using this concept, we have solved the problem of the animal diet. In the process of milk production, the highest cost relates to animal feed. Based on reports provided by the experts, around seventy percent of dairy livestock costs included feed costs. In order to minimize the total price of livestock feed, according to the limits of feed sources in each region or season, and also the transportation and maintenance costs and ultimately milk price reduction, optimization of the livestock nutrition program is an essential issue. Because of the uncertainty and lack of precision in the optimal food ration done with existing methods based on linear programming, there is a need to use appropriate methods to meet this purpose. Therefore, in this study formulation of completely mixed nutrient diets of dairy cows is done by using a fuzzy linear programming in early lactation. Application of fuzzy optimization method and floating price make it possible to formulate and change the completely mixed diets with adequate safety margins. Therefore, applications of fuzzy methods in feed rations of dairy cattle are recommended to optimize the diets. Obviously, it would be useful to design suitable software, which provides the possibility of using floating prices to set feed rations by the use of fuzzy optimization method.