Design of assembly line is done in order to more coordinate a collection of some consecutive work stations for the aim of obtaining more productivity from the work stations and workers. The stations are arranged in a way to have a continuous and constant material flow. In this paper a multi-objective formulation for assembly line balancing is introduced. As a solution approach a two-step approach is proposed. In the first step the problem in a fuzzy environment is converted to a crisp problem and in the second step an efficient solution of the crisp problem is found by fuzzy programming approach. The efficiency of the proposed approach is shown by a numerical example.

In this paper, we show a procedure for solving multilevel fractional programming problems in a large hierarchical decentralized organization using fuzzy goal programming approach. In the proposed method, the tolerance membership functions for the fuzzily described numerator and denominator part of the objective functions of all levels as well as the control vectors of the higher level decision makers are respectively defined by determining individual optimal solutions of each of the level decision makers. A possible relaxation of the higher level decision is considered for avoiding decision deadlock due to the conflicting nature of objective functions. Then, fuzzy goal programming approach is used for achieving the highest degree of each of the membership goal by minimizing negative deviational variables. We also provide sensitivity analysis with variation of tolerance values on decision vectors to show how the solution is sensitive to the change of tolerance values with the help of a numerical example.

Transportation costs today are considered as one of the most important costs affecting the cost of goods and the final price of consumption for the customer, especially in supply chain management. Therefore, it is necessary to focus on transportation costs due to lower prices, and consequently increase customer satisfaction and increase the position of the industry among competitors. Given that the available resources (capacity of supply centers) are generally considered as the minimum capacity available in the model, generally to solve such models, first the fuzzy supply limit is definitively converted and then using methods. The existing solution is a crisp problem that unfortunately does not fit well with the nature of the uncertainty in the decision. Therefore, in this paper, in order to adapt to the real conditions, the supply constraint in the flexible fuzzy transportation model is studied. For this purpose, a fuzzy mathematical programming approach to the transportation problem with flexible fuzzy constraints is proposed to obtain the most satisfactory solution. Finally, a numerical example for the proposed model and the proposed solution method is considered.

Transportation costs today are considered as one of the most important costs affecting the cost of goods and the final price of consumption for the customer, especially in supply chain management. Therefore, it is necessary to focus on transportation costs due to lower prices, and consequently increase customer satisfaction and increase the position of the industry among competitors. Given that the available resources (capacity of supply centers) are generally considered as the minimum capacity available in the model, generally to solve such models, first the fuzzy supply limit is definitively converted and then using methods. The existing solution is a crisp problem that unfortunately does not fit well with the nature of the uncertainty in the decision. Therefore, in this paper, in order to adapt to the real conditions, the supply constraint in the flexible fuzzy transportation model is studied. For this purpose, a fuzzy mathematical programming approach to the transportation problem with flexible fuzzy constraints is proposed to obtain the most satisfactory solution. Finally, a numerical example for the proposed model and the proposed solution method is considered.

In this paper, we introduce a new mathematical programming approach to estimate the parameters of fuzzy linear regression with crisp/fuzzy input and fuzzy output. The advantage of this approach is, both of dependent and independent variables are influenced on the objective function. Therefore, this model rectifies some problems about outliers. Also, in this paper we use possibility function in constraints. To compare the performance of the proposed approach with previous methods, three examples are presented. 

Ordering on fuzzy quantities have been attracted a wide domains of studies in fuzzy sets theory in the two last decades. In many practical situations as well as fuzzy mathematical programming, it is necessary to the decision makers consider L-R fuzzy numbers according to their aims. But in the most of methods which are presented to order fuzzy numbers, the authors have been considered a special kind of fuzzy numbers such as triangular fuzzy numbers, trapezoidal fuzzy numbers and etc. But as we know the L-R fuzzy numbers as a general kind of these numbers have not been discussed. Hence in this paper, we focus on a general L-R fuzzy number and propose a new approach to order them as an extension of the method which is given by Nasseri in [14]. For validity of the proposed method, we will illustrate this method based on a convenient examples which is appeared in the literature of fuzzy ordering. Furthermore, we emphasize that the proposed method will be useful for evaluating the optimality conditions in the fuzzy primal simplex algorithms and the other related algorithms such as the fuzzy dual simplex algorithm and the fuzzy two phase simplex algorithm, fuzzy transportation models, fuzzy interval linear programming and etc.