in the fundamental production inventory model, in order to solve the economic production quantity (epq) we always fix both the demand quantity and the production quantity per day. but, in the real situation, both of them probably will have little disturbances every day. therefore, we should fuzzify both of them to solve the economic production quantity (q*) per cycle. using α-cut for defuzzific...

this paper considers the fuzzy inventory model for deteriorating items for power demand under fully backlogged conditions. we define various factors which are affecting the inventory cost by using the shortage costs. an intention of this paper is to study the inventory modelling through fuzzy environment. inventory parameters, such as holding cost, shortage cost, purchasing cost and deteriorati...

this study considers an eoq inventory model with advance payment policy in a fuzzy situation by employing two types of fuzzy numbers that are trapezoidal and triangular. two fuzzy models are developed here. in the first model the cost parameters are fuzzified, but the demand rate is treated as crisp constant. in the second model, the demand rate is fuzzified but the cost parameters are treated ...

this paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. this architecture of articial neural networks, can get a real input vector and calculates its corresponding fu...

uncertainty plays a major role in any project evaluation and management process. one of the trickiest parts of any production project work is its cost and time forecasting. since in the initial phases of production projects uncertainty is at its highest level, a reliable method of project scheduling and cash flow generation is vital to help the managers reach successful implementation of the pr...

artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. this paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. for this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. the sugg...

this study presents the effects of project uncertainties on nonlinear time-cost tradeoff (tct) profile of real life engineering projects by the fusion of fuzzy logic and artificial neural network (ann) models with hybrid meta-heuristic (hmh) technique, abridged as fuzzy-ann-hmh. nonlinear time-cost relationship of project activities is dealt with ann models. ann models are then integrated with ...

fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. in this research, fcm is chosen for fuzzy clustering. parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. these two parameters require tuning to reduce the overfitting in the...

In this paper, we consider fuzzy inventory with backorder. First, we fuzzify the storing cost a, backorder cost b, cost of placing an order c, total demand r, order quantity q, and shortage quantity s as the triangular fuzzy numbers in the total cost. From these, we can obtain the fuzzy total cost. Using the signed distance method to defuzzify, we get the estimate of the total cost in the fuzzy...