Jin-Shieh Su

Fuzzy inventory without backorder for fuzzy order quantity and fuzzy total demand quantity

Abstract In this paper, we consider the inventory problem without backorder such that both order and the total demand quantities are triangular fuzzy numbers Q =(q 1 , q 0 , q 2 ) , and R =(r 1 , r 0 , r 2 ) , respectively, where q 1 =q 0 − Δ 1 , q 2 =q 0 + Δ 2 , r 1 =r 0 − Δ 3 , r 2 =r 0 + Δ 4 such that 0 Δ 1 0 , 0 Δ 2 , 0 Δ 3 0 , 0 Δ 4 , and r0 is a known positive number. Under conditions 0⩽q1 μ G( Q , R ) (z) of the total fuzzy cost function G( Q , R ) and their centroid, then obtain order quantity q ∗∗ in the fuzzy sense and the estimate of the total demand quantity. Scope and purpose This paper deals with the inventory problem without backorder with total cost function F(q)=cTq/2+ar/q, q>0 . In the classical inventory (without backorder) model, both the total demand over the planning time period [0, T] and the period from ordering to arriving are fixed. In the real situation, the total demand r and order quantity q probably will be different from the values used in the total cost function. Also, r influences the values of T. In view of this circumstances, we consider the inventory problem in which both order and total demand quantities are triangular fuzzy numbers Q =(q 1 , q 0 , q 2 ) , and R =(r 1 , r 0 , r 2 ) , respectively, where q 1 =q 0 − Δ 1 , q 2 =q 0 + Δ 2 , r 1 =r 0 − Δ 3 , r 2 =r 0 + Δ 4 such that 0 G( Q , R )=cT Q /2+a R / Q , we use the extension principle to find the membership function μ G( Q , R ) of the fuzzy total cost function G( Q , R ) and their centroid (see Proposition 3). Therefore, given the value of q 1 , q 0 , q 2 , r 1 and r2, we can find an estimate of the total cost in the fuzzy sense. Finally, we make a comparison between the crisp sense and fuzzy sense by some numerical result.

Fuzzy inventory with backorder for fuzzy total demand based on interval-valued fuzzy set

Abstract It is difficult to determine the fixed total demand r 0 in an inventory problem with backorder in a whole plan period. We will fuzzify it as R=⌈ near r 0 ⌋ . In this article, we will classify R into three kinds: (1) fuzzy total demand with triangular fuzzy number ( Section 2 ), (2) fuzzy total demand with interval-valued fuzzy set based on two triangular fuzzy numbers ( Section 3 ), (3) fuzzy total demand with interval-valued fuzzy set based on two trapezoidal fuzzy numbers ( Section 4 ). We will find the corresponding order quantities and the shortage inventories, respectively.

Fuzzy system reliability analysis based on level (λ,1) interval-valued fuzzy numbers

This study uses Level (λ,1) interval-valued fuzzy numbers to examine the fuzzy reliability of a serial system and a parallel system and obtain the estimated reliability of both systems in the fuzzy sense.

Fuzzy decision making for IJV performance based on statistical confidence-interval estimates

This paper considers the International Joint Ventures (IJVs) problem using interval-valued fuzzy sets and the compositional rule of inference in the statistical sense. We consider the performance effects of evaluation criteria facts and weights based on fuzzy set theory to determine the performance ranking among IJVs. Due to the lack of precise information based on some fuzzy language is used in the evaluation criterion. We use the statistical confidence-interval estimates and apply the signed distance method to defuzzify.

Fuzzy Multiple Objective Programming Based on Interval-Valued Fuzzy Sets

In this paper, we use the interval-valued fuzzy sets to derive linear programming in the fuzzy sense from crisp multiple objective programming. For many practical problems, if we just used the general fuzzy sets, it may not be quite well to describe the real situation. That is the reason why we use the interval-valued fuzzy sets and the method in Zimmerman to fuzzify the crisp linear programming.

A self-tuning emergency model of home network environment

In this paper, we proposed a self-tuning emergency model of home network environment (SEMHNE). This model can not only tune the scaling factors and membership functions to fit the home network environment but also detect the emergency events automatically. There are three modules in this model, namely, emergency report module (ERM), renewable emergency rule base (RERB), and evolutionary database (EDB). ERM determines the emergency situations by fuzzy inferences and sends the warning message to the users. RERB can provide rules to ERM for inference. EDB can do self-tuning by using genetic algorithm and provide information to ERM for inference. Via this model, our home network environment will become more reliable and safety.

Fuzzy decision making for diagnosing machine fault

The purpose of this study is to present a fuzzy diagnosing machine fault to support the developing machine diagnosis system. The fuzzy evaluation is used to process the problems of which the fault causes and the symptoms are dealing with the uncertainty environment. In this study, we propose two propositions to treat the machine diagnosis fault.

Maximum Revenue for Fuzzy Price Based on (λ,1) Interval-Valued Fuzzy Numbers

In this paper, we use level (λ,1) interval-valued fuzzy numbers to consider the fuzzy price and the fuzzy revenue in economic problem. Using signed distance to defuzzify, we can get the demand function and revenue function in fuzzy sense. What follows is that we can find the maximum revenue in fuzzy sense.

Fuzzy Performance Analysis Model Based on Grid Environment

In grid computing environment, job requirements are so large scale and complex that we need the allocating mechanism to manage the resources and schedule the job. So that, a well-allocated mechanism is needed to enhance the grid resources be more useful and scalable. In this paper, we propose a resource performance analysis model for grid resources under the grid computing environment. By this model, we can analyze the information about CPU usage, memory usage by fuzzy inferences, and number of running jobs of each grid resource node to achieve load-balancing and make the plans and allocations of the resources of collaborated nodes optimize. There are three modules in the proposed model, namely, resource detecting module, resource estimator module, and resource assignment module. According to the result of experiment, the mechanism can achieve the best resources allocation, and enhance the overall grid computing performance.

Fuzzy system reliability analysis based on level(1−β,1−α) interval-valued fuzzy numbers and using statistical data

In this paper, we consider the fuzzy reliability of the serial system and the fuzzy reliability of the parallel system problems. We use the statistical data to derive a level 1-alpha fuzzy numbers and a level (1-beta, 1-alpha) interval-valued fuzzy numbers. Then, we compute both fuzzy reliability of the serial system and fuzzy reliability of the parallel system based on level (1-beta, 1-alpha) interval-valued fuzzy numbers.