Huey-Ming Lee

Economic production quantity for fuzzy demand quantity, and fuzzy production quantity

Abstract In the fundamental production inventory model, in order to solve the economic production quantity (EPQ) per cycle 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 ecoomic production quantity ( q ∗ ) per cycle. The purpose of this paper is to investigate a computing schema for the EPQ in the fuzzy sense. We find that, after defuzzification, the total cost is although slightly higher than in the crisp model; however, it permits better use of the EPQ in the crisp arising with little disturbances in the production, and demand.

Applying fuzzy set theory to evaluate the rate of aggregative risk in software development

Abstract The purpose of this study is not only to build a structure model of risk in software development but also evaluate the rate of aggregative risk by fuzzy set theory. While evaluating the rate of aggregative risk, we may adjust the weights or grades of the factors until we can accept it. We also show that the rate of aggregative risk is reasonable.

Economic reorder point for fuzzy backorder quantity

Abstract In this paper we consider the backorder inventory problem with fuzzy backorder such that the backorder quantity is a triangular fuzzy number S = (s 1 , s 0 , s 2 ) . Suppose s ∗ and q ∗ denote the crisp economic backorder and order quantities respectively in the classical inventory with backorder model. According to four order relations of s ∗ and s 1 , s 0 , s 2 ( s 1 s 0 s 2 ) we find the membership function μ G q ( S ) (Z) of the fuzzy cost function G q ( S ) and their centroid. We also obtain the economic order quantity q ∗∗ and the economic backorder quantity s ∗∗ in the fuzzy sense. We conclude that, after solving the model in the fuzzy sense, the total cost is slightly higher than that in the crisp model; however, it permits better use of the economic fuzzy quantities arising with changes in orders, deliveries, and sales.

Group decision making using fuzzy sets theory for evaluating the rate of aggregative risk in software development

Abstract The purpose of this study is not only to build a group decision making structure model of risk in software development but also to propose two algorithms to tackle the rate of aggregative risk in a fuzzy environment by fuzzy sets theory during any phase of the life cycle. While evaluating the rate of aggregative risk, one may adjust or improve the weights or grades of the factors until she/he can accept it. Moreover, our result will be more objective and unbiased since it is generated by a group of evaluators.

Economic order quantity in fuzzy sense for inventory without backorder model

Abstract This paper investigates a group of computing schemas for economic order quantity as fuzzy values of the inventory without backorder. We express the fuzzy order quantity as the normal triangular fuzzy number ( q 1 , q 0 , q 2 ), and then solve the aforementioned optimization problem. We find that, after defuzzification, the total cost is slightly higher than in the crisp model; however, it permits better use of the economic fuzzy quantities arising with changes in orders, deliveries, and sales.

Fuzzy inventory with backorder for fuzzy order quantity

This paper investigates a group of computing schemas for economic order quantity as fuzzy values, and the corresponding optimal stock quantity of the invenyory with backorder. We express the fuzzy order quantity as the normal triangular fuzzy number (q1, q0, q2), and then we solve the aforementioned optimization problem under the constraints 0 < s ⩽ q1 < q0 < q2, where s denotes the optimizing stock quantity. We find that, after defuzzification, the total cost is slightly higher than in the crisp model; however, it permits better use of the economic fuzzy quantities arising with changes in orders, deliveries, and sales.

Fuzzy inventory with or without backorder for fuzzy order quantity with trapezoid fuzzy number

Abstract The purpose of this paper is to investigate a group of computing schemas for the economic order quantity as fuzzy values of the inventory with/without backorder. We express the fuzzy order quantity as the normal trapezoid fuzzy number (q1,q2,q3,q4), and then we solve the aforementioned optimization problem. We find that, after defuzzification the total cost is slightly higher than in the crisp model; however, it permits better use of the economic fuzzy quantities arising with changes in orders, and deliveries.

A new algorithm for applying fuzzy set theory to evaluate the rate of aggregative risk in software development

In this paper, we present a new algorithm to tackle the rate of aggregative risk in fuzzy circumstances by fuzzy sets theory during any phase of the life cycle. The proposed algorithm is easier, more flexible and useful than the ones they have presented before.

Fuzzy assessment method on sampling survey analysis

Developing a well-designed market survey questionnaire will ensure that surveyors get the information they need about the target market. Traditional sampling survey via questionnaire, which rates item by linguistic variables, possesses the vague nature. It has difficulty in reflecting interviewees incomplete and uncertain thought. Therefore, if we can use fuzzy sense to express the degree of interviewees feelings based on his own concept, the sampling result will be closer to interviewees real thought. In this study, we propose the fuzzy sense on sampling survey to do aggregated assessment analysis. The proposed fuzzy assessment method on sampling survey analysis is easily to assess the sampling survey and evaluate the aggregative evaluation.

Generalization of the group decision making using fuzzy sets theory for evaluating the rate of aggregative risk in software development

Abstract This study is to propose an algorithm of the group decision makers with crisp or fuzzy weights to tackle the rate of aggregative risk in software development in fuzzy circumstances by fuzzy sets theory during any phase of the life cycle. The proposed algorithm is more flexible and useful than the ones we have presented before(H.-M. Lee, Fuzzy Sets and Systems 79 (3) (1996) 323–336; 80 (3) (1996) 261–271, since the weights against decision makers are considered.