Descriptive statistics with vague data

Kruse, Rudolf; Meyer, Klaus Dieter

doi:10.1007/978-94-009-3943-1_8

Cited by 6 publications

(2 citation statements)

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“…In this paper, we consider a crop planning problem where the profit coefficients for crops are fuzzy numbers. We propose a model based on the concept of maximizing the possibility measure, and an effective algorithm for the problem , Itoh et al (2000Itoh et al ( , 2001Itoh et al ( , 2003, Kaufmann (1995), Kruse and Dieter (1987), Yanger (1982), Zimmermann (1991)). Further, there are stochastic climate factors (weather, temperature, rainfall level, amount of insolation, etc.)…”

Section: Introductionmentioning

confidence: 99%

A Crop Planning Problem with Fuzzy Random Profit Coefficients

Toyonaga

Itoh

Ishii

2005

Fuzzy Optim Decis Making

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The main purpose of this paper is to present a crop planning problem for agricultural management under uncertainty. It is significant that agricultural managers assign their limited farmlands to cultivation of which crops in a season. This planning is called the crop planning problem and influences their incomes for the season. Usually, the crop planning problem is formulated as a linear programming problem. But there are many uncertain factors in agricultural problems, so future profits for crops are not certain values. A linear programming model with constant profit coefficients may not reflect the environment of decision making properly. Therefore, we propose a model of crop planning with fuzzy profit coefficients, and an effective solution procedure for the model. Furthermore, we extend this fuzzy model, setting the profit coefficients as discrete randomized fuzzy numbers. We show concrete optimal solutions for each models.

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Section: Introductionmentioning

confidence: 99%

A Crop Planning Problem with Fuzzy Random Profit Coefficients

Toyonaga

Itoh

Ishii

2005

Fuzzy Optim Decis Making

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“…In such a case, randomness and fuzziness are required to be considered simultaneously. Kwakernaak [9], [10]first introduced the notion of fuzzy random variables, then this concept was developed by several researchers such as Puri and Ralescu [11], and Kruse and Meyer [12]. Roughly speaking, a fuzzy random variable defined by Kwakernaak [9], [10] is a measurable function from a probability space to a collection of fuzzy numbers, and its expected value is described as a fuzzy number.…”

Section: Introductionmentioning

confidence: 99%

Equilibrium Strategy for Two-Person Zero-Sum Matrix Game with Random Fuzzy Payoffs

2010

2010 Asia-Pacific Conference on Wearable Computing Systems

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This paper investigates a two-person zero-sum matrix game in which the payoffs are characterized as random fuzzy variables. Based on random fuzzy expected value operator, a random fuzzy expected minimax equilibrium strategy to the game is defined. Then an iterative algorithm based on random fuzzy simulation is designed to seek the minimax equilibrium strategy. Finally, a numerical example is provided to illustrate the effectiveness of the algorithm.

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