Strict Solution Method for Linear Programming Problem with Ellipsoidal Distributions under Fuzziness

This paper considers a linear programming problem with ellipsoidal distributions including fuzziness. Since this problem is not well-defined due to randomness and fuzziness, it is hard to solve it directly. Therefore, introducing chance constraints, fuzzy goals and possibility measures, the proposed model is transformed into the deterministic equivalent problems. Furthermore, since it is difficult to solve the main problem analytically and efficiently due to nonlinear programming, the solution method is constructed introducing an appropriate parameter and performing the equivalent transformations

Fuzzy Random Weighted Weber Problems in Facility Location

This article considers facility location in a Weber problem with weights including both uncertainty and vagueness. By representing its weights as fuzzy random variables, it can be extended to a fuzzy random weighted Weber problem, and then formulated as a fuzzy random programming problem. By introducing possibility and necessity measures and chance constraints, the extended problem is reformulated to new two types of Weber problems. Based upon characteristics of facility location, theorem for solving the reformulated problems are shown

Possibility/Necessity-Based Probabilistic Expectation Models for Linear Programming Problems with Discrete Fuzzy Random Variables

This paper considers linear programming problems (LPPs) where the objective functions involve discrete fuzzy random variables (fuzzy set-valued discrete random variables). New decision making models, which are useful in fuzzy stochastic environments, are proposed based on both possibility theory and probability theory. In multi-objective cases, Pareto optimal solutions of the proposed models are newly defined. Computational algorithms for obtaining the Pareto optimal solutions of the proposed models are provided. It is shown that problems involving discrete fuzzy random variables can be transformed into deterministic nonlinear mathematical programming problems which can be solved through a conventional mathematical programming solver under practically reasonable assumptions. A numerical example of agriculture production problems is given to demonstrate the applicability of the proposed models to real-world problems in fuzzy stochastic environments

A Semantic Similarity Measurement Method Based on Information Quality in the Structure of the Gene Ontology

Gene ontology (GO) which described a biological concept of gene has attracted attention as an index for measuring semantic similarity of gene. This paper considers a new method for measuring the semantic similarity of GO through an extension and combination of two existing methods by Resnik and Wang et al. in order to improve their drawbacks of effects of shallow annotation. It is shown that the proposed method is superior to existing methods through experiments with pathway data

Agent-based Simulation Analysis for Network Formation

In this paper, we conduct agent-based simulation experiments for network formation analysis. In the published papers, Bala and Goyal (2000) have constructed a mathematical model leading a star network to be strict Nash equilibrium. However, Berninghaus et al. (2007) have conducted the laboratory experiments using human subjects basing on the mathematical model, and the result of the experiments indicates that human subjects do not always make decision just as the mathematical model predicted. In this paper, we propose a simulation model using the adaptive artificial agents to clarify the reason of the deviation from the mathematical predictions

An Evolutionary Multi-Objective Optimization-Based Constructive Method for Learning Classifier Systems Adjusting to Non-Markov Environments

Learning Classifier Systems (LCSs) are rule-based systems that automatically build their rule set so as to get optimal policies through evolutionary processes. This paper considers an evolutionary multi-objective optimization-based constructive method for LCSs that adjust to non-Markov environments. Our goal is to construct a XCSMH (eXtended Classifier System - Memory Hierarchic) that can obtain not only optimal policies but also highly generalized rule sets. Results of numerical experiments show that the proposed method is superior to an existing method with respect to the generality of the obtained rule sets

An approximate solution method based on tabu search for k-minimum spanning tree problems

This paper considers k-minimum spanning tree problems. An existing solution algorithm based on tabu search, which was proposed by Katagiri et al., includes an iterative solving procedure of minimum spanning tree (MST) problems for subgraphs to obtain a local optimal solution of k-minimum spanning tree problems. This article provides a new tabu-searchbased approximate solution method that does not iteratively solve minimum spanning tree problems. Results of numerical experiments show that the proposed method provides a good performance in terms of accuracy over those of existing methods for relatively high cardinality k

Risk-Controlled Multiobjective Portfolio Selection Problem Using a Principle of Compromise

This paper proposes a multiobjective portfolio selection problem with most probable random distribution derived from current market data and other random distributions of boom and recession under the risk-controlled parameters determined by an investor. The current market data and information include not only historical data but also interpretations of economists’ oral and linguistic information, and hence, the boom and recession are often caused by these nonnumeric data. Therefore, investors need to consider several situations from most probable condition to boom and recession and to avoid the risk less than the target return in each situation. Furthermore, it is generally difficult to set random distributions of these cases exactly. Therefore, a robust-based approach for portfolio selection problems using the only mean values and variances of securities is proposed as a multiobjective programming problem. In addition, an exact algorithm is developed to obtain an explicit optimal portfolio using a principle of compromise