Fuzzy Greedy Search: An Algorithmic Approach for Combinatorial Optimisation

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Author(s): Kaveh Sheibani (ORLab Analytics, Canada)
Copyright: 2021
Pages: 22
Source title: Advanced Models and Tools for Effective Decision Making Under Uncertainty and Risk Contexts
Source Author(s)/Editor(s): Vicente González-Prida (University of Seville, Spain & National University of Distance Education, Spain)and María Carmen Carnero (University of Castilla-La Mancha, Spain)
DOI: 10.4018/978-1-7998-3246-1.ch010

Keywords: Business & Management / Business and Organizational Research / Business Science Reference / Risk Management & Analysis

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Abstract

In recent years, there has been a growth of interest in the development of systematic search methods for solving problems in operational research and artificial intelligence. This chapter introduces a new idea for the integration of approaches for hard combinatorial optimisation problems. The proposed methodology evaluates objects in a way that combines fuzzy reasoning with a greedy mechanism. In other words, a fuzzy solution space is exploited using greedy methods. This seems to be superior to the standard greedy version. The chapter consists of two main parts. The first part focuses on description of the theory and mathematics of the so-called fuzzy greedy evaluation concept. The second part demonstrates through computational experiments the effectiveness and efficiency of the proposed concept for hard combinatorial optimisation problems.

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Fuzzy Greedy Search: An Algorithmic Approach for Combinatorial Optimisation

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