Prof. Dr. Salwani Abdullah

Committee: International Scientific Committee of Computer and Information Engineering
University: Universiti Kebangsaan Malaysia
Department: Faculty of Information Science and Technology
Research Fields: rough set theory, attribute reduction, fuzzy logic, memetic algorithms, record to record algorithm, great deluge algorithm,

Publications

2 Job Shop Scheduling: Classification, Constraints and Objective Functions

Authors: Salwani Abdullah, Majid Abdolrazzagh-Nezhad

Abstract:

The job-shop scheduling problem (JSSP) is an important decision facing those involved in the fields of industry, economics and management. This problem is a class of combinational optimization problem known as the NP-hard problem. JSSPs deal with a set of machines and a set of jobs with various predetermined routes through the machines, where the objective is to assemble a schedule of jobs that minimizes certain criteria such as makespan, maximum lateness, and total weighted tardiness. Over the past several decades, interest in meta-heuristic approaches to address JSSPs has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides the classification, constraints and objective functions imposed on JSSPs that are available in the literature.

Keywords: classification, Constraints, job-shop scheduling, objective functions

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1 Fuzzy Population-Based Meta-Heuristic Approaches for Attribute Reduction in Rough Set Theory

Authors: Salwani Abdullah, Mafarja Majdi, Najmeh S. Jaddi

Abstract:

One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: Memetic algorithms, Rough Set Theory, attribute reduction, record to record algorithm, Fuzzy logic, Great Deluge Algorithm

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Abstracts

2 Job Shop Scheduling: Classification, Constraints and Objective Functions

Authors: Salwani Abdullah, Majid Abdolrazzagh-Nezhad

Abstract:

The job-shop scheduling problem (JSSP) is an important decision facing those involved in the fields of industry, economics and management. This problem is a class of combinational optimization problem known as the NP-hard problem. JSSPs deal with a set of machines and a set of jobs with various predetermined routes through the machines, where the objective is to assemble a schedule of jobs that minimizes certain criteria such as makespan, maximum lateness, and total weighted tardiness. Over the past several decades, interest in meta-heuristic approaches to address JSSPs has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides the classification, constraints and objective functions imposed on JSSPs that are available in the literature.

Keywords: classification, Constraints, job-shop scheduling, objective functions

Procedia PDF Downloads 236
1 Fuzzy Population-Based Meta-Heuristic Approaches for Attribute Reduction in Rough Set Theory

Authors: Salwani Abdullah, Mafarja Majdi, Najmeh S. Jaddi

Abstract:

One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: Fuzzy Logic, Memetic algorithms, Rough Set Theory, attribute reduction, record to record algorithm, great deluge algorithm

Procedia PDF Downloads 301