Genetic Algorithm for Solving Multi-Objective Optimization in Examination Timetabling Problem

Son, Ngo Tung; Jaafar, Jafreezal; Aziz, Izzatdin Abdul; Nguyen, Giang; Anh, Bùi Ngọc

doi:10.3991/ijet.v16i11.21017

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…Белгілі және кеңінен қолданылатын генетикалық алгоритм сияқты оңтайландыру есептерін шешуде сәтті және тиімді болып табылатын көптеген эволюциялық алгоритмдер бар: * Генетикалық алгоритм (GA) Генетикалық алгоритм-табиғи эволюциялық процеске негізделген және табиғи сұрыпталу процесінде жұмыс істейтін молекулалық биологиядан шабыттанған стохастикалық іздеу әдісі [14]. GA үлкен кеңістікте тамаша іздеу қабілетіне ие, икемді алгоритм болып табылады және әдетте сенімді шешіммен сипатталатын күрделі комбинаторлық есептер [14]. Алгоритм шешімді жақсы және жаман фенотиптерді тасымалдайтын хромосома ретінде қарастырады, содан кейін қайта алу әдісін қолданады.…”

Section: эволюциялық алгоритмдер (Eas)unclassified

Review and analysis of existing methods of scheduling classes

Kazbekova¹,

Nurtaza²

2023

fızıka matematıka ınformatıka serııasy

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One of the most important steps in organizing the quality of the educational process in educational institutions and the effectiveness of the use of scientific and pedagogical potential is the task of forming a high-quality lesson schedule. A qualitatively designed lesson schedule should ensure the uniform loading of student groups and teaching staff. The problem of university course planning (UCTP) is a specific problem of course planning at a university, with simultaneous use of resources such as students, faculty, and classrooms. These problems are considered non-polynomial time (NP) and Combinatorial Optimization (COP) problems, which means that they can be solved by optimization algorithms to obtain the desired table. Several methods have been used in universities to solve table problems, and most of them use optimization methods. This article discusses six methods for solving problems with the schedule of classes in universities: Sequential methods, сluster methods, сonstraint-based methods, meta-heuristic methods, generalized search, hybrid evolutionary algorithms, multi-criteria approaches, case-based reasoning techniques, hyper-heuristics, adaptive approaches. The purpose of this article is a comprehensive review of optimization approaches to solving the problems of scheduling in universities. In addition, when solving such problems, the accepted algorithms for Meta-heuristic optimization are shown. In addition, methods have been developed to analyze and measure the performance of a solution by specifying frequently used control data sets.

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Section: эволюциялық алгоритмдер (Eas)unclassified

Review and analysis of existing methods of scheduling classes

Kazbekova¹,

Nurtaza²

2023

fızıka matematıka ınformatıka serııasy

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show abstract

“…For this purpose, the weighted 𝐿 𝑝 metrics measure the distance of any solution from the reference point. The ideal objective vector is often used as the reference point: Many studies have used CP to approach the MOP problem, such as for university timetabling [40][41][42], team selection [43], in a knowledge-based recommender [44], and project task assignment [45]. However, this t may require pre-defined minimal and maximal values of the objective functions.…”

Section: Compromise Programming For Mop-vrpmentioning

confidence: 99%

Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment Problem

Son

Jaafar

Aziz

et al. 2022

Entropy

Self Cite

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The Vehicle Routing Problem (VRP) and its variants are found in many fields, especially logistics. In this study, we introduced an adaptive method to a complex VRP. It combines multi-objective optimization and several forms of VRPs with practical requirements for an urban shipment system. The optimizer needs to consider terrain and traffic conditions. The proposed model also considers customers’ expectations and shipper considerations as goals, and a common goal such as transportation cost. We offered compromise programming to approach the multi-objective problem by decomposing the original multi-objective problem into a minimized distance-based problem. We designed a hybrid version of the genetic algorithm with the local search algorithm to solve the proposed problem. We evaluated the effectiveness of the proposed algorithm with the Tabu Search algorithm and the original genetic algorithm on the tested dataset. The results show that our method is an effective decision-making tool for the multi-objective VRP and an effective solver for the new variation of VRP.

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“…For this purpose, the weighted 𝐿 𝑝 metrics measure the distance of any solution from the reference point. The ideal objective vector is often used as the reference point: There are many studies that have used CP to approach the MOP problem such as for university timetabling [40], [41], [42], or in team selection [43], in knowledge based recommender [44], project task assignment [45]. However, it may require pre-defined minimal and maximal values of the objective functions.…”

Section: Compromise Programming For Mop-vrpmentioning

confidence: 99%

Metaheuristic Algorithms Based on Compromise Programming for Multi-Objective Urban Shipment Problem

Son¹,

Jaafar²,

Aziz³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The Vehicle Routing Problem (VRP) and its variants are found in many fields, especially logistics. In this study, we introduced an adaptive method to a complex VRP. It combines multi-objective optimization and several forms of VRPs with practical requirements for an urban shipment system. The optimizer needs to consider terrain and traffic conditions. The proposed model also considers customers' expectations, shipper considerations as goals, and the common goal like transportation cost. We offered compromise programming to approach the multi-objective problem by decomposing the original multi-objective problem into a minimized distance-based problem. We designed a hybrid version of the Genetic algorithm with the Local Search algorithm to solve the proposed problem. We evaluate the effectiveness of the proposed algorithm with the Tabu Search algorithm, the original Genetic algorithm on the tested dataset. The results show that our method is an effective decision-making tool for the multi-objective VRP and an effective solver for the new variation of VRP.

show abstract

Genetic Algorithm for Solving Multi-Objective Optimization in Examination Timetabling Problem

Cited by 14 publications

References 19 publications

Review and analysis of existing methods of scheduling classes

Review and analysis of existing methods of scheduling classes

Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment Problem

Metaheuristic Algorithms Based on Compromise Programming for Multi-Objective Urban Shipment Problem

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