A Review of Genetic Algorithm Applications in Solving Vehicle Routing Problem

Mohammed, Mazin Abed; Ghani, Mohd Khanapi Abd; Obaid, Omar Ibrahim; Mostafa, Salama A.; Ahmad, Mohd Sharifuddin; Ibrahim, Dheyaa Ahmed; Burhanuddin, M. A.

doi:10.36478/jeasci.2017.4267.4283

Cited by 5 publications

(4 citation statements)

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“…GA 21,22 is a kind of algorithm based on principles of natural selection and genetics; it simulates the biological evolution. Because GA has rapid random search ability, strong robustness, simple process, strong extensibility, and the other characteristics, it has been widely applied in various fields [23][24][25][26] and also has been proven to be one of the most effective evolutionary techniques for solving job shop scheduling problems. [27][28][29][30][31] Therefore, in this article, we use GA to solve the DRCFJSP.…”

Section: Introductionmentioning

confidence: 99%

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Guo

et al. 2018

Advances in Mechanical Engineering

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In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker's learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a threedimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.

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Section: Introductionmentioning

confidence: 99%

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Guo

et al. 2018

Advances in Mechanical Engineering

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“…Various concepts have been explored in the realm analysis methodology, including the impact of pole piece saturation on the focal characteristics of the target lens [23] and the influence of axial magnetic field distribution on the asymmetry of the objective lens in high-voltage electron microscopy [24]. The first methodology is called the "examination/analysis methodology" and depends on the experimentation that includes three categories known as "H1 programs" for magnetic scalar potential together with using and understanding of the Laplace conditions [25] and "H2 and H3 programs" for magnetic vector potential, addressing the conditions of Poisson [26]. The second methodology is the "H4 programs" for the "union/synthesis method".…”

Section: Literature Reviewmentioning

confidence: 99%

Program for Calculating the Axial Magnetic Field Distribution of Magnetic Lenses Using Finite Element Method

2023

JJP

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Introducing FEM-CMFD (Finite Element Method for Calculating Magnetic Field Distribution), a new program designed for calculating the magnetic field distribution for axially symmetric complex magnetic systems, such as magnetic lenses. The program operates by reading user-inputted magnetic system data presented in a two-dimensional graph. Utilizing the finite element method, it calculates the axial magnetic field distribution for various coil excitations accommodating up to approximately 10 000 intersection nodes. Keywords: Electron magnetic lens, Finite element method, Magnetic field distribution.

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“…Problemi GA ile çözmeye çalışan birçok yazar kromozomları oluşturmak için rastgele bir atama işlemi uygulamıştır [26], [27]. Bizim çalışmamızda ise başlangıç rotalarının oluşturulmasında genetik çeşitlilik açısından bir rastgelelik olsa da tamamen random bir atama yoktur.…”

Section: Li̇teratürunclassified

Üniversite Ders Çizelgeleme Probleminin Genetik Algoritma ile Optimizasyonu

Çolak

Yi̇ği̇t

2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Üniversitelerin her dönem başında yaptığı ders çizelgeleme problemi kombinatortal optimizasyon problemlerindendir. Çizelgeleme problemleri NP-Hard sınıfına giren ve çözümü zor problemlerdendir. Determinist bir yaklaşımla olası bütün ihtimallerin denenmesi gibi algoritmalarla çözüm mümkün olsa da çok zaman alıcı bir işlem olduğundan pratikte bu algoritmalar kullanılmamaktadır. Özelikle probleme ait veriler arttıkça ve çözülmesi gereken çok fazla kısıt olması durumunda çözüme ulaşmak daha da güçleşmektedir. Bu çalışmada ders çizelgeleme problemi çözülmesi gereken katı ve esnek kısıtlar olarak ele alınmıştır. Katı kısıtlar ders çakışması, derslik çakışması, kapasiteye uygun olmayan dersliğe şube atanması gibi kesin olarak çözülmesi gereken kısıtlardır. Esnek kısıtlar ise derslerin istenmeyen zaman dilimlerine atanması bir kısmı ihmal edilebilen kısıtlardır. Bu çalışmada probleme ait katı ve esnek kısıtlar belirlenmiş ve bu kısıtları ihlal edilen durumlara ceza puanları atanarak en az ceza puanına sahip çözümler aranmıştır. Problemin çözümü için çizelgeleme problemlerinde sıkılıkla kullanılan Genetik Algoritma kullanılmıştır. Yapılan testeler sonucunda Genetik Algoritma ile ders çizelgeleme probleminin kısa sürede çözülebildiği görülmüştür.

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A Review of Genetic Algorithm Applications in Solving Vehicle Routing Problem

Cited by 5 publications

References 0 publications

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Program for Calculating the Axial Magnetic Field Distribution of Magnetic Lenses Using Finite Element Method

Üniversite Ders Çizelgeleme Probleminin Genetik Algoritma ile Optimizasyonu

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