Optimization of Ship’s Route Scheduling Using Genetic Algorithm

2016 ◽  
Vol 2 (1) ◽  
pp. 180 ◽  
Author(s):  
Vivi Nur Wijayaningrum ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Mutation Rate ◽  
Computational Experiment ◽  
Optimal Parameters ◽  
Ship Scheduling ◽  
Complicated Process ◽  
Ship Route ◽  
Hard Problems ◽  
Route Scheduling ◽  
Np Hard Problems

<span lang="EN-US">Route scheduling is a quite complicated process because it involves some determinant factors. Several methods have been used to help resolve the NP-hard problems. This research uses genetic algorithm to assist in optimizing ship scheduling, that where there are several ports to be visited by some ships. The goal is to divide the ship to go to a specific port so that each port is only visited by one ship to minimize the total distance of all ships. The computational experiment produces optimal parameters such as the number of popsize is 30, the number of generations is 100, crossover rate value is 0.3 and mutation rate values is 0.7. The final results is an optimal ship route by minimizing the distance of each ship.</span>

scholarly journals Application of genetic algorithm to industrial scheduling and problems of parameters evaluation

2021 ◽  
Vol 47 ◽  
Author(s):  
Edgaras Šakurovas ◽  
Narimantas Listopadskis
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Flow Shop ◽  
Np Hard ◽  
Scheduling Problems ◽  
Optimal Parameters ◽  
Algorithm Performance ◽  
Hard Problems ◽  
Np Hard Problems ◽  
Real World Problems

Genetic algorithms are widely used in various mathematical and real world problems. They are approximate metaheuristic algorithms, commonly used for solving NP-hard problems in combinatorial optimisation. Industrial scheduling is one of the classical NP-hard problems. We analyze three classical industrial scheduling problems: job-shop, flow-shop and open-shop. Canonical genetic algorithm is applied for those problems varying its parameters. We analyze some aspects of parameters such as selecting optimal parameters of algorithm, influence on algorithm performance. Finally, three strategies of algorithm – combination of parameters and new conceptualmodel of genetic algorithm are proposed.

scholarly journals Genetic Algorithm for Teaching Distribution based on Lecturers’ Expertise

2019 ◽  
pp. 297-304
Author(s):  
Rizki Agung Pambudi ◽  
Wahyuni Lubis ◽  
Firhad Rinaldi Saputra ◽  
Hanif Prasetyo Maulidina ◽  
Vivi Nur Wijayaningrum
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Mutation Rate ◽  
Learning Process ◽  
Teaching And Learning ◽  
Optimization Methods ◽  
Test Results ◽  
Optimal Parameters ◽  
Crossover Rate ◽  
Fitness Value

The teaching distribution for lecturers based on their expertise is very important in the teaching and learning process. Lecturers who teach a course that is in accordance with their interests and abilities will make it easier for them to deliver material in class. In addition, students will also be easier to accept the material presented. However, in reality, the teaching distribution is often not in accordance with the expertise of the lecturer so that the lecturers are not optimal in providing material to their students. This problem can be solved using optimization methods such as the genetic algorithm. This study offers a solution for teaching distribution that focuses on the interest of each lecturer by considering the order of priorities. The optimal parameters of the test results are crossover rate (cr) = 0.6, mutation rate (mr) = 0.4, number of generations = 40, and population size = 15. Genetic algorithm is proven to be able to produce teaching distribution solutions with a relatively high fitness value at 4903.3.

scholarly journals Exploring human genome feature for improving genetic algorithm performance

2019 ◽  
Vol 270 ◽  
pp. 03001
Author(s):  
Febri Zukhruf ◽  
Irma Susan Kurnia ◽  
Russ Bona Frazila ◽  
Gaga Irawan Nugraha ◽  
Mas Rizky A.A Syamsunarno
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Transportation Network ◽  
Network Design Problem ◽  
Darwinian Evolution ◽  
Genetic Operators ◽  
Algorithm Performance ◽  
Genome Feature ◽  
Hard Problems ◽  
Np Hard Problems

Genetic algorithm (i.e., GA) has longtermly obtained an extensive recognition for solving the optimization problem. Its pipelines process, which involves several operations, has been applied in many NP-hard problems, including the transportation network design problem (i.e., TNDP). As part of evolutionary computation methods, GA is inspired by Darwinian evolution, which is relied on the genetic operators (i.e., recombination, and mutation). On other side, the considerably achievement has been acquired by the genome researches, which offers an opportunity to deeply explore the recombination and mutation processes. This paper then presents variants of GA, which are inspired by the recent genome evidence of genetic operators. This exploration expectantly extends the benefit of evolution-based algorithm, which has been shown by the previous finding of GA. For examining the performance of proposed GA, the numerical experiment is involved for solving the TNDP. The performance comparisons show that the variation of crossover rate within a certain group of population provide better result than the standard GA.

scholarly journals Meta-Heuristic Solutions to a Student Grouping Optimization Problem faced in Higher Education Institutions

2020 ◽  
pp. 61-74
Author(s):  
Kenekayoro Patrick ◽  
Biralatei Fawei
Keyword(s):  
Higher Education ◽  
Genetic Algorithm ◽  
Optimization Problem ◽  
Higher Education Institutions ◽  
Combinatorial Problems ◽  
Np Hard ◽  
Ant Colony Optimization Algorithm ◽  
Grouping Students ◽  
Hard Problems ◽  
Np Hard Problems

Combinatorial problems which have been proven to be NP-hard are faced in Higher Education Institutions and researches have extensively investigated some of the well-known combinatorial problems such as the timetabling and student project allocation problems. However, NP-hard problems faced in Higher Education Institutions are not only confined to these categories of combinatorial problems. The majority of NP-hard problems faced in institutions involve grouping students and/or resources, albeit with each problem having its own unique set of constraints. Thus, it can be argued that techniques to solve NP-hard problems in Higher Education Institutions can be transferred across the different problem categories. As no method is guaranteed tooutperform all others in all problems, it is necessary to investigate heuristic techniques for solving lesser-known problems in order to guide stakeholders or software developers to the most appropriate algorithm for each unique class of NP-hard problems faced in Higher Education Institutions. To this end, this study described an optimization problem faced in a real university that involved grouping students for the presentation of semester results. Ordering based heuristics, genetic algorithm and the ant colony optimization algorithm implemented in Python programming language were used to find feasible solutions to this problem, with the ant colony optimization algorithm performing better or equal in 75% of the test instances and the genetic algorithm producing better or equal results in 38% of the test instances.

scholarly journals Population-based Method for Optimizing Targeted Offers Problem in Direct Marketing Campaigns

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Programming ◽  
Direct Marketing ◽  
Bat Algorithm ◽  
Population Based ◽  
Exact Methods ◽  
Main Challenge ◽  
Hard Problems ◽  
The Right ◽  
Np Hard Problems

In direct marketing campaigns, the optimization of targeted offers problem is a big business concern. The main goal is to maximize the company’s profit by reaching the right clients. The main challenge faced by companies when advertising, is to configure properly a campaign by choosing the appropriate target, so it is guaranteed a high acceptance of users to advertisements. When dealing with an important size of data, the important specification to consider is the combinatorial aspect of the problem and the limitation of the approach based on mathematical programming methods. In this article, and since this problem belongs to the class of NP-hard problems, the use of metaheuristic, instead of exact methods, is essential; the Bat Algorithm which is a new inspired algorithm is proposed after hybridization with Genetic Algorithm. Computational experiments show that the proposed algorithm was able to give good and competitive solutions

scholarly journals Solving N-queen Problem Using Genetic Algorithm by Advance Mutation Operator

2018 ◽  
Vol 8 (6) ◽  
pp. 4519
Author(s):  
Vinod Jain ◽  
Jay Shankar Prasad
Keyword(s):  
Genetic Algorithm ◽  
Np Hard ◽  
Mutation Operator ◽  
Science And Engineering ◽  
New Mutation ◽  
Hard Problems ◽  
Np Hard Problems

N-queen problem represents a class of constraint problems. It belongs to set of NP-Hard problems. It is applicable in many areas of science and engineering. In this paper N-queen problem is solved using genetic algorithm. A new genetic algoerithm is proposed which uses greedy mutation operator. This new mutation operator solves the N-queen problem very quickly. The proposed algorithm is applied on some instances of N-queen problem and results outperforms the previous findings.

Research on P-Hub Network Based on Rough Set: Model and Algorithm

2011 ◽  
Vol 211-212 ◽  
pp. 720-725
Author(s):  
Qing Liu ◽  
Tong Shui Wu ◽  
Xian Fei Luo
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Rough Set ◽  
Evaluation System ◽  
Median Problem ◽  
Route Network ◽  
Hard Problems ◽  
Hub Network ◽  
Improved Model ◽  
Np Hard Problems

To solve the classical uncapacitated multiple allocation p-hub median problem, (UMpHMP), this paper establishes the evaluation System of hub-index by making use of the rough set data mining technology to reduce the range of hub choice from n alternative hubs to the limited q(q<n) airports, which greatly reduces the variables and constraints of the UMpHMP model. To solve the NP-hard problems, the genetic algorithm was designed according to the improved model for solutions. The simulation example of domestic 15 cities the route network designing indicates that the new model can reduce the solution complexity and increase the efficiency.

Sign in / Sign up

Export Citation Format

Share Document