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 An Algorithm for Generating a Dispersed Population of Feasible Schedules for Flexible Job Shop Problems

2017 ◽  
Vol 15 (3) ◽  
pp. 16-19
Author(s):  
L. Kirilov ◽  
V. Guliashki
Keyword(s):  
Job Shop ◽  
Flexible Job Shop ◽  
Np Hard ◽  
Important Class ◽  
Scheduling Problems ◽  
Hard Problems ◽  
Np Hard Problems

Abstract The flexible job shop problems (FJSP) are an important class of scheduling problems and they have a significant practical value. Unfortunately it is not easy to solve job shop problems and in particular FJSPs because they are NP-hard problems. In this paper we propose a method for generating a set of feasible schedules for a given FJSP.

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.

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 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 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.

A Two-Stage Hybrid Algorithm for Flexible Job-Shop Scheduling

2011 ◽  
Vol 268-270 ◽  
pp. 476-481
Author(s):  
Li Gao ◽  
Ke Lin Xu ◽  
Wei Zhu ◽  
Na Na Yang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Shop ◽  
Flow Shop ◽  
Job Shop Scheduling ◽  
Selection Procedure ◽  
Dynamic Programming Method ◽  
Two Stage ◽  
Simulated Annealing Genetic Algorithm ◽  
Mutation Operators

A mathematical model was constructed with two objectives. A two-stage hybrid algorithm was developed for solving this problem. At first, the man-hour optimization based on genetic algorithm and dynamic programming method, the model decomposes the flow shop into two layers: sub-layer and patrilineal layer. On the basis of the man-hour optimization，A simulated annealing genetic algorithm was proposed to optimize the sequence of operations. A new selection procedure was proposed and hybrid crossover operators and mutation operators were adopted. A benchmark problem solving result indicates that the proposed algorithm is effective.

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