A Genetic Algorithm for Railway Scheduling Problems

2008 ◽  
pp. 255-276 ◽  
Author(s):  
P. Tormos ◽  
A. Lova ◽  
F. Barber ◽  
L. Ingolotti ◽  
M. Abril ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Problems

Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

2013 ◽  
Vol 651 ◽  
pp. 548-552
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems

2009 ◽  
Vol 419-420 ◽  
pp. 633-636 ◽  
Author(s):  
James C. Chen ◽  
Wun Hao Jaong ◽  
Cheng Ju Sun ◽  
Hung Yu Lee ◽  
Jenn Sheng Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Total Tardiness ◽  
Scheduling Problems ◽  
Test Bed ◽  
Resource Constrained ◽  
Multiple Resources ◽  
Practical Constraints

Resource-constrained multi-project scheduling problems (RCMPSP) consider precedence relationship among activities and the capacity constraints of multiple resources for multiple projects. RCMPSP are NP-hard due to these practical constraints indicating an exponential calculation time to reach optimal solution. In order to improve the speed and the performance of problem solving, heuristic approaches are widely applied to solve RCMPSP. This research proposes Hybrid Genetic Algorithm (HGA) and heuristic approach to solve RCMPSP with an objective to minimize the total tardiness. HGA is compared with three typical heuristics for RCMPSP: Maximum Total Work Content, Earliest Due Date, and Minimum Slack. Two typical RCMPSP from literature are used as a test bed for performance evaluation. The results demonstrate that HGA outperforms the three heuristic methods in term of the total tardiness.

scholarly journals Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem

2018 ◽  
Vol 3 (2) ◽  
pp. 172
Author(s):  
Hamidreza Salmani mojaveri
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Machines ◽  
Job Shop ◽  
Optimization Problems ◽  
Premature Convergence ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Algorithm Implementation

One of the discussed topics in scheduling problems is Dynamic Flexible Job Shop with Parallel Machines (FDJSPM). Surveys show that this problem because of its concave and nonlinear nature usually has several local optimums. Some of the scheduling problems researchers think that genetic algorithms (GA) are appropriate approach to solve optimization problems of this kind. But researches show that one of the disadvantages of classical genetic algorithms is premature convergence and the probability of trap into the local optimum. Considering these facts, in present research, represented a developed genetic algorithm that its controlling parameters change during algorithm implementation and optimization process. This approach decreases the probability of premature convergence and trap into the local optimum. The several experiments were done show that the priority of proposed procedure of solving in field of the quality of obtained solution and convergence speed toward other present procedure.

scholarly journals Lecture Scheduling Automation Using Genetic Algorithm

2019 ◽  
Vol 8 (5C) ◽  
pp. 1444-1447
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Results ◽  
Scheduling Problems ◽  
Scheduling System ◽  
Parameter Values

This paper aims produce an academic scheduling system using Genetic Algorithm (GA) to solve the academic schedule. Factors to consider in academic scheduling are the lecture to be held, the available room, the lecturers and the time of the lecturer, the suitability of the credits with the time of the lecture, and perhaps also the time of Friday prayers, and so forth. Genetic Algorithms can provide the best solution for some solutions in dealing with scheduling problems. Based on the test results, the resulting system can automate the scheduling of lectures properly. Determination of parameter values in Genetic Algorithm also gives effect in producing the solution of lecture schedule

scholarly journals Modifikasi Algoritma Genetika untuk Penyelesaian Permasalahan Penjadwalan Pelajaran Sekolah

2014 ◽  
Vol 10 (1) ◽  
pp. 111
Author(s):  
Rahman Erama ◽  
Retantyo Wardoyo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Effective Algorithm ◽  
Scheduling Problems ◽  
The Core ◽  
Alternative Algorithm ◽  
Crossover Phase ◽  
School Scheduling ◽  
Core Problem ◽  
Standard Genetic Algorithm

AbstrakModifikasi Algoritma Genetika pada penelitian ini dilakukan berdasarkan temuan-temuan para peneliti sebelumnya tentang kelemahan Algoritma Genetika. Temuan-temuan yang dimakasud terkait proses crossover sebagai salah satu tahapan terpenting dalam Algoritma Genetika dinilai tidak menjamin solusi yang lebih baik oleh beberapa peneliti. Berdasarkan temuan-temuan oleh beberapa peneliti sebelumnya, maka penelitian ini akan mencoba memodifikasi Algoritma Genetika dengan mengeliminasi proses crossover yang menjadi inti permasalahan dari beberapa peneliti tersebut. Eliminasi proses crossover ini diharapkan melahirkan algoritma yang lebih efektif sebagai alternative untuk penyelesaian permasalahan khususnya penjadwalan pelajaran sekolah.Tujuan dari penelitian ini adalah Memodifikasi Algoritma Genetika menjadi algoritma alternatif untuk menyelesaikan permasalahan penjadwalan sekolah, sehingga diharapkan terciptanya algoritma alternatif ini bisa menjadi tambahan referensi bagi para peneliti untuk menyelesaikan permasalahan penjadwalan lainnya.Algoritma hasil modifikasi yang mengeliminasi tahapan crossover pada algoritma genetika ini mampu memberikan performa 3,06% lebih baik dibandingkan algoritma genetika sederhana dalam menyelesaikan permasalahan penjadwalan sekolah. Kata kunci—algoritma genetika, penjadwalan sekolah, eliminasi crossover  AbstractModified Genetic Algorithm in this study was based on the findings of previous researchers about the weakness of Genetic Algorithms. crossover as one of the most important stages in the Genetic Algorithms considered not guarantee a better solution by several researchers. Based on the findings by previous researchers, this research will try to modify the genetic algorithm by eliminating crossover2 which is the core problem of several researchers. Elimination crossover is expected to create a more effective algorithm as an alternative to the settlement issue in particular scheduling school.This study is intended to modify the genetic algorithm into an algorithm that is more effective as an alternative to solve the problems of school scheduling. So expect the creation of this alternative algorithm could be an additional resource for researchers to solve other scheduling problems.Modified algorithm that eliminates the crossover phase of the genetic algorithm is able to provide 2,30% better performance than standard genetic algorithm in solving scheduling problems school. Keywords—Genetic Algorithm, timetabling school, eliminate crossover

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