scholarly journals Genetic Algorithm for lecturing schedule optimization

2019 ◽  
Vol 13 (1) ◽  
pp. 83 ◽  
Author(s):  
David Kristiadi ◽  
Rudy Hartanto
Keyword(s):  
Genetic Algorithm ◽  
Optimal Schedule ◽  
Optimal Solution ◽  
Computation Method ◽  
Scheduling Problems ◽  
Study Program ◽  
Directed Mutation ◽  
Classic Problem ◽  
Mutation Operators ◽  
Fitness Value

Scheduling is a classic problem in lecturing. Rooms, lecturers, times and scheduling constraints must be managed well to get an optimal schedule. University of Boyolali (UBY) also encounter the same scheduling problems. The problem was tried to be solved by building a library based on Genetic Algorithm (GA). GA is a computation method which inspired by natural selection. The computation consists of some operators i.e. Tournament Selection, Uniform Crossover, Weak Parent Replacement and two mutation operators (Interchanging Mutation and Violated Directed Mutation (VDM)). The two mutation method are compared to find which better mutation operator. The library was planned to have a capability to define custom constraints (scheduling requirements that were not accommodated by the library) without core program modifications. The test results show that VDM is more promising for optimal solutions than Interchanging Mutation. In UBY cases, optimal solution (fitness value=1) is reached in 12 minutes 41 second with adding 6 new room and inactivated 2 constraint i.e. lecturing begins at 14.00 except for 3rd semester of science law study program with morning class and lecturing participants must not over classroom capacity.

scholarly journals Penerapan Metode Hybrid Genetic Algorithm (GA) dan Pattern Search (PS) untuk Penjadwalan Mata Kuliah Universitas

2020 ◽  
Vol 9 (3) ◽  
pp. 201-212
Author(s):  
Fani Puspitasari ◽  
Parwadi Moengin
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Pattern Search ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Course Scheduling ◽  
Fitness Value ◽  
Optimum Solution ◽  
University Course

The problem of university course scheduling is a complicated job to do because of the many constraints that must be considered, such as the number of courses, the number of rooms available, the number of students, lecturer preferences, and time slots. The more courses that will be scheduled, the scheduling problem becomes more complex to solve. Therefore, it is necessary to set an automatic course schedule based on optimization method. The aim of this research is to gain an optimal solution in the form of schedule in order to decrease the number of clashed courses, optimize room utilization and consider the preferences of lecturer-course. In this research, a hybridization method of Genetic Algorithm (GA) and Pattern Search (PS) is investigated for solving university course scheduling problems. The main algorithm is GA to find the global optimum solution, while the PS algorithm is used to find the local optimum solution that is difficult to obtain by the GA method. The simulation results with 93 courses show that the Hybrid GA-PS method works better than does the GA method without hybrid, as evidenced by the better fitness value of the hybrid GA-PS method which is -3528.62 and 99.24% of the solutions achieved. While the GA method without hybrid is only able to reach a solution of around 65% and has an average fitness value of -3100.76.

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.

Theoretical Analysis on Powers-of-Two Applied to JSP

2011 ◽  
Vol 2 (2) ◽  
pp. 1-20
Author(s):  
V. Mahesh ◽  
L. Siva Rama Krishna ◽  
Sandeep Dulluri ◽  
C. S. P. Rao
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Schedule ◽  
Optimal Solution ◽  
Mathematical Induction ◽  
Release Dates ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Optimal Value

This paper discusses the scheduling of precedence-related jobs non-preemptively in a job shop environment with an objective of minimizing the makespan. Due to the NP-hard nature of the scheduling problems, it is usually difficult to find an exact optimal schedule and hence one should rely on finding a near to optimal solution. This paper proposes a computationally effective powers-of-two heuristic for solving job shop scheduling problem. The authors prove that the makespan obtained through powers-of-two release dates lies within 6% of the optimal value. The authors also prove the efficacy of powers-of-two approach through mathematical induction.

A Natural Evolution Based Numerical Optimisation Framework to Develop and Enhance Airfoil-Slat Arrangement

2019 ◽  
Author(s):  
Sushrut Kumar ◽  
Priyam Gupta ◽  
Raj Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Fuel Efficiency ◽  
Optimal Solution ◽  
Leading Edge ◽  
Initial Population ◽  
Invasive Weed ◽  
Fitness Value ◽  
Leading Edge Slats ◽  
Dynamics Simulations

Abstract Leading Edge Slats are popularly being put into practice due to their capability to provide a significant increase in the lift generated by the wing airfoil and decrease in the stall. Consequently, their optimum design is critical for increased fuel efficiency and minimized environmental impact. This paper attempts to develop and optimize the Leading-Edge Slat geometry and its orientation with respect to airfoil using Genetic Algorithm. The class of Genetic Algorithm implemented was Invasive Weed Optimization as it showed significant potential in converging design to an optimal solution. For the study, Clark Y was taken as test airfoil. Slats being aerodynamic devices require smooth contoured surfaces without any sharp deformities and accordingly Bézier airfoil parameterization method was used. The design process was initiated by producing an initial population of various profiles (chromosomes). These chromosomes are composed of genes which define and control the shape and orientation of the slat. Control points, Airfoil-Slat offset and relative chord angle were taken as genes for the framework and different profiles were acquired by randomly modifying the genes within a decided design space. To compare individual chromosomes and to evaluate their feasibility, the fitness function was determined using Computational Fluid Dynamics simulations conducted on OpenFOAM. The lift force at a constant angle of attack (AOA) was taken as fitness value. It was assigned to each chromosome and the process was then repeated in a loop for different profiles and the fittest wing slat arrangement was obtained which had an increase in CL by 78% and the stall angle improved to 22°. The framework was found capable of optimizing multi-element airfoil arrangements.

Solving Flow Shop Scheduling Problems with Blocking by using Genetic Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 20-38
Author(s):  
Harendra Kumar ◽  
Pankaj Kumar ◽  
Manisha Sharma
Keyword(s):  
Genetic Algorithm ◽  
Completion Time ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Working Process ◽  
Shop Scheduling ◽  
Mutation Operators ◽  
And Inversion ◽  
Present Algorithm

Flow shop scheduling problems have been analyzed worldwide due to their various applications in industry. In this article, a new genetic algorithm (NGA) is developed to obtain the optimum schedule for the minimization of total completion time of n-jobs in an m-machine flow shop operating without buffers. The working process of the present algorithm is very efficient to implement and effective to find the best results. To implement the proposed algorithm more effectively, similar job order crossover operators and inversion mutation operators have been used. Numerous examples are illustrated to explain proposed approach. Finally, the computational results indicate that present NGA performs much superior to the heuristics for blocking flow shop developed in the literature.

scholarly journals A Memetic Genetic Algorithm for the Vertex p-center Problem

2008 ◽  
Vol 16 (3) ◽  
pp. 417-436 ◽  
Author(s):  
Wayne Pullan
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Population Based ◽  
Approximate Algorithms ◽  
Center Problem ◽  
Maximum Cost ◽  
Directed Mutation ◽  
Mutation Operators ◽  
Benchmark Instances ◽  
Comparable Performance

The p-center problem is one of choosing p facilities from a set of candidates to satisfy the demands of n clients in order to minimize the maximum cost between a client and the facility to which it is assigned. In this article, PBS, a population based meta-heuristic for the p-center problem, is described. PBS is a genetic algorithm based meta-heuristic that uses phenotype crossover and directed mutation operators to generate new starting points for a local search. For larger p-center instances, PBS is able to effectively utilize a number of computer processors. It is shown empirically that PBS has comparable performance to state-of-the-art exact and approximate algorithms for a range of p-center benchmark instances.

Theoretical Analysis on Powers-of-Two Applied to JSP

2013 ◽  
pp. 66-86
Author(s):  
V. Mahesh ◽  
L. Siva Rama Krishna ◽  
Sandeep Dulluri ◽  
C. S. P. Rao
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Schedule ◽  
Optimal Solution ◽  
Mathematical Induction ◽  
Release Dates ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Optimal Value

This paper discusses the scheduling of precedence-related jobs non-preemptively in a job shop environment with an objective of minimizing the makespan. Due to the NP-hard nature of the scheduling problems, it is usually difficult to find an exact optimal schedule and hence one should rely on finding a near to optimal solution. This paper proposes a computationally effective powers-of-two heuristic for solving job shop scheduling problem. The authors prove that the makespan obtained through powers-of-two release dates lies within 6% of the optimal value. The authors also prove the efficacy of powers-of-two approach through mathematical induction.

Penjadwalan Produksi Dengan Algoritma Genetik Untuk Meniminisasi Makespan di PT AAA

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
M. Ghassan Fattah ◽  
Rosnani Ginting
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Machine Scheduling ◽  
Optimal Timing ◽  
Scheduling Problems ◽  
Fitness Value ◽  
Genetic Algorithm Method

PT. AAA dari bulan Januari sampai Desember mendapat total 88 order dengan jumlah keterlambatan 12 order maka persentase keterlembatan adalah 13,63%. Tujuan penelitian ini adalah untuk merancangan penerapan algoritma genetik yang dapat menghindari keterlambatan order yaitu untuk mengukur makespan produk dan merancang urutan penjadwalan mesin. Penyelesaian masalah penjadwakan dengan algoritma genetik. Algoritma genetik merupakan teknik search stochastic yang berdasarkan mekanisme seleksi alam dan genetika natural dengan melakukan proses inisialisasi awal lalu dicari nilai fitness dari setiap individu, yang akan menjadi induk adalah yang memiliki nilai fitness terbaik lalu dilakukan proses penyilangan dan mutasi dan pemilihan waktu optimal. Dari hasil perhitungan dengan menggunakan metode Algoritma Genetika diperoleh urutan penjadwalan mesin terbaik dan dengan nilai makespan terkecil.   PT. AAA from January to December received a total of 88 orders with the number of delays of 12 orders, the percentage of bridges was 13.63%. The purpose of this study is to design the application of a genetic algorithm that can avoid delay in order to measure product makespan and design the order of machine scheduling. Resolving scheduling problems with genetic algorithms. Genetic algorithm is a search stochastic technique that is based on the mechanism of natural selection and natural genetics by carrying out the initial initialization process and then looks for the fitness value of each individual, who will be the parent who has the best fitness value and then the process of crossing and mutation and optimal timing. From the results of calculations using the Genetic Algorithm method, the best sequence of machine scheduling is obtained and with the smallest makespan value.

Hybrid Flow-Shop Scheduling Method and Simulation Based on Adaptive Genetic Algorithm

2014 ◽  
Vol 670-671 ◽  
pp. 1434-1438
Author(s):  
Jian Feng Zhao ◽  
Xiao Chun Zhu ◽  
Bao Sheng Wang
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Adaptive Genetic Algorithm ◽  
Scheduling Problems ◽  
Mutation Operators ◽  
Hybrid Flow Shop Scheduling ◽  
Encoding Method

The n-job, k-stage hybrid flow shop problem is one of the general production scheduling problems. Hybrid flow shop (HFS) problems are NP-Hard when the objective is to minimize the makespan .The research deals with the criterion of makespan minimization for the HFS scheduling problems. In this paper we present a new encoding method so as to guarantee the validity of chromosomes and convenience of calculation and corresponding crossover and mutation operators are designed for optimum sequencing. The simulation results show that the Sequence Adaptive Cross Genetic Algorithm (SACGA) is an effective and efficient method for solving HFS Problems.

An Improved Genetic Algorithm of Web Services Composition with QOS

2012 ◽  
Vol 532-533 ◽  
pp. 1836-1840 ◽  
Author(s):  
Yan Yan Zhang ◽  
Hai Ling Xiong ◽  
Yong Chun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Web Services ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Experimental Result ◽  
Web Services Composition ◽  
Improved Genetic Algorithm ◽  
Search Range ◽  
Services Composition ◽  
Fitness Value

A web service composition method based on the adaptive genetic operator was proposed to deal with the issues of the lack of adaptability and the easy-premature phenomena in web services composition genetic algorithm. Adaptive crossover and mutation operator were designed according to the individual adaptability and evolution stage for enlarging local search range and increasing convergent speed. Moreover, use for reference the idea of taboo table in taboo search algorithm, we can inhibit the algorithm from converging to false optimal solution untimely; meanwhile, an evolution strategy was adopted to prevent the loss of composite service with high fitness value. The experimental result shows that better composite services can be gotten through the improved algorithm; moreover the convergence speed has also been improved.

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