scholarly journals Genetic Algorithm for Exam Timetabling Problem - A Specific Case for Japanese University Final Presentation Timetabling

2020 ◽  
Author(s):  
Jiawei LI ◽  
Tad Gonsalves
Keyword(s):  
Genetic Algorithm ◽  
Real Data ◽  
Initial Population ◽  
Exam Timetabling ◽  
Timetabling Problem ◽  
Training Approach ◽  
Chromosome Representation ◽  
Genetic Algorithm Approach ◽  
Hard And Soft Constraints ◽  
Microsoft Office

This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which can be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results, and the comparison of results proves that the initial population pre-training approach can improve the result quality.

scholarly journals Efficient Scheduling of Plantation Company Workers using Genetic Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 60
Author(s):  
Wayan Firdaus Mahmudy ◽  
Andreas Pardede ◽  
Agus Wahyu Widodo ◽  
Muh Arif Rahman
Keyword(s):  
Genetic Algorithm ◽  
Physical Health ◽  
Selection Method ◽  
Computational Experiments ◽  
Cut Point ◽  
Chromosome Representation ◽  
Plantation Company ◽  
Reciprocal Mutation ◽  
Best Parameters ◽  
Work Scheduling

Workers at large plantation companies have various activities. These activities include caring for plants, regularly applying fertilizers according to schedule, and crop harvesting activities. The density of worker activities must be balanced with efficient and fair work scheduling. A good schedule will minimize worker dissatisfaction while also maintaining their physical health. This study aims to optimize workers' schedules using a genetic algorithm. An efficient chromosome representation is designed to produce a good schedule in a reasonable amount of time. The mutation method is used in combination with reciprocal mutation and exchange mutation, while the type of crossover used is one cut point, and the selection method is elitism selection. A set of computational experiments is carried out to determine the best parameters’ value of the genetic algorithm. The final result is a better 30 days worker schedule compare to the previous schedule that was produced manually. 

Using genetic algorithms to evolve type-2 fuzzy logic systems for predicting bankruptcy

Kybernetes ◽  
2017 ◽  
Vol 46 (1) ◽  
pp. 142-156 ◽  
Author(s):  
Vasile Georgescu
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Degrees Of Freedom ◽  
Bankruptcy Prediction ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Generalization Capability ◽  
Content Type ◽  
Chromosome Representation

Purpose Type-2 fuzzy sets became attractive in practice because of their footprint of uncertainty that gives them more degrees of freedom. This paper aims to use genetic algorithms (GAs) to design an interval Type-2 fuzzy logic system (IT2FLS) for the purpose of predicting bankruptcy. Design/methodology/approach The shape of type-2 membership functions, the parameters giving their spread and location in the fuzzy partitions and the set of fuzzy rules are evolved at the same time by encoding all together into the chromosome representation. The enhanced Karnik–Mendel algorithms are used for the centroid type-reduction and defuzzification stage. The performance in predicting bankruptcy is evaluated by benchmarking IT2FLSs against type-1 FLSs. The experimental setup consists of evolving 100 configurations for both the T1FLS and IT2FLS and comparing their in-sample and out-of-sample average accuracy. Findings The experiments confirm that representing and capturing uncertainty with more degrees of freedom is an important advantage. It is this extra potential of IT2FLSs that allows them to outperform T1FLS, especially in terms of generalization capability. Originality/value The strategy followed in this paper is to train an IT2FLS from scratch rather than tuning the parameters of an existing T1FLS. Because this leads to solving a mixed integer optimization problem, the GA-based approach is specifically designed and uses genetic operators that are most suited for such a case: tournament selection, extended Laplace crossover and power mutation. Finally, the trained IT2FLS is applied to bankruptcy prediction, and its generalization capability is compared with related techniques.

scholarly journals Genetic optimization–based scheduling in maritime cyber physical systems

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771771716 ◽  
Author(s):  
Tingting Yang ◽  
Hailong Feng ◽  
Jian Zhao ◽  
Ruilong Deng ◽  
Ying Wang ◽  
...  
Keyword(s):  
Scheduling Algorithms ◽  
Cyber Physical Systems ◽  
Mapping Method ◽  
Video Data ◽  
Release Time ◽  
Improved Genetic Algorithm ◽  
Physical Systems ◽  
Video Packet ◽  
Chromosome Representation ◽  
The Time Domain

In this article, we attempt to solve the issue of optimal scheduling for vessels monitoring video data uploading in maritime cyber physical systems, during the period of sailing from the origin port to destination port. We consider the terrestrial infrastructure-based networking, delivering video packets assisted by the shoreside infostations to the authorities on land. Each video packet has respective elements (i.e. release time, deadline, processing time, and weight) to describe, in which deadline is utilized to demonstrate the time domain limitation before that to upload it successfully. In order to cope with the computation complexity of traditional scheduling algorithms in intermittent infostations scenario, time-capacity mapping method is exploited to transfer it to a continue scenario when classic scheduling algorithms could be utilized with lower time complexity. An ingenious mathematic job-machine scheduling formulation is indicated with the goal of minimizing the total penalties of tardiness of uploaded video packets, taking into account the tardiness and the weights of jobs simultaneously. A genetic based algorithm, as well as an improved genetic algorithm–based optimization scheme, is proposed to target this optimization formulation. Specially, the genetic based algorithm as well as the improved genetic based algorithm are described in detail, including a novel chromosome representation, a heuristic initialization procedure, as well as a modified crossover and mutation process. The effectiveness of the proposed schemes is verified by the simulation results.

An optimization model for workgroup-based repetitive scheduling

2006 ◽  
Vol 33 (9) ◽  
pp. 1172-1194 ◽  
Author(s):  
Rong-yau Huang ◽  
Kuo-Shun Sun
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Construction Projects ◽  
Optimal Solution ◽  
Planning And Scheduling ◽  
Construction Scheduling ◽  
Chromosome Representation ◽  
Scheduling Method ◽  
Model Formation ◽  
System Project

Most construction repetitive scheduling methods developed so far have been based on the premise that a repetitive project is comprised of many identical production units. Recently, Huang and Sun (2005) developed a workgroup-based repetitive scheduling method that takes the view that a repetitive construction project consists of repetitive activities of workgroups. Instead of repetitive production units, workgroups with repetitive or similar activities in a repetitive project are identified and employed in the planning and scheduling. The workgroup-based approach adds more flexibility to the planning and scheduling of repetitive construction projects and enhances the effectiveness of repetitive scheduling. This work builds on previous research and develops an optimization model for workgroup-based repetitive scheduling. A genetic algorithm (GA) is employed in model formation for finding the optimal or near-optimal solution. A chromosome representation, as well as specification of other parameters for GA analysis, is described in the paper. Two sample case studies, one simple and one sewer system project, are used for model validation and demonstration. Results and findings are reported.Key words: construction scheduling, repetitive project, workgroup, optimization, genetic algorithm.

Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part I: Modelling

2013 ◽  
Vol 701 ◽  
pp. 359-363 ◽  
Author(s):  
Wayan Firdaus Mahmudy ◽  
Romeo M. Marian ◽  
Lee H.S. Luong
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Combinatorial Problem ◽  
Search Space ◽  
Flexible Job Shop ◽  
The Novel ◽  
Test Bed ◽  
Genetic Operators ◽  
Chromosome Representation ◽  
Feasible Solutions

This paper and its companion (Part 2) deal with modelling and optimization of the flexible job-shop problem (FJSP). The FJSP is a generalised form of the classical job-shop problem (JSP) which allows an operation to be processed on several alternatives machines. To solve this NP-hard combinatorial problem, this paper proposes a customised Genetic Algorithm (GA) which uses an array of real numbers as chromosome representation so the proposed GA is called a real-coded GA (RCGA). The novel chromosome representation is designed to produces only feasible solutions which can be used to effectively explore the feasible search space. This first part of the papers focuses on the modelling of the problems and discusses how the novel chromosome representation can be decoded into a feasible solution. The second part will discuss genetic operators and the effectiveness of the RCGA to solve various test bed problems from literature.

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