scholarly journals Innovative Crossover and Mutation in a Genetic Algorithm Based Approach to a Campus Bus Driver Scheduling Problem with Break Consideration and Embedded Overtime

2013 ◽  
Vol 7 (5) ◽  
pp. 1921-1928 ◽  
Author(s):  
Razamin Ramli ◽  
Haslinda Ibrahim ◽  
Lim Tze Shung
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Problem ◽  
Driver Scheduling ◽  
Bus Driver ◽  
Crossover And Mutation

Solving a bus driver scheduling problem: A genetic algorithm approach

2013 ◽  
Author(s):  
Razamin Ramli ◽  
Haslinda Ibrahim ◽  
Tze Shung Lim
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Problem ◽  
University Campus ◽  
Campus Environment ◽  
Driver Scheduling ◽  
Transport Services ◽  
Bus Driver ◽  
Genetic Algorithm Approach ◽  
The University

Many transport companies face problems in regulating their transport services due to various challenges and issues. These problems affect the quality of the services provided especially in a university campus environment, where students heavily depend on the university transport services for their daily commuting.What are the problems faced by the management of the campus transport company? What are the issues raised by the drivers operating the on-campus buses?Hence, in assisting the management of the transport company the authors have identified the inefficiency of their bus driver scheduling system as one of the main problems, which needed to be tackled.For that reason, the authors developed an efficient bus driver scheduling model based on the Genetic Algorithm (GA) approach.The GA model is able to provide some resolutions and insight in relation to these inquiries: What are the constraints being considered in this bus driver scheduling problem?How were the drivers break times being distributed in this GA approach?How was the time taken to generate an efficient schedule?

scholarly journals A Two-Dimensional Genetic Algorithm and Its Application to Aircraft Scheduling Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ming-Wen Tsai ◽  
Tzung-Pei Hong ◽  
Woo-Tsong Lin
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Scheduling Problem ◽  
Two Dimensional ◽  
Dimensional Representation ◽  
One Dimensional ◽  
Time Table ◽  
Feasible Solutions ◽  
Crossover And Mutation ◽  
Airline Company

Genetic algorithms have become increasingly important for researchers in resolving difficult problems because they can provide feasible solutions in limited time. Using genetic algorithms to solve a problem involves first defining a representation that describes the problem states. Most previous studies have adopted one-dimensional representation. Some real problems are, however, naturally suitable to two-dimensional representation. Therefore, a two-dimensional encoding representation is designed and the traditional genetic algorithm is modified to fit the representation. Particularly, appropriate two-dimensional crossover and mutation operations are proposed to generate candidate chromosomes in the next generations. A two-dimensional repairing mechanism is also developed to adjust infeasible chromosomes to feasible ones. Finally, the proposed approach is used to solve the scheduling problem of assigning aircrafts to a time table in an airline company for demonstrating the effectiveness of the proposed genetic algorithm.

Multiobjective Metaheuristics for the Bus Driver Scheduling Problem

2001 ◽  
Vol 35 (3) ◽  
pp. 331-343 ◽  
Author(s):  
Helena R. Lourenço ◽  
José P. Paixão ◽  
Rita Portugal
Keyword(s):  
Scheduling Problem ◽  
Driver Scheduling ◽  
Bus Driver

The Research of Resource Scheduling Based on Genetic Algorithm

2012 ◽  
Vol 522 ◽  
pp. 799-803
Author(s):  
Zhi Ling Yuan ◽  
Yi Ping Yuan ◽  
Meng Yang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Simulation Analysis ◽  
Hot Spot ◽  
Low Frequency ◽  
Scheduling Problem ◽  
Np Hard Problem ◽  
Working Procedure ◽  
Crossover And Mutation

Job Shop scheduling problem, the essence of which is the resources scheduling problem, which has been proved to be a complete NP-hard problem. It has importantly realistic effect on further research, and has become a hot spot of research now. According to the practical Job Shop, as equipment resources are not unique, there are several machine tools with high frequency, while the number of that of low frequency is only one; the working procedure of processing components are also quite different, so we have put forward the Genetic Algorithm considering the sequence and, simultaneously, the machine choice. For reaching the shortest producing period, this method adopts Gemini string to encode, combining with the characteristics of the resources scheduling problem, and designs the unique way of Crossover and Mutation, meanwhile, it shows that the algorithm is effective through a specific example simulation analysis.

The Effect of Crossover and Mutation Operators on Genetic Algorithm for Job Shop Scheduling Problem

2012 ◽  
Vol 542-543 ◽  
pp. 1251-1259
Author(s):  
Long Xu ◽  
Wen Bin Hu
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Mutation Operators ◽  
Scheduling Plan ◽  
Crossover And Mutation

Job Shop Scheduling Problem (JSSP) is a famous NP-hard problem in scheduling field. The concentration of JSSP is to find a feasible scheduling plan to figure out the earliest completion time under machine and processing sequence constraints. At present, genetic algorithm has been widely adopted in varies of operation research problems including JSSP, and good performance have been achieved. However, few work have stress the selection of varies operators when implemented for JSSP. Using benchmark problems, this paper compares the effect of crossover and mutation operators on genetic algorithm for JSSP.

Research of an Improved Genetic Algorithm for Job Shop Scheduling

2014 ◽  
Vol 1078 ◽  
pp. 417-421
Author(s):  
Guo Hua Zhou
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Three Dimensional ◽  
Scheduling Problem ◽  
Np Hard ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Crossover And Mutation

job shop scheduling is one of the most difficult NP-hard combinatorial optimize problems, in order to solve this problem, an improved Genetic Algorithm with three- dimensional coded model was put forward in this paper. In this model, the gene was coded with 3-D space, and self-adapting plot was drawn into conventional GA, then the probability of crossover and mutation can automatic adjust by fit degree. The instance shows that this algorithmic is effective to solve job shop scheduling problem.

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