Hybrid Genetic Algorithm for Solving Job-Shop Scheduling Problem

Job Shop Scheduling Problem (JSSP) is an optimization problem in which ideal jobs are assigned to resources at particular times. In recent years many attempts have been made at the solution of this problem using a various range of tools and techniques. This paper presents hybrid genetic algorithm (HGA) for JSSP. The hybrid algorithm is a combination between genetic algorithm (GA) and local search. Firstly, a new initialization method is proposed. A modified crossover and mutation operators are used. Secondly, local search based on the neighborhood structure is applied in the GA result. Finally, the approach is tested on a set of standard instances taken from the literature. The computation results have validated the effectiveness of the proposed algorithm.

2001

European Journal of Operational Research, 2005

This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

International Journal of Advanced Intelligence Paradigms, 2020

Job shop scheduling problem is an NP-hard problem. This paper proposes a new hybrid genetic algorithm to solve the problem in an appropriate way. In this paper, a new selection criterion to tackle premature convergence problem is introduced. To make full use of the problem itself, a new crossover based on the machines is designed. Furthermore, a new local search is designed which can improve the local search ability of proposed GA. This new approach is run on the some problems and computer simulation shows the effectiveness of the proposed approach.

Computers & Industrial Engineering, 2003

This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

Tehnicki Vjesnik-technical Gazette, 2017

The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling combinatorial problems with considerable importance in industry. When solving complex problems, search based on traditional genetic algorithms has a major drawback - high requirement for computational power. The goal of this research was to develop fast and efficient scheduling method based on genetic algorithm for solving the job-shop scheduling problems. In proposed GA initial population is generated randomly, and the relevant crossover and mutation operation is also designed. This paper presents an efficient genetic algorithm for solving job-shop scheduling problems. Performance of the algorithm is demonstrated in the real-world examples.

The job shop scheduling problem is a well known practical planning problem in the manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this paper, a multi-population based hybrid genetic algorithm is developed for solving the JSSP. The population is divided in several groups at first and the hybrid algorithm is applied to the disjoint groups. Then the migration operator is used. The proposed approach, MP-HGA, have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 15 benchmark problems and compared results obtained with a number of algorithms established in the literature. The experimental results show that MP-HGA could gain the best known makespan in 13 out of 15 problems.

Journal of Software Engineering and Applications, 2010

Due to the NP-hardness of the job shop scheduling problem (JSP), many heuristic approaches have been proposed; among them is the genetic algorithm (GA). In the literature, there are eight different GA representations for the JSP; each one aims to provide subtle environment through which the GA's reproduction and mutation operators would succeed in finding near optimal solutions in small computational time. This paper provides a computational study to compare the performance of the GA under six different representations.

Mathematical Problems in Engineering

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

Proceedings of IEEE International Conference on Systems, Man and Cybernetics

Job-shop Scheduling Problem (JSP) is one of extremely hard problems because it requires very large combinatorial search space and the precedence constraint between machines. The traditional algorithm used t o solve the problem is the branch-and-bound method, which takes considerable computing time when the size of problem is large. W e propose a new method for solving JSP using Genetic Algorithm (G A) and demonstrate its efficiency by the standard benchmark of job-shop scheduling problems. Some important points of G A are how t o represent the schedules as an individuals and t o design the genetic operators for the representation in order t o produce better results.