Modified Genetic Algorithm for Job-Shop Scheduling: a Gap-Utilization Technique (original) (raw)

Applying Improved Genetic Algorithm for Solving Job Shop Scheduling Problems

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.

Genetic Algorithm for Job Shop Scheduling Problem: A Case Study

The job-shop scheduling (JSS) is a schedule planning for low volume systems with many variations in requirements. In job-shop scheduling problem (JSSP), there are k operations and n jobs to be processed on m machines with a certain objective function to be minimized. Due to complexity of transferring work in process product, this research add transfer time variable from one machine to another for each different operation. Performance measures are mean flow time and make span. In this paper we used genetic algorithm (GA) with some modifications to deal with problem of job shop scheduling. The result than is compared with dispatching rules such as longest processing time, shortest processing time and first come first serve. The numerical example showed that GA result can outperform the other three methods.

Hybrid Genetic Algorithm for Solving Job-Shop Scheduling Problem

2007

Abstract—The Job-Shop Scheduling Problem (JSSP) is a well-known difficult combinatorial optimization problem. Many algorithms have been proposed for solving JSSP in the last few decades, including algorithms based on evolutionary techniques. However, there is room for improvement in ...

Improved genetic algorithm for the job-shop scheduling problem

The International Journal of Advanced Manufacturing Technology, 2006

In this paper, an improved genetic algorithm, called the hybrid Taguchi-genetic algorithm (HTGA), is proposed to solve the job-shop scheduling problem (JSP). The HTGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimal offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to systematically select the better genes to achieve crossover, and consequently enhance the genetic algorithm. Therefore, the proposed HTGA approach possesses the merits of global exploration and robustness. The proposed HTGA approach is effectively applied to solve the famous Fisher-Thompson benchmarks of 10 jobs to 10 machines and 20 jobs to 5 machines for the JSP. In these studied problems, there are numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed GA-based approaches. The computational experiments show that the proposed HTGA approach can obtain both better and more robust results than other GA-based methods reported recently.

Representations in Genetic Algorithm for the Job Shop Scheduling Problem: A Computational Study

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.

Solving job-shop scheduling problems by genetic algorithm

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.

A Genetic Algorithm with Priority Rules for Solving Job-Shop Scheduling Problems

Studies in Computational Intelligence, 2009

The Job-Shop Scheduling Problem (JSSP) is one of the most difficult NPhard combinatorial optimization problems. In this chapter, we consider JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. First, we develop a genetic algorithm (GA) based approach for solving JSSPs. We then introduce a number of priority rules to improve the performance of GA, such as partial re-ordering, gap reduction, and restricted swapping. The addition of these rules results in a new hybrid GA algorithm that is clearly superior to other wellknown algorithms appearing in the literature. Results show that this new algorithm obtained optimal solutions for 27 out of 40 benchmark problems. It thus makes a significantly new contribution to the research into solving JSSPs.

A genetic algorithm for the Flexible Job-shop Scheduling Problem

Computers & Operations Research, 2008

Flexible Job Shop scheduling problem (FJSSP) is an important scheduling problem which has received considerable importance in the manufacturing domain. In this paper a genetic algorithm (GA) based scheduler is presented for flexible job shop problem to minimise makespan. The proposed approach implements a domain independent GA to solve this important class of problem. The scheduler is implemented in Microsoft Excel™ spreadsheet. The shop model is developed in the spreadsheet using the built in functions. Benchmark problems from the literature have been used to compare the performance of the proposed approach.

A new hybrid genetic algorithm for job shop scheduling problem

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.

A HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEDULING PROBLEMS

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.