An experienced learning genetic algorithm to solve the single machine total weighted tardiness scheduling problem

Authors:
Fuh-Der Chou
Affiliations:
Department of Industrial Engineering and Management, Ching Yun University, Jung-Li, Tao Yuan, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 8
Cited 3

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
A performance comparison of heuristics for the total weighted tardiness problem

Computers and Industrial Engineering - Special issue: new advances in analysis of manufacturing systems
A new lower bounding scheme for the total weighted tardiness problem

Computers and Operations Research
A genetic algorithm to minimize maximum lateness on a batch processing machine

Computers and Operations Research
Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem

INFORMS Journal on Computing
An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem

INFORMS Journal on Computing
A Weighted Modified Due Date Rule for Sequencing to Minimize Weighted Tardiness

Journal of Scheduling
An enhanced dynasearch neighborhood for the single-machine total weighted tardiness scheduling problem

Operations Research Letters

A single-machine bi-criterion learning scheduling problem with release times

Expert Systems with Applications: An International Journal
Genetic algorithm for minimizing the total weighted completion time scheduling problem with learning and release times

Computers and Operations Research
Solving a two-agent single-machine scheduling problem considering learning effect

Computers and Operations Research

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Abstract

In this paper, an experienced learning genetic algorithm (ELGA) is presented in an attempt to solve the single machine total weighted tardiness problem. In the proposed ELGA, a position-job and a job-job matrix, which can be updated over generations by using the exponential smoothing method, are used to build the relationships between jobs and positions according to information on the genes of chromosomes in the generation. Based on the dynamic matrices, an experienced learning (EL) heuristic is developed to produce some potential chromosomes for the GA. In order to evaluate the performance of the ELGA, the solutions obtained by the ELGA were compared with the best known solutions, which appeared on J.E. Beasley's OR-Library Web site. The computational results showed that the ELGA can obtain the best known solutions in a short time. Moreover, the ELGA is robust because one of the performance measures, the standard deviations of the percentage of relative difference in the solutions, is extremely smaller for all experimental runs.