This paper presents a new genetic algorithm for job-shop scheduling problems. When we design a genetic algorithm for difficult ordering problems such as job-shop scheduling problems, it is important to design encoding/crossover that is excel lent in characteristic preservation. We regard a sub-sequence on each machine as a characteristic to be preserved between parents and their children. The proposed method uses a job sequence for encoding. This paper proposes a new crossover, the sub-sequence exchange crossover (SXX), that can preserve the characteristic very well. Since the children generated by SXX are not always feasible, we propose a technique to transform them into active schedules by using the GT method with a few modifications. Maintaining a diversity of population is important for preventing premature convergence. We present a muta tion based on the shift change operator for efficiently introducing a diversity. Furthermore, we design a generation alterna tion model that is excellent in diversity maintaining. By applying the proposed method to Fisher's and Thompson's 10x10 and 20x5 problems, we show its effectiveness.
[1]
Darrell Whitley,et al.
Scheduling problems and traveling salesman: the genetic edge recombination
,
1989
.
[2]
Lawrence Davis,et al.
Job Shop Scheduling with Genetic Algorithms
,
1985,
ICGA.
[3]
Takeshi Yamada,et al.
Conventional Genetic Algorithm for Job Shop Problems
,
1991,
ICGA.
[4]
Christian Bierwirth,et al.
Control of Parallel Population Dynamics by Social-Like Behavior of GA-Individuals
,
1994,
PPSN.
[5]
Takeshi Yamada,et al.
The ECOlogical Framework II : Improving GA Performance At Virtually Zero Cost
,
1993,
ICGA.
[6]
Hiroaki Satoh,et al.
Minimal generation gap model for GAs considering both exploration and exploitation
,
1996
.
[7]
G. Thompson,et al.
Algorithms for Solving Production-Scheduling Problems
,
1960
.
[8]
Takeshi Yamada,et al.
A genetic algorithm with multi-step crossover for job-shop scheduling problems
,
1995
.