Abstract
In recent years, fuzzy job shop scheduling problems (FJSSP) with fuzzy triangular processing time and fuzzy due date have received an increasing interests because of its flexibility and similarity with practical problems. The objective of FJSSP is to maximize the minimal average customer’s degree of satisfaction. In this paper, a novel adaptive immune-genetic algorithm (CAGA) is proposed to solve FJSSP. CAGA manipulates a number of individuals to involve the progresses of clonal proliferation, adaptive genetic mutations and clone selection. The main characteristic of CAGA is the usage of clone proliferation to generate more clones for fitter individuals which undergo the adaptive genetic mutations, thus leading a fast convergence. Moreover, the encoding scheme of CAGA is also properly adapted for FJSSP. Simulation results based on several instances verify the effectiveness of CAGA in terms of search capacity and convergence performance.
| Original language | English |
|---|---|
| Pages (from-to) | 246-257 |
| Number of pages | 12 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 8794 |
| DOIs | |
| State | Published - 2014 |
Keywords
- Adaptive genetic algorithm
- Clonal algorithm
- Fuzzy due date
- Fuzzy processing time
- Job shop scheduling
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science