Abstract
Job shop scheduling problem (JSP) is a strongly NP-hard combinatorial optimization problem. It is difficult to solve the problem to the optimum in a reasonable time. Teaching-learning-based optimization (TLBO) algorithm is a novel population oriented meta-heuristic algorithm. It has been proved that TLBO has a considerable potential when compared to the best-known heuristic algorithms for scheduling problems. In this paper, the traditional TLBO is improved to enhance diversification and intensification when exploring solutions for JSP. The improvements include changing the coding method, increasing number of teachers, introducing new learners and performing local search around potentially optimal solutions. To show effectiveness of the improved TLBO algorithm, the simulation results obtained by the improved TLBO for benchmark problems are compared with results obtained by the traditional TLBO and the best known lower bounds.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 797-801 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509055074 |
| DOIs | |
| Publication status | Published - 2017 Jun 27 |
| Event | 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017 - Wuhan, China Duration: 2017 May 24 → 2017 May 26 |
Other
| Other | 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017 |
|---|---|
| Country | China |
| City | Wuhan |
| Period | 17/5/24 → 17/5/26 |
Keywords
- Job shop scheduling
- Optimization
- Teaching-learning-based optimization algorithm
ASJC Scopus subject areas
- Management Information Systems
- Information Systems
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
- Information Systems and Management
- Modelling and Simulation