Performance improvement of Teaching-Learning-Based Optimisation for robust machine layout design
Autor: | Pupong Pongcharoen, Srisatja Vitayasak |
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Rok vydání: | 2018 |
Předmět: |
0209 industrial biotechnology
Page layout Computer science General Engineering 02 engineering and technology Benchmarking computer.software_genre Industrial engineering Computer Science Applications Material flow 020901 industrial engineering & automation Artificial Intelligence Dynamic demand 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Performance improvement computer Metaheuristic Selection (genetic algorithm) |
Zdroj: | Expert Systems with Applications. 98:129-152 |
ISSN: | 0957-4174 |
DOI: | 10.1016/j.eswa.2018.01.005 |
Popis: | Teaching-Learning-Based Optimisation (TLBO) is one of the more recently developed metaheuristics and has been successfully applied to solve various optimisation problems. However, TLBO has not been academically reported for solving the robust machine layout design (MLD) problems with dynamic demand. Considering internal logistics activities, shortening material flow distance within a manufacturing area can lead to efficient productivity and a decrease in related costs. The robust machine layout is concerned with determining the efficient arrangement of machines/facilities located on a manufacturing shop floor under future demand fluctuation. A robust designed layout is essential for a company to maintain a high productivity rate through multiple time-periods of demand uncertainty with minimum effects related to the re-layout time and cost, manufacturing disruption, and the movement of monument machines. The objectives of this paper were to: i) describe the development of a computer aided layout designing tool for minimising the total material flow distance under dynamic demand scenario, ii) investigate the appropriate setting of TLBO parameters, and iii) propose four TLBO modifications for improving its performance. The modified TLBOs were inspired by multiple teachers with two types of classes and two approaches to teacher selection. The numerical experiments were designed and conducted using eleven MLD benchmarking datasets. Statistical analyses on the experimental results showed a superior performance for the proposed modifications. |
Databáze: | OpenAIRE |
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