Name
Abstract | ||
|---|---|---|
Assembly Line Balance (ALB) problem is a typical combinatorial optimization problem where pieces of work are transported between the work stations. In the ALB problem, the ultimate goal is to seek the optimal makespan. It is a very difficult problem to solve particularly in Sewn Product Industry (SPI) which is a labor-intensive manufacturing industry. In order to achieve the optimal makespan, it is necessary to take into account factors such as the efficiency of each machinist, the allocation of suitable Work In Progress (WIP) into each assembly line, the calculation of each product production time in terms of Sewing Minute Value (SMV) and the assignment of each machinist into different work stations according to his/her capability. However, the current methodologies are dependent on human experts relying on statistical data. These data, however, are problematic in that they are historical data and as such are unlikely to be suitable for all circumstances especially as in a highly competitive industry such as the SPI practices, standards and tasks are constantly changing and adapting. In this paper, two models have been proposed to solve the WIP allocation problem and the SMV calculation problem. The preliminary results are encouraging. The first model is able to extract a large number of the rules and has attained a prediction accuracy of 93%. The second model can increase 11% in accuracy in predicting the SMV compared to the current widely used General Sewing Data (GSD) method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s10845-010-0379-2 | J. Intelligent Manufacturing |
Keywords | Field | DocType |
Assembly line balance,Artificial neural networks,Case base reasoning,Master production schedule,Assembly lines balance and job shop scheduling | Mathematical optimization,Job shop scheduling,Manufacturing,Combinatorial optimization problem,Work in process,Computer science,Operations research,Artificial intelligence,Master production schedule,Artificial neural network,Case-based reasoning,Machine learning | Journal |
Volume | Issue | ISSN |
23 | 3 | 0956-5515 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucas K. C. Lai | 1 | 2 | 1.00 |
| James N. K. Liu | 2 | 529 | 44.35 |