Abstract
Nowadays, shop floor managers are facing numerous unpredictable risks in the actual manufacturing environment. These unpredictable risks not only involve stringent requirements regarding the replenishment of materials but also increase the difficulty in preparing material stock. In this paper, a real-time production operations decision support system (RPODS) is proposed for solving stochastic production material demand problems. Based on Poon et al. (2009), three additional tests are proposed to evaluate RFID reading performance. Besides, by using RPODS, the real-time status of production and warehouse operations are monitored by Radio Frequency Identification (RFID) technology, and a genetic algorithm (GA) technique is applied to formulate feasible solutions for tackling these stochastic production demand problems. The capability of the RPODS is demonstrated in a mould manufacturing company. Through the case study, the objectives of reducing the effect of stochastic production demand problems and enhancing productivity both on the shop floor and in the warehouse are achieved.
| Original language | English |
|---|---|
| Pages (from-to) | 4829-4838 |
| Number of pages | 10 |
| Journal | Expert Systems with Applications |
| Volume | 38 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 1 May 2011 |
Keywords
- RFID
- Unpredictable risks
ASJC Scopus subject areas
- General Engineering
- Computer Science Applications
- Artificial Intelligence