Cinus, Marco and Confalonieri, Matteo and Barni, Andrea Francesco and Valente, Anna (2016) An ANN based decision support system fostering production plan optimization through preventive maintenance management. Smart Innovation, Systems and Technologies, 54. pp. 447-455. ISSN 2190-3018
Full text not available from this repository.Abstract
Besides pursuing the economic goals of low costs and high profits, companies are becoming more and more aware of the environmental and social impact of their actions. Companies striving for the integrated optimization of environmental and economic perspectives within their production processes, need to be supported by tools helping to understand the effects of the decision making process. In this context, this paper describes the Artificial Intelligence developed for a Decision Support System (DSS) which enables the early identification of problems occurring on manufacturing. The decision making process beneath the DSS starts from the aggregation of production lines sensors data in Key Performance Indicators (KPI). The data are then processed by means of an Artificial Neural Networks (ANN) based knowledge system which enables to suggest preventive maintenance interventions. The proposed maintenance activities, elaborated throughout a scheduling engine, are integrated within the weekly production schedule, according to the selected optimization policy.
| Item Type: | Scientific journal article, Newspaper article or Magazine article |
|---|---|
| Uncontrolled Keywords: | Decision support system, Production line, Preventive maintenance |
| Subjects: | Thechnologies Thechnologies > Others in technology Thechnologies > Others in technology > Machinery maintenance Thechnologies > Others in technology > Machinery maintenance > Industrial machinery maintenance |
| Department/unit: | Dipartimento tecnologie innovative > Istituto sistemi e tecnologie per la produzione sostenibile |
| Depositing User: | Andrea Francesco Barni |
| Date Deposited: | 14 Mar 2018 07:20 |
| Last Modified: | 14 Mar 2018 07:20 |
| URI: | http://repository.supsi.ch/id/eprint/8475 |
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