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Erschienen in: Soft Computing 13/2017

21.12.2015 | Methodologies and Application

Facing energy-aware scheduling: a multi-objective extension of a scheduling support system for improving energy efficiency in a moulding industry

verfasst von: Massimo Paolucci, Davide Anghinolfi, Flavio Tonelli

Erschienen in: Soft Computing | Ausgabe 13/2017

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Abstract

Nowadays most industries do not integrate product, process and energy data. Costs due to energy consumption are often considered externalities and energy efficiency is not deemed a relevant performance criterion. In energy-intensive processes, as injection moulding, the specific energy consumption, embedded inside the same products, depends on the machine–product combinations. Multi-objective scheduling, including the energy data acquired from shop floor and allocation criteria, is a valuable approach to improve energy efficiency. This paper presents the extension of a commercial detailed scheduling support system developed within a regional Italian project aiming at providing tools to manufacturing industry for improving energy efficiency. The project designed a monitoring system developed by instrumenting injection moulding presses to acquire the energy consumption for each product–machine combination. The commercial scheduling system was extended by implementing a multi-objective metaheuristic scheduling approach. The experimental assessment of the proposed approach involved a major producer of plastic dispensers. The extended algorithm simultaneously optimizes the total weighted tardiness, the total setup and the energy consumption costs. The obtained results, produced for a real test case and a set of random generated instances, show the effectiveness of the proposed approach.

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Metadaten
Titel
Facing energy-aware scheduling: a multi-objective extension of a scheduling support system for improving energy efficiency in a moulding industry
verfasst von
Massimo Paolucci
Davide Anghinolfi
Flavio Tonelli
Publikationsdatum
21.12.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 13/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-1987-8

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