Giancarlo Dalle Ave

Energy- and Equipment-Condition-Aware Online Scheduling Methods for the Process Industries


Front	Back

ISBN:

978-3-8440-8254-8

Series:

Schriftenreihe des Lehrstuhls für Systemdynamik und Prozessführung
Herausgeber: Prof. Dr.-Ing. Sebastian Engell
Dortmund

Volume:

2022,2

Keywords:

Optimization; Scheduling; Demand Side Management

Type of publication:

Thesis

Language:

English

Pages:

208 pages

Figures:

69 figures

Weight:

309 g

Format:

21 x 14,8 cm

Bindung:

Paperback

Price:

49,80 € / 62,30 SFr

Published:

June 2022

Buy:

Download:

Available PDF-Files for this title:

You need the Adobe Reader, to open the files. Here you get help and information, for the download.

These files are not printable.


	Document		Document
	Type		PDF
	Costs		37,35 EUR
	Action		Purchase in obligation and display of file - 38,4 MB (40260054 Byte)
	Action		Purchase in obligation and download of file - 38,4 MB (40260054 Byte)


	Document		Table of contents
	Type		PDF
	Costs		free
	Action		Display of file - 2,5 MB (2590138 Byte)
	Action		Download of file - 2,5 MB (2590138 Byte)

User settings for registered users

You can change your address here or download your paid documents again.

User:	Not logged in.
Actions:	Login / Register Forgotten your password?

Recommendation:

You want to recommend this title?

Review copy:

Here you can order a review copy.

Link:

You want to link this page? Click here.

Export citations:

Text
BibTex
RIS

Abstract:

This work investigates aspects of online scheduling with an emphasis placed on industrial requirements. Two areas that are becoming increasingly important in industry are energy management (via demand side management) and condition-based maintenance. This work investigates the challenges of online production scheduling considering both of these production concerns. This work builds upon the Resource Task Network (RTN) scheduling framework (Pantelides, 1994) as it is easily adaptable to the ISA-95 industrial production scheduling standard (ANSI/ISA-95.00.03-2005, 2005). Results show that the proposed algorithms and models can address shortcomings of current methods and represent a step forward in online scheduling.