A Meta-Heuristics Approach for the Design and Scheduling of Multipurpose Batch Plants

Nelson Chibeles Martins

doi:10.1016/S1570-7946(10)28220-2

A Meta-Heuristics Approach for the Design and Scheduling of Multipurpose Batch Plants

Nelson Chibeles Martins

DM - Departamento de Matemática

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

The optimal design and scheduling of multipurpose batch plants is a complex task, since the design of the plant resources and the associated schedule should be undertaken together. Due to the nature and dimension of these problems, they often result into large Mixed Integer Linear Program (MILP) formulations that come associated with a high computational burden. In order to overcome this difficulty, a meta-heuristic approach, based on the Simulated Annealing (SA) methodology, is developed and tested along this paper. Sensitivity analysis is performed to the main meta-heuristic parameters. Several examples proposed by Pinto et al.(2008) are solved using the SA and the results are compared to an exact optimization approach.

Original language	Unknown
Title of host publication	Computer-Aided Chemical Engineering
Editors	S Pierucci, B Ferraris
Place of Publication	SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
Publisher	ELSEVIER SCIENCE BV
Pages	1315-1320
Volume	28
ISBN (Print)	978-0-444-53569-6
DOIs	https://doi.org/10.1016/S1570-7946(10)28220-2
Publication status	Published - 1 Jan 2010
Event	ESCAPE20 - Duration: 1 Jan 2010 → …

Conference

Conference	ESCAPE20
Period	1/01/10 → …

Access to Document

10.1016/S1570-7946(10)28220-2

Cite this

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title = "A Meta-Heuristics Approach for the Design and Scheduling of Multipurpose Batch Plants",

abstract = "The optimal design and scheduling of multipurpose batch plants is a complex task, since the design of the plant resources and the associated schedule should be undertaken together. Due to the nature and dimension of these problems, they often result into large Mixed Integer Linear Program (MILP) formulations that come associated with a high computational burden. In order to overcome this difficulty, a meta-heuristic approach, based on the Simulated Annealing (SA) methodology, is developed and tested along this paper. Sensitivity analysis is performed to the main meta-heuristic parameters. Several examples proposed by Pinto et al.(2008) are solved using the SA and the results are compared to an exact optimization approach.",

keywords = "Simulated Annealing, Design and Scheduling, Batch Plants, MILP",

author = "{Chibeles Martins}, Nelson",

note = "20th European Symposium on Computer Aided Process Engineering (ESCAPE), Ischia, ITALY, JUN 06-09, 2010; ESCAPE20 ; Conference date: 01-01-2010",

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A Meta-Heuristics Approach for the Design and Scheduling of Multipurpose Batch Plants. / Chibeles Martins, Nelson.
Computer-Aided Chemical Engineering. ed. / S Pierucci; B Ferraris. Vol. 28 SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS: ELSEVIER SCIENCE BV, 2010. p. 1315-1320.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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N2 - The optimal design and scheduling of multipurpose batch plants is a complex task, since the design of the plant resources and the associated schedule should be undertaken together. Due to the nature and dimension of these problems, they often result into large Mixed Integer Linear Program (MILP) formulations that come associated with a high computational burden. In order to overcome this difficulty, a meta-heuristic approach, based on the Simulated Annealing (SA) methodology, is developed and tested along this paper. Sensitivity analysis is performed to the main meta-heuristic parameters. Several examples proposed by Pinto et al.(2008) are solved using the SA and the results are compared to an exact optimization approach.

AB - The optimal design and scheduling of multipurpose batch plants is a complex task, since the design of the plant resources and the associated schedule should be undertaken together. Due to the nature and dimension of these problems, they often result into large Mixed Integer Linear Program (MILP) formulations that come associated with a high computational burden. In order to overcome this difficulty, a meta-heuristic approach, based on the Simulated Annealing (SA) methodology, is developed and tested along this paper. Sensitivity analysis is performed to the main meta-heuristic parameters. Several examples proposed by Pinto et al.(2008) are solved using the SA and the results are compared to an exact optimization approach.

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