Heuristic algorithms for the inverse mixed integer linear programming problem

The recent development in inverse optimization, in particular the extension from the inverse linear programming problem to the inverse mixed integer linear programming problem (InvMILP), provides more powerful modeling tools but also presents more challenges to the design of efficient solution techn...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of global optimization Jg. 51; H. 3; S. 463 - 471
Hauptverfasser: Duan, Zhaoyang, Wang, Lizhi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.11.2011
Springer Nature B.V
Schlagworte:
Algorithms
Computer Science
Design
Heuristic
Linear programming
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Parallel processing
Real Functions
Renewable resources
Studies
Subsidies
Tax credits
Parallel computing
Inverse optimization
Mixed integer linear programming
ISSN:0925-5001, 1573-2916
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The recent development in inverse optimization, in particular the extension from the inverse linear programming problem to the inverse mixed integer linear programming problem (InvMILP), provides more powerful modeling tools but also presents more challenges to the design of efficient solution techniques. The only reported InvMILP algorithm, referred to as Alg InvMILP , although finitely converging to global optimality, suffers two limitations that greatly restrict its applicability: it is time consuming and does not generate a feasible solution except for the optimal one. This paper presents heuristic algorithms that are designed to be implemented and executed in parallel with Alg InvMILP in order to alleviate and overcome its two limitations. Computational experiments show that implementing the heuristic algorithm on one auxiliary processor in parallel with Alg InvMILP on the main processor significantly improves its computational efficiency, in addition to providing a series of improving feasible upper bound solutions. The additional speedup of parallel implementation on two or more auxiliary processors appears to be incremental, but the upper bound can be improved much faster.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-010-9637-2