Arbeitspapier
A comparison of logic and mixed-integer programming solvers for batch sequencing with sequence-dependent setups
A batch sequencing model with sequence-dependent setup-times and -costs is used to compare modelling and solving with two different general solvers. "Conceptual models" are implemented in the constraint propagation / logic programming language CHARME and solved with the PROLOG inference engine. The mixed-integer-programming (MIP) formulation of the same problem is solved with OSL, a state-of-the-art MIP solver. Modelling is easier in CHARME and computational results show, that the first approach outperforms the second one for instances with high capacity utilization.
- Sprache
-
Englisch
- Erschienen in
-
Series: Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel ; No. 322
- Klassifikation
-
Management
- Thema
-
Single Machine Batching/ Sequencing
Deadlines
Sequence-Dependent Setup-Times And -Costs
Logic/ Mixed-Integer Programming
Produktionssteuerung
Mathematische Optimierung
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Jordan, Carsten
Drexl, Andreas
- Ereignis
-
Veröffentlichung
- (wer)
-
Universität Kiel, Institut für Betriebswirtschaftslehre
ZBW – Leibniz Information Centre for Economics
- (wo)
-
Kiel
- (wann)
-
1993
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Jordan, Carsten
- Drexl, Andreas
- Universität Kiel, Institut für Betriebswirtschaftslehre
- ZBW – Leibniz Information Centre for Economics
Entstanden
- 1993
Ähnliche Objekte (12)
Configuring Mixed-Integer Programming Solvers for Large-Scale Instances
Exact mixed-integer programming
A mixed-integer approximation of robust optimization problems with mixed-integer adjustments
Vorlesung 05: Mixed-Integer Programme
Computational mixed-integer semidefinite programming
On Efficient Solution Methods for Mixed-Integer Nonlinear and Mixed-Integer Quadratic Optimization Problems
Mixed-integer programming for additive manufacturing
Reducing mixed-integer to zero-one programs
Mixed-integer programming subject to uncertain data
Convex relaxations for mixed-integer nonlinear programs
Tighter relaxations in mixed-integer nonlinear programming
Inner Parallel Sets in Mixed-Integer Optimization
Configuring Mixed-Integer Programming Solvers for Large-Scale Instances
Exact mixed-integer programming
A mixed-integer approximation of robust optimization problems with mixed-integer adjustments
Vorlesung 05: Mixed-Integer Programme
Computational mixed-integer semidefinite programming
On Efficient Solution Methods for Mixed-Integer Nonlinear and Mixed-Integer Quadratic Optimization Problems
Mixed-integer programming for additive manufacturing
Reducing mixed-integer to zero-one programs
Mixed-integer programming subject to uncertain data
Convex relaxations for mixed-integer nonlinear programs
Tighter relaxations in mixed-integer nonlinear programming
Inner Parallel Sets in Mixed-Integer Optimization
Configuring Mixed-Integer Programming Solvers for Large-Scale Instances
Exact mixed-integer programming
A mixed-integer approximation of robust optimization problems with mixed-integer adjustments
Vorlesung 05: Mixed-Integer Programme
Computational mixed-integer semidefinite programming
On Efficient Solution Methods for Mixed-Integer Nonlinear and Mixed-Integer Quadratic Optimization Problems
Mixed-integer programming for additive manufacturing
Reducing mixed-integer to zero-one programs
Mixed-integer programming subject to uncertain data
Convex relaxations for mixed-integer nonlinear programs
Tighter relaxations in mixed-integer nonlinear programming