Library

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • 2010-2014  (6)
Years
Year
Language
  • 1
    Publication Date: 2020-08-05
    Description: Die vorliegende Arbeit befasst sich mit Primalheuristiken für gemischt-ganzzahlige, lineare Optimierungsprobleme (engl.: mixed integer program MIP). Zahlreiche Optimierungsprobleme aus der Praxis lassen sich als MIP modellieren, Beispiele hierfür sind u. a. Optimierungsprobleme im öffentlichen Nah- und Fernverkehr, bei logistischen Fragestellungen oder im Bereich der Chip-Verifikation. Das Lösen von MIP ist NP-schwer und wird heutzutage meistens mit Hilfe von Branch-and-Bound-basierenden Algorithmen versucht. Das Branch-and-Bound-Ver\-fah\-ren profitiert unter Umständen von bereits frühzeitig zur Verfügung stehenden Lösungen, daher sind wir sehr an heuristischen Verfahren interessiert, die in der Praxis schnell eine gute Lösung für eine große Zahl an MIPs liefern und somit die Lösezeit des Branch-and-Bound-Verfahrens erheblich beschleunigen können. Primalheuristiken sind Suchverfahren zum Auffinden zulässiger Lösungen eines MIP. Verschiedene Typen von Primalheuristiken sollen dabei den jeweiligen Bedarf des Anwenders zu unterschiedlichen Zeiten während der Branch-and-Bound-Suche decken. Während Start- und Rundeheuristiken zu Beginn des Löseprozesses eine große Rolle bei der Suche nach der ersten zulässigen Lösung haben, arbeiten Verbesserungs-heuristiken auf schon bekannten Lösungen, um neue, bessere Lösungen zu produzieren. Diese Arbeit beschäftigt sich mit Primalheuristiken, welche Teil des MIP-Lösers SCIP sind. Im ersten Kapitel werden nach der Erarbeitung grundlegender Definitionen viele der durch Tobias Achterberg und Timo Berthold in SCIP integrierten heuristischen Verfahren vorgestellt und kategorisiert. Auf dieser Grundlage bauen dann die Kapitel 2-4 der Arbeit auf. In diesen werden drei zusätzliche Heuristiken vorgestellt, im Einzelnen sind dies ZI Round, eine Rundeheuristik, welche zuerst von Wallace beschrieben wurde, außerdem eine 2-Opt-Heuristik für MIP und eine neue Startheuristik, Shift-And-Propagate. Großer Wert wird in jedem Kapitel auf die algorithmische Beschreibung der Heuristiken gelegt, die stets anhand von motivierenden Beispielen eingeführt und anhand von Pseudocode-Algorithmen begleitet werden. Zusätzlich enthält jedes Kapitel Auswertungen der mit den neuen Heuristiken gemessenen Ergebnisse von SCIP. Eine kurze Zusammenfassung in Kapitel 5 schließt diese Arbeit ab.
    Description: Many practically relevant problems can be formulated in terms of a mixed integer programming (MIP) model. MIP denotes the optimization of a linear objective function under a certain number of linear side constraints including the need for some of the involved variables to take integral solution values. Applications of MIP based optimization can be found in the area of public transit, scheduling, automatic vehicle routing, network design, etc. From a complexity point of view, MIP solving is known to be NP-hard and most commonly tried to be solved via Branch-and-Bound based algorithms. Branch-and-Bound algorithms benefit from early and good feasible solutions of a MIP in various ways. Primal heuristics are aimed at finding new solutions during the MIP solving process. There are different types of primal heuristics: while start heuristics are particularly valuable to find an early solution, improvement heuristics hopefully drive a given solution further towards optimality. This thesis focusses on primal heuristics which are part of the MIP-solving framework SCIP. The first chapter comes with basic definitions and a brief description of SCIP and the test set which we used. The remainder of the first chapter is an overview of the existing heuristics in SCIP which have been implemented by Achterberg and Berthold. In the following chapters we introduce three new heuristics which apply rounding or propagation techniques for their specific purpose, namely the new rounding heuristic ZI Round, taken from Wallace, a 2-Opt improvement heuristic for MIP and the propagation heuristic Shift-and-Propagate. It is characteristic of all three heuristics that they mainly apply computationally inexpensive algorithms. Each of them is presented in an own chapter, starting with an algorithmic description, followed by implementational details. All chapters close with a discussion of the computational results obtained with the respective implementations in SCIP.
    Language: English
    Type: bachelorthesis , doc-type:bachelorThesis
    Format: application/pdf
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    facet.materialart.
    Unknown
    Publication Date: 2022-03-14
    Description: For mixed integer programming, recent years have seen a growing interest in the design of general purpose primal heuristics for use inside complete solvers. Many of these heuristics rely on an optimal LP solution. Finding this may itself take a significant amount of time. The presented paper addresses this issue by the introduction of the Shift-And-Propagate heuristic. Shift-And-Propagate is a pre-root primal heuristic that does not require a previously found LP solution. It applies domain propagation techniques to quickly drive a variable assignment towards feasibility. Computational experiments indicate that this heuristic is a powerful supplement of existing rounding and propagation heuristics.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    facet.materialart.
    Unknown
    Publication Date: 2022-03-14
    Description: In recent years, there has been a growing interest in the design of general purpose primal heuristics for use inside complete mixed integer programming solvers. Many of these heuristics rely on an optimal LP solution, which may take a significant amount of time to find. In this paper, we address this issue by introducing a pre-root primal heuristic that does not require a previously found LP solution. This heuristic, named Shift-and-Propagate , applies domain propagation techniques to quickly drive a variable assignment towards feasibility. Computational experiments indicate that this heuristic is a powerful supplement to existing rounding and propagation heuristics.
    Language: English
    Type: article , doc-type:article
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    Publication Date: 2022-03-14
    Description: Primal heuristics are an important component of state-of-the-art codes for mixed integer programming. In this paper, we focus on primal heuristics that only employ computationally inexpensive procedures such as rounding and logical deductions (propagation). We give an overview of eight different approaches. To assess the impact of these primal heuristics on the ability to find feasible solutions, in particular early during search, we introduce a new performance measure, the primal integral. Computational experiments evaluate this and other measures on MIPLIB~2010 benchmark instances.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    Publication Date: 2022-03-14
    Description: Primal heuristics are an important component of state-of-the-art codes for mixed integer programming. In this paper, we focus on primal heuristics that only employ computationally inexpensive procedures such as rounding and logical deductions (propagation). We give an overview of eight different approaches. To assess the impact of these primal heuristics on the ability to find feasible solutions, in particular early during search, we introduce a new performance measure, the primal integral. Computational experiments evaluate this and other measures on MIPLIB~2010 benchmark instances.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 6
    Publication Date: 2022-03-14
    Description: Modern solving software for mixed-integer programming (MIP) incorporates numerous algorithmic components whose behavior is controlled by user parameter choices, and whose usefulness dramatically varies depending on the progress of the solving process. In this thesis, our aim is to construct a phase-based solver that dynamically reacts on phase transitions with an appropriate change of its component behavior. Therefore, we decompose the branch-and-bound solving process into three distinct phases: The first phase objective is to find a feasible solution. During the second phase, a sequence of incumbent solutions gets constructed until the incumbent is eventually optimal. Proving optimality is the central objective of the remaining third phase. Based on the MIP-solver SCIP we construct a phase-based solver to make use of the phase concept in two steps: First, we identify promising components for every solving phase individually and show that their combination is beneficial on a test bed of practical MIP instances. We then present and evaluate three heuristic criteria to make use of the phase-based solver in practice, where it is infeasible to distinguish between the last two phases before the termination of the solving process.
    Language: English
    Type: masterthesis , doc-type:masterThesis
    Format: application/pdf
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...