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    <title language="eng">Second-order Conditional Gradient Sliding</title>
    <abstract language="eng">Constrained second-order convex optimization algorithms are the method of choice when a high accuracy solution to a problem is needed, due to their local quadratic convergence. These algorithms require the solution of a constrained quadratic subproblem at every iteration. We present the \emph{Second-Order Conditional Gradient Sliding} (SOCGS) algorithm, which uses a projection-free algorithm to solve the constrained quadratic subproblems inexactly. When the feasible region is a polytope the algorithm converges quadratically in primal gap after a finite number of linearly convergent iterations. Once in the quadratic regime the SOCGS algorithm requires O(log(log1/ε)) first-order and Hessian oracle calls and O(log(1/ε)log(log1/ε)) linear minimization oracle calls to achieve an ε-optimal solution. This algorithm is useful when the feasible region can only be accessed efficiently through a linear optimization oracle, and computing first-order information of the function, although possible, is costly.</abstract>
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    <author>Alejandro Carderera</author>
    <submitter>Alejandro Carderera</submitter>
    <author>Sebastian Pokutta</author>
    <collection role="persons" number="pokutta">Pokutta, Sebastian</collection>
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    <id>8393</id>
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    <publishedYear>2022</publishedYear>
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    <title language="eng">FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients</title>
    <abstract language="eng">We present FrankWolfe.jl, an open-source implementation of several popular Frank–Wolfe and conditional gradients variants for first-order constrained optimization. The package is designed with flexibility and high performance in mind, allowing for easy extension and relying on few assumptions regarding the user-provided functions. It supports Julia’s unique multiple dispatch feature, and it interfaces smoothly with generic linear optimization formulations using MathOptInterface.jl.</abstract>
    <parentTitle language="eng">INFORMS Journal on Computing</parentTitle>
    <identifier type="doi">10.1287/ijoc.2022.1191</identifier>
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    <author>Besançon Mathieu</author>
    <submitter>Alejandro Carderera</submitter>
    <author>Alejandro Carderera</author>
    <author>Sebastian Pokutta</author>
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    <id>9828</id>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
    <pageFirst>718</pageFirst>
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    <volume>6</volume>
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    <publisherName>Springer Science and Business Media LLC</publisherName>
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    <completedDate>2024-10-28</completedDate>
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    <title language="eng">Challenges and opportunities in quantum optimization</title>
    <parentTitle language="eng">Nature Reviews Physics</parentTitle>
    <identifier type="doi">10.1038/s42254-024-00770-9</identifier>
    <identifier type="issn">2522-5820</identifier>
    <identifier type="arxiv">2312.02279</identifier>
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    <author>Amira Abbas</author>
    <submitter>Janina Zittel</submitter>
    <author>Andris Ambainis</author>
    <author>Brandon Augustino</author>
    <author>Andreas Bärtschi</author>
    <author>Harry Buhrman</author>
    <author>Carleton Coffrin</author>
    <author>Giorgio Cortiana</author>
    <author>Vedran Dunjko</author>
    <author>Daniel J. Egger</author>
    <author>Bruce G. Elmegreen</author>
    <author>Nicola Franco</author>
    <author>Filippo Fratini</author>
    <author>Bryce Fuller</author>
    <author>Julien Gacon</author>
    <author>Constantin Gonciulea</author>
    <author>Sander Gribling</author>
    <author>Swati Gupta</author>
    <author>Stuart Hadfield</author>
    <author>Raoul Heese</author>
    <author>Gerhard Kircher</author>
    <author>Thomas Kleinert</author>
    <author>Thorsten Koch</author>
    <author>Georgios Korpas</author>
    <author>Steve Lenk</author>
    <author>Jakub Marecek</author>
    <author>Vanio Markov</author>
    <author>Guglielmo Mazzola</author>
    <author>Stefano Mensa</author>
    <author>Naeimeh Mohseni</author>
    <author>Giacomo Nannicini</author>
    <author>Corey O’Meara</author>
    <author>Elena Peña Tapia</author>
    <author>Sebastian Pokutta</author>
    <author>Manuel Proissl</author>
    <author>Patrick Rebentrost</author>
    <author>Emre Sahin</author>
    <author>Benjamin C. B. Symons</author>
    <author>Sabine Tornow</author>
    <author>Víctor Valls</author>
    <author>Stefan Woerner</author>
    <author>Mira L. Wolf-Bauwens</author>
    <author>Jon Yard</author>
    <author>Sheir Yarkoni</author>
    <author>Dirk Zechiel</author>
    <author>Sergiy Zhuk</author>
    <author>Christa Zoufal</author>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
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  <doc>
    <id>8343</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
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    <language>eng</language>
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    <title language="eng">Location, transshipment and routing: An adaptive transportation network integrating long-haul and local vehicle routing</title>
    <abstract language="eng">The routing of commodities is a tactical problem in supply chain management that aims to synchronise transportation services connecting a network of warehouses and consolidation locations. This paper considers the routing of commodities in a transportation network that is flexible in response to demand through changes to regional warehouse clustering and the designation of consolidation locations. Traditionally, warehouse clustering and consolidation locations are determined as part of strategic planning that is performed months to years in advance of operations---limiting the flexibility in transportation networks to respond to changes in demand. A mathematical programming-based algorithmic framework is proposed to integrate the strategic decisions of location planning with tactical decisions of vehicle routing and synchronisation. A multi-armed bandit problem is developed to explore warehouse clustering decisions and exploit those that lead to small transportation costs. An extensive computational study will show that the proposed algorithmic framework effectively integrates strategic and tactical planning decisions to reduce the overall transportation costs.</abstract>
    <identifier type="urn">urn:nbn:de:0297-zib-83438</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Junko Hosoda</author>
    <submitter>Stephen Maher</submitter>
    <author>Stephen J. Maher</author>
    <author>Yuji Shinano</author>
    <author>Jonas Christoffer Villumsen</author>
    <series>
      <title>ZIB-Report</title>
      <number>21-12</number>
    </series>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
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  </doc>
  <doc>
    <id>9534</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>107105</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>54</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-03-07</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">How Many Clues To Give? A Bilevel Formulation For The Minimum Sudoku Clue Problem</title>
    <abstract language="eng">It has been shown that any 9 by 9 Sudoku puzzle must contain at least 17 clues to have a unique solution. This paper investigates the more specific question: given a particular completed Sudoku grid, what is the minimum number of clues in any puzzle whose unique solution is the given grid? We call this problem the Minimum Sudoku Clue Problem (MSCP). We formulate MSCP as a binary bilevel linear program, present a class of globally valid inequalities, and provide a computational study on 50 MSCP instances of 9 by 9 Sudoku grids. Using a general bilevel solver, we solve 95% of instances to optimality, and show that the solution process benefits from the addition of a moderate amount of inequalities. Finally, we extend the proposed model to other combinatorial problems in which uniqueness of the solution is of interest.</abstract>
    <parentTitle language="eng">Operations Research Letters</parentTitle>
    <identifier type="doi">10.1016/j.orl.2024.107105</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
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    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-90902</enrichment>
    <author>Gennesaret Tjusila</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Mathieu Besançon</author>
    <author>Mark Turner</author>
    <author>Thorsten Koch</author>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="turner">Turner, Mark Ruben</collection>
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  </doc>
  <doc>
    <id>9782</id>
    <completedYear/>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2024-10-09</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Sorting Criteria for Line-based Periodic Timetabling Heuristics</title>
    <abstract language="eng">It is well-known that optimal solutions are notoriously hard to find for the Periodic Event Scheduling Problem (PESP), which is the standard mathematical formulation to optimize periodic timetables in public transport. We consider a class of incremental heuristics that have been demonstrated to be effective by Lindner and Liebchen (2023), however, for only one fixed sorting strategy of lines along which a solution is constructed. Thus, in this paper, we examine a variety of sortings based on the number, weight, weighted span, and lower bound of arcs, and test for each setting various combinations of the driving, dwelling, and transfer arcs of lines. Additionally, we assess the impact on the incremental extension of the event-activity network by minimizing resp. maximizing a connectivity measure between subsets of lines. We compare our 27 sortings on the railway instances of the benchmarking library PESPlib within the ConcurrentPESP solver framework. We are able to find five new incumbent solutions, resulting in improvements of up to 2%.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-97826</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="AcceptedDate">2024-10-07</enrichment>
    <enrichment key="SubmissionStatus">accepted for publication</enrichment>
    <enrichment key="SourceTitle">to appear in Operations Research Proceedings 2024</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <author>Patricia Ebert</author>
    <submitter>Niels Lindner</submitter>
    <author>Berenike Masing</author>
    <author>Niels Lindner</author>
    <author>Ambros Gleixner</author>
    <series>
      <title>ZIB-Report</title>
      <number>24-07</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Public Transport</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Timetabling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Periodic Event Scheduling</value>
    </subject>
    <collection role="ccs" number="J.">Computer Applications</collection>
    <collection role="msc" number="90-XX">OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="lindner">Lindner, Niels</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="institutes" number="neo">Network Optimization</collection>
    <collection role="persons" number="masing">Masing, Berenike</collection>
    <collection role="projects" number="MODAL-MobilityLab">MODAL-MobilityLab</collection>
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    <file>https://opus4.kobv.de/opus4-zib/files/9782/ZR-24-07.pdf</file>
  </doc>
  <doc>
    <id>8530</id>
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    <language>eng</language>
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    <type>reportzib</type>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2021-12-17</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The SCIP Optimization Suite 8.0</title>
    <abstract language="eng">The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. This paper discusses enhancements and extensions contained in version 8.0 of the SCIP Optimization Suite. Major updates in SCIP include improvements in symmetry handling and decomposition algorithms, new cutting planes, a new plugin type for cut selection, and a complete rework of the way nonlinear constraints are handled. Additionally, SCIP 8.0 now supports interfaces for Julia as well as Matlab. Further, UG now includes a unified framework to parallelize all solvers, a utility to analyze computational experiments has been added to GCG, dual solutions can be postsolved by PaPILO, new heuristics and presolving methods were added to SCIP-SDP, and additional problem classes and major performance improvements are available in SCIP-Jack.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-85309</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <author>Ksenia Bestuzheva</author>
    <submitter>Ksenia Bestuzheva</submitter>
    <author>Mathieu Besançon</author>
    <author>Wei-Kun Chen</author>
    <author>Antonia Chmiela</author>
    <author>Tim Donkiewicz</author>
    <author>Jasper van Doornmalen</author>
    <author>Leon Eifler</author>
    <author>Oliver Gaul</author>
    <author>Gerald Gamrath</author>
    <author>Ambros Gleixner</author>
    <author>Leona Gottwald</author>
    <author>Christoph Graczyk</author>
    <author>Katrin Halbig</author>
    <author>Alexander Hoen</author>
    <author>Christopher Hojny</author>
    <author>Rolf van der Hulst</author>
    <author>Thorsten Koch</author>
    <author>Marco Lübbecke</author>
    <author>Stephen J. Maher</author>
    <author>Frederic Matter</author>
    <author>Erik Mühmer</author>
    <author>Benjamin Müller</author>
    <author>Marc E. Pfetsch</author>
    <author>Daniel Rehfeldt</author>
    <author>Steffan Schlein</author>
    <author>Franziska Schlösser</author>
    <author>Felipe Serrano</author>
    <author>Yuji Shinano</author>
    <author>Boro Sofranac</author>
    <author>Mark Turner</author>
    <author>Stefan Vigerske</author>
    <author>Fabian Wegscheider</author>
    <author>Philipp Wellner</author>
    <author>Dieter Weninger</author>
    <author>Jakob Witzig</author>
    <series>
      <title>ZIB-Report</title>
      <number>21-41</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Constraint integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Linear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-integer linear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-integer nonlinear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Optimization solver</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Branch-and-cut</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Branch-and-price</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Column generation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Parallelization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-integer semidefinite programming</value>
    </subject>
    <collection role="msc" number="65Y05">Parallel computation</collection>
    <collection role="msc" number="90C05">Linear programming</collection>
    <collection role="msc" number="90C10">Integer programming</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="msc" number="90C30">Nonlinear programming</collection>
    <collection role="msc" number="90C90">Applications of mathematical programming</collection>
    <collection role="persons" number="gamrath">Gamrath, Gerald</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="rehfeldt">Rehfeldt, Daniel</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
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    <collection role="persons" number="schloesser">Schlösser, Franziska</collection>
    <collection role="persons" number="turner">Turner, Mark Ruben</collection>
    <collection role="persons" number="bestuzheva">Bestuzheva, Ksenia</collection>
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  <doc>
    <id>9552</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
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    <completedDate>--</completedDate>
    <publishedDate>2024-02-26</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The SCIP Optimization Suite 9.0</title>
    <abstract language="eng">The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming framework SCIP. This report discusses the enhancements and extensions included in the SCIP Optimization Suite 9.0. The updates in SCIP 9.0 include improved symmetry handling, additions and improvements of nonlinear handlers and primal heuristics, a new cut generator and two new cut selection schemes, a new branching rule, a new LP interface, and several bug fixes. The SCIP Optimization Suite 9.0 also features new Rust and C++ interfaces for SCIP, new Python interface for SoPlex, along with enhancements to existing interfaces. The SCIP Optimization Suite 9.0 also includes new and improved features in the LP solver SoPlex, the presolving library PaPILO, the parallel framework UG, the decomposition framework GCG, and the SCIP extension SCIP-SDP. These additions and enhancements have resulted in an overall performance improvement of SCIP in terms of solving time, number of nodes in the branch-and-bound tree, as well as the reliability of the solver.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-95528</identifier>
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    <author>Suresh Bolusani</author>
    <submitter>Christoph Spiegel</submitter>
    <author>Mathieu Besançon</author>
    <submitter>Suresh Bolusani</submitter>
    <author>Ksenia Bestuzheva</author>
    <author>Antonia Chmiela</author>
    <author>João Dionísio</author>
    <author>Tim Donkiewicz</author>
    <author>Jasper van Doornmalen</author>
    <author>Leon Eifler</author>
    <author>Mohammed Ghannam</author>
    <author>Ambros Gleixner</author>
    <author>Christoph Graczyk</author>
    <author>Katrin Halbig</author>
    <author>Ivo Hedtke</author>
    <author>Alexander Hoen</author>
    <author>Christopher Hojny</author>
    <author>Rolf van der Hulst</author>
    <author>Dominik Kamp</author>
    <author>Thorsten Koch</author>
    <author>Kevin Kofler</author>
    <author>Jurgen Lentz</author>
    <author>Julian Manns</author>
    <author>Gioni Mexi</author>
    <author>Erik Mühmer</author>
    <author>Marc E. Pfetsch</author>
    <author>Franziska Schlösser</author>
    <author>Felipe Serrano</author>
    <author>Yuji Shinano</author>
    <author>Mark Turner</author>
    <author>Stefan Vigerske</author>
    <author>Dieter Weninger</author>
    <author>Liding Xu</author>
    <series>
      <title>ZIB-Report</title>
      <number>24-02-29</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Constraint integer programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Linear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-integer linear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-integer nonlinear programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Optimization solver</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Branch-and-cut</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Branch-and-price</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Column generation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Parallelization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-integer semidefinite programming</value>
    </subject>
    <collection role="msc" number="65Y05">Parallel computation</collection>
    <collection role="msc" number="90C05">Linear programming</collection>
    <collection role="msc" number="90C10">Integer programming</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="msc" number="90C30">Nonlinear programming</collection>
    <collection role="msc" number="90C90">Applications of mathematical programming</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="persons" number="vigerske">Vigerske, Stefan</collection>
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    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="schloesser">Schlösser, Franziska</collection>
    <collection role="persons" number="turner">Turner, Mark Ruben</collection>
    <collection role="persons" number="bestuzheva">Bestuzheva, Ksenia</collection>
    <collection role="persons" number="mexi">Mexi, Gioni</collection>
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    <collection role="persons" number="hoen">Hoen, Alexander</collection>
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    <collection role="persons" number="joao.goncalves.dionisio">Dionísio, João Pedro</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/9552/scipopt-90.pdf</file>
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  <doc>
    <id>9785</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>348</pageFirst>
    <pageLast>354</pageLast>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-08-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Sorting Criteria for Line-based Periodic Timetabling Heuristics</title>
    <abstract language="eng">It is well-known that optimal solutions are notoriously hard to find for the Periodic Event Scheduling Problem (PESP), which is the standard mathematical formulation to optimize periodic timetables in public transport. We consider a class of incremental heuristics that have been demonstrated to be effective by Lindner and Liebchen (2023), however, for only one fixed sorting strategy of lines along which a solution is constructed. Thus, in this paper, we examine a variety of sortings based on the number, weight, weighted span, and lower bound of arcs, and test for each setting various combinations of the driving, dwelling, and transfer arcs of lines. Additionally, we assess the impact on the incremental extension of the event-activity network by minimizing resp. maximizing a connectivity measure between subsets of lines. We compare our 27 sortings on the railway instances of the benchmarking library PESPlib within the ConcurrentPESP solver framework. We are able to find five new incumbent solutions, resulting in improvements of up to 2%.</abstract>
    <parentTitle language="eng">Operations Research Proceedings 2024. OR 2024</parentTitle>
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    <enrichment key="AcceptedDate">2024-10-07</enrichment>
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    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-97826</enrichment>
    <enrichment key="Series">Lecture Notes in Operations Research</enrichment>
    <author>Patricia Ebert</author>
    <submitter>Niels Lindner</submitter>
    <author>Berenike Masing</author>
    <author>Niels Lindner</author>
    <author>Ambros Gleixner</author>
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