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    <id>1855</id>
    <completedYear>2024</completedYear>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1198</pageFirst>
    <pageLast>1214</pageLast>
    <pageNumber>17 Seiten</pageNumber>
    <edition/>
    <issue>10</issue>
    <volume>57</volume>
    <type>article</type>
    <publisherName>Taylor &amp; Francis</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-11-22</completedDate>
    <publishedDate>2024-11-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">On delivery policies for a truck-and-drone tandem in disaster relief</title>
    <abstract language="eng">This article introduces the traveling salesman problem with a truck and a drone under incomplete information (TSP-DI). TSP-DI is motivated by the deliveries of emergency supplies under unknown road conditions in the immediate aftermath of a disastrous event. The urgency may force the immediate dispatch of relief vehicles, such that road damages blocking the truck’s planned route are detected “on-the-fly”. The relief transport must schedule deliveries anticipating possible unplanned truck detours, enforce (planned) drone detours for early checking of key road segments, and consider the dynamic nature of road condition information. In this investigation, we perform a competitive analysis of a widely used delivery policy for TSP-DI in practice – the online re-optimization policy (Reopt) – and compare it with several alternative delivery strategies. Competitive analysis examines the worst-case performance of the strategies and is particularly important in the context of disaster relief, where worst-case outcomes must be avoided. Our analysis shows that Reopt is dominated by alternative delivery policies in terms of the competitive ratio even at a medium level of damage on the road. It also underscores the importance of surveillance detours performed by the drone, even if the surveillance delays the start of the deliveries.</abstract>
    <parentTitle language="eng">IISE Transactions (Online ISSN: 2472-5862)</parentTitle>
    <identifier type="doi">10.1080/24725854.2024.2410353</identifier>
    <identifier type="urn">urn:nbn:de:bvb:739-opus4-18555</identifier>
    <enrichment key="opus.source">publish</enrichment>
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    <enrichment key="review.accepted_by">2</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alena Otto</author>
    <author>Bruce Golden</author>
    <author>Catherine Lorenz</author>
    <author>Yuchen Luo</author>
    <author>Erwin Pesch</author>
    <author>Luis Rocha</author>
    <collection role="ddc" number="330">Wirtschaft</collection>
    <collection role="ddc" number="380">Handel, Kommunikation, Verkehr</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="">Wirtschaftswissenschaftliche Fakultät</collection>
    <collection role="Transformationsvertrag" number="">Taylor &amp; Francis</collection>
    <thesisPublisher>Universität Passau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-uni-passau/files/1855/otto_delivery_policies_truck-and-drone_tandem.pdf</file>
  </doc>
  <doc>
    <id>1564</id>
    <completedYear>2025</completedYear>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted>2024</thesisYearAccepted>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>xv, 184 Seiten</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>doctoralthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-03-31</completedDate>
    <publishedDate>2025-03-31</publishedDate>
    <thesisDateAccepted>2024-11-28</thesisDateAccepted>
    <title language="eng">Studies on optimization problems with dynamically arriving information</title>
    <abstract language="eng">In today’s fast-paced world, transportation planning, e-commerce, smart manufacturing, emergency services, and financial markets operate in real-time environments where dynamically arriving information must be integrated on-the-fly into decision-making. Research produced online algorithms ranging from myopic reoptimization (Reopt) to learning-based anticipation methods. However, given the complexity of real-world problems, optimal decision policies remain unknown, and effectiveness is often assessed through simulations against simple benchmarks, leaving improvement potential and robustness uncertain.&#13;
This dissertation proposes effective online policies using classical and innovative analytical and computational evaluation methods, establishing performance bounds and comparisons to optimal solutions. It designs algorithmic frameworks for two dynamic optimization problems: the Online Order Batching, Sequencing, and picker Routing Problem (OOBSRP) in warehousing and the Traveling Salesman Problem with a Truck and a Drone under Incomplete Information (TSP-DI), for disaster relief. Given the importance of automation in real-time environments, a strong emphasis is placed on robotic solutions. &#13;
&#13;
For the OOBSRP with manual and robotic carts, we prove that Reopt is asymptotically optimal with probability one under broad stochastic conditions. From a worst-case perspective, no policy can improve Reopt by more than 50%, as it is shown to be asymptotically two-competitive. A computational study confirms that Reopt’s gaps to the complete-information optimum are small, e.g. averaging less than 5% for a cost-minimization objective. A pattern analysis of Complete-Information Optimal Solutions (CIOSs), generated with dynamic programming algorithms, identifies simple algorithmic enhancements – like eliminating waiting, intervention, or strategic relocation  – that further reduce costs and delivery times. These findings suggest limited benefits of anticipatory (including AI-based) algorithms in OOBSRP. &#13;
  &#13;
Conversely, for TSP-DI, where road blockages reveal dynamically, Reopt performs poorly in the worst case, as we reveal its exponentially growing competitive ratio.  We show that policies delaying deliveries for drone surveillance are significantly superior in competitive ratio. A proposed hybrid policy achieves best average and worst-case results in experiments. Using battery-limited drones introduces a challenging static subproblem within these policies, classified as Drone Routing Problems with Energy Replenishment (DRP-E). We develop a Very Large-Scale Neighborhood Search (VLNS) and an exact method for generic DRP-Es. VLNS searches an exponential-sized neighborhood of a promising solution entirely in polynomial runtime, making it ideal for real-time policies or intensification in metaheuristics. &#13;
&#13;
This dissertation underscores the importance of analytical guarantees and comparisons to the optimum in online algorithm design, as policy effectiveness often diverges from intuition and varies significantly across problems.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:739-opus4-15649</identifier>
    <note>According to § 11 FPromO, Abs. 1, Satz 5 of the Promotionsordnung and with the agreement of the Chair of the Board of Examiners for Doctoral Awards, the following minor revisions have been made for the publication of the dissertation compared to the version submitted for grading. These changes result from comments and requests by the external reviewer, Prof. Dr. Stefan Irnich, as well as the author’s own observations during the revision process:&#13;
&#13;
- Corrected minor typos in grammar and mathematical notation.&#13;
&#13;
- Implemented wording improvements.&#13;
&#13;
- Reorganized and updated the list of abbreviations alphabetically.&#13;
&#13;
- Updated the publication status of the list of papers to the submission date and added the affiliation of the University of Bologna.&#13;
&#13;
- Page 13: Corrected the reduction of the average observed gap to CIOS of Reopt (16.6 percentage points) and provided clarification.&#13;
&#13;
- Pages 23 and 58: Added an inequality of indices k and l in definitions of a partition of batches in an optimal solution.&#13;
&#13;
- Page 24, "because the cart- and picker equipment is order-specific for each batch" changed to "because of pick-lists that are printed out "&#13;
&#13;
- Page 55: Added and corrected a statement (one sentence) regarding the reference Wahlen and Geschwind (2023).&#13;
&#13;
- Page 71: Added the statement: "Note that an increase in batching capacity significantly impacts the runtime of the DP approaches for both objectives."&#13;
&#13;
- Page 80: Replaced and corrected Figure 3.8 (statement remains unchanged).&#13;
&#13;
- Page 79: Corrected column names in Table 3.12.&#13;
&#13;
- Standardized the abbreviation "VLNS" instead of "VLSN" throughout Chapter 5.&#13;
&#13;
- Unified the written-out problem names of OOBSRP and OBSRP-R throughout the dissertation</note>
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    <licence>Standardbedingung laut Einverständniserklärung</licence>
    <author>Catherine Lorenz</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dynamic optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Online algorithms</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Competitive analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Probabilistic performance guarantees</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Very large-scale neighborhood search</value>
    </subject>
    <collection role="ddc" number="330">Wirtschaft</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="">Wirtschaftswissenschaftliche Fakultät</collection>
    <thesisPublisher>Universität Passau</thesisPublisher>
    <thesisGrantor>Universität Passau</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-uni-passau/files/1564/Catherine_Lorenz_dynamic_optimization.pdf</file>
  </doc>
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