c16bf6de572858f6aebf9107cd1e0baf mathematics-12-01758-v2.pdf 5cc17d969ea5b07835d7f236b2ff1814b32619e9 mathematics-12-01758-v2.pdf 84e22bc0877a9d52e05e93cf529092d6de80bed5ae4e3038da30a6765de41c8d mathematics-12-01758-v2.pdf Title: A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem Subject: The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and uncertain conditions inherent in real-world transportation scenarios, we introduce a novel dynamic variant of the TOP that considers real-time changes in environmental conditions affecting reward acquisition at each node. Specifically, we model the dynamic nature of environmental factors—such as traffic congestion, weather conditions, and battery level of each vehicle—to reflect their impact on the probability of obtaining the reward when visiting each type of node in a heterogeneous network. To address this problem, a learnheuristic optimization framework is proposed. It combines a metaheuristic algorithm with Thompson sampling to make informed decisions in dynamic environments. Furthermore, we conduct empirical experiments to assess the impact of varying reward probabilities on resource allocation and route planning within the context of this dynamic TOP, where nodes might offer a different reward behavior depending upon the environmental conditions. Our numerical results indicate that the proposed learnheuristic algorithm outperforms static approaches, achieving up to 25% better performance in highly dynamic scenarios. Our findings highlight the effectiveness of our approach in adapting to dynamic conditions and optimizing decision-making processes in transportation systems. Keywords: combinatorial optimization; team orienteering problem; reinforcement learning; learnheuristics Author: Antonio R. Uguina, Juan F. Gomez, Javier Panadero, Anna Martínez-Gavara and Angel A. Juan Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Tue Jun 11 10:15:02 2024 CEST ModDate: Tue Jun 11 10:19:55 2024 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 19 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 681071 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- KVVELN+URWPalladioL-Bold Type 1 Custom yes yes yes 10 0 WJTYUT+URWPalladioL-Roma Type 1 Custom yes yes yes 16 0 UXIFYI+URWPalladioL-Ital Type 1 Custom yes yes yes 21 0 DVTHKG+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 SYFPBV+CMMI10 Type 1 Builtin yes yes yes 57 0 VSQXZF+CMSY10 Type 1 Builtin yes yes yes 62 0 BYLEUW+PazoMath-Italic Type 1 Builtin yes yes yes 71 0 SOSTRQ+CMR10 Type 1 Builtin yes yes yes 76 0 XNDIHY+PazoMath Type 1 Builtin yes yes yes 86 0 ZPGVCH+CMEX10 Type 1 Builtin yes yes yes 97 0 IUOUHH+PazoMath-BoldItalic Type 1 Builtin yes yes yes 123 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-12-19 02:51:19 CET RepresentationInformation: mathematics-12-01758-v2.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-12-14 08:46:46 CET Size: 681071 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 325 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Related Work Destination: section.2 Item: Title: Describing the DTOP Destination: section.3 Children: Item: Title: An Illustrative Example of the DTOP Destination: subsection.3.1 Item: Title: A Mathematical Model for the DTOP Destination: subsection.3.2 Item: Title: Modeling Probabilities of Reward for Different Types of Nodes Destination: section.4 Item: Title: Solution Approaches for the Heterogeneous DTOP Destination: section.5 Children: Item: Title: Online Contextual Thompson Sampling Destination: subsection.5.1 Item: Title: A Static Constructive Heuristic Destination: subsection.5.2 Item: Title: A Learnheuristic Constructive Heuristic Destination: subsection.5.3 Item: Title: Numerical Experiments Destination: section.6 Item: Title: Analysis of Results Destination: section.7 Item: Title: Conclusions and Future Work Destination: section.8 Item: Title: References Destination: section.9 Info: Title: A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem Author: Antonio R. Uguina, Juan F. Gomez, Javier Panadero, Anna Martínez-Gavara and Angel A. Juan Subject: The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and uncertain conditions inherent in real-world transportation scenarios, we introduce a novel dynamic variant of the TOP that considers real-time changes in environmental conditions affecting reward acquisition at each node. Specifically, we model the dynamic nature of environmental factors such as traffic congestion, weather conditions, and battery level of each vehicle to reflect their impact on the probability of obtaining the reward when visiting each type of node in a heterogeneous network. To address this problem, a learnheuristic optimization framework is proposed. It combines a metaheuristic algorithm with Thompson sampling to make informed decisions in dynamic environments. Furthermore, we conduct empirical experiments to assess the impact of varying reward probabilities on resource allocation and route planning within the context of this dynamic TOP, where nodes might offer a different reward behavior depending upon the environmental conditions. Our numerical results indicate that the proposed learnheuristic algorithm outperforms static approaches, achieving up to 25% better performance in highly dynamic scenarios. Our findings highlight the effectiveness of our approach in adapting to dynamic conditions and optimizing decision-making processes in transportation systems. 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