afd70dd9675da84a4c81d9d591d99e29 Alg_a2023v16n12p532.pdf 139224f9df50db4d8939082cf5b533c65c35da98 Alg_a2023v16n12p532.pdf 8c0961bddbfc8177461745af1068ffdc7eef1e04ddde4e1df1f8ad1c512e08ac Alg_a2023v16n12p532.pdf Title: A Learnheuristic Algorithm for the Capacitated Dispersion Problem under Dynamic Conditions Subject: The capacitated dispersion problem, which is a variant of the maximum diversity problem, aims to determine a set of elements within a network. These elements could symbolize, for instance, facilities in a supply chain or transmission nodes in a telecommunication network. While each element typically has a bounded service capacity, in this research, we introduce a twist. The capacity of each node might be influenced by a random Bernoulli component, thereby rendering the possibility of a node having zero capacity, which is contingent upon a black box mechanism that accounts for environmental variables. Recognizing the inherent complexity and the NP-hard nature of the capacitated dispersion problem, heuristic algorithms have become indispensable for handling larger instances. In this paper, we introduce a novel approach by hybridizing a heuristic algorithm with reinforcement learning to address this intricate problem variant. Keywords: capacitated dispersion problem; metaheuristics; reinforcement learning; supply chains; telecommunication networks Author: Juan F. Gomez, Antonio R. Uguina, Javier Panadero and Angel A. Juan Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Nov 22 08:41:42 2023 CET ModDate: Wed Nov 22 08:44:45 2023 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 15 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 532357 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- IJWRPD+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 JAMHNH+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 QBOZOO+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 WCYBKT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 38 0 SYFPBV+CMMI10 Type 1 Builtin yes yes no 60 0 RDVFIF+CMSY10 Type 1 Builtin yes yes no 64 0 GIGFZE+CMR10 Type 1 Builtin yes yes no 68 0 XNDIHY+PazoMath Type 1 Builtin yes yes no 72 0 BLBZGP+PazoMath-Italic Type 1 Builtin yes yes no 79 0 DXJXEP+CMEX10 Type 1 Builtin yes yes no 97 0 IUOUHH+PazoMath-BoldItalic Type 1 Builtin yes yes no 110 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-05-11 02:46:19 CEST RepresentationInformation: Alg_a2023v16n12p532.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-05-10 16:04:04 CEST Size: 532357 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 264 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Related Work on Diversity Problems Destination: section.2 Item: Title: Problem Definition Destination: section.3 Item: Title: Solution Approaches for the Dynamic Capacitated Dispersion Problem Destination: section.4 Item: Title: Numerical Experiments and Results Destination: section.5 Children: Item: Title: Computational Environment Destination: subsection.5.1 Item: Title: Black Box Parameters Destination: subsection.5.2 Item: Title: Analysis of Results Destination: subsection.5.3 Item: Title: Conclusions and Future Work Destination: section.6 Item: Title: References Destination: section.7 Info: Title: A Learnheuristic Algorithm for the Capacitated Dispersion Problem under Dynamic Conditions Author: Juan F. Gomez, Antonio R. Uguina, Javier Panadero and Angel A. Juan Subject: The capacitated dispersion problem, which is a variant of the maximum diversity problem, aims to determine a set of elements within a network. These elements could symbolize, for instance, facilities in a supply chain or transmission nodes in a telecommunication network. While each element typically has a bounded service capacity, in this research, we introduce a twist. The capacity of each node might be influenced by a random Bernoulli component, thereby rendering the possibility of a node having zero capacity, which is contingent upon a black box mechanism that accounts for environmental variables. Recognizing the inherent complexity and the NP-hard nature of the capacitated dispersion problem, heuristic algorithms have become indispensable for handling larger instances. In this paper, we introduce a novel approach by hybridizing a heuristic algorithm with reinforcement learning to address this intricate problem variant. 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