8179a3f109380b4f0ba2302e3e67b0ca mathematics-12-01873-v3.pdf 65e442517329b5910afb8242f971ee81865f23a3 mathematics-12-01873-v3.pdf b36b1f610171ab3d8e31618f48b9647c4a83f7187b6277795f8aa4b57f3791f9 mathematics-12-01873-v3.pdf Title: An Agile Adaptive Biased-Randomized Discrete-Event Heuristic for the Resource-Constrained Project Scheduling Problem Subject: In industries such as aircraft or train manufacturing, large-scale manufacturing companies often manage several complex projects. Each of these projects includes multiple tasks that share a set of limited resources. Typically, these tasks are also subject to time dependencies among them. One frequent goal in these scenarios is to minimize the makespan, or total time required to complete all the tasks within the entire project. Decisions revolve around scheduling these tasks, determining the sequence in which they are processed, and allocating shared resources to optimize efficiency while respecting the time dependencies among tasks. This problem is known in the scientific literature as the Resource-Constrained Project Scheduling Problem (RCPSP). Being an NP-hard problem with time dependencies and resource constraints, several optimization algorithms have already been proposed to tackle the RCPSP. In this paper, a novel discrete-event heuristic is introduced and later extended into an agile biased-randomized algorithm complemented with an adaptive capability to tune the parameters of the algorithm. The results underscore the effectiveness of the algorithm in finding competitive solutions for this problem within short computing times. Keywords: resource-constrained project scheduling; discrete-event heuristics; biased-randomization; adaptive algorithms; agile optimization Author: Xabier A. Martin, Rosa Herrero, Angel A. Juan and Javier Panadero Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Tue Jun 25 05:29:08 2024 CEST ModDate: Tue Jun 25 05:36:09 2024 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 21 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1975037 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- EFBKCY+URWPalladioL-Bold Type 1 Custom yes yes yes 10 0 JCUMNP+URWPalladioL-Roma Type 1 Custom yes yes yes 16 0 YGLGIN+URWPalladioL-Ital Type 1 Custom yes yes yes 21 0 DVTHKG+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 SOSTRQ+CMR10 Type 1 Builtin yes yes yes 54 0 UGDJYM+CMSY10 Type 1 Builtin yes yes yes 59 0 LHRDPA+CMEX10 Type 1 Builtin yes yes yes 78 0 NAVZQW+PazoMath-Italic Type 1 Builtin yes yes yes 83 0 XNDIHY+PazoMath Type 1 Builtin yes yes yes 88 0 APUZFZ+MSAM10 Type 1 Builtin yes yes yes 93 0 RLYTRW+CMMI10 Type 1 Builtin yes yes yes 104 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-02-20 02:13:52 CET RepresentationInformation: mathematics-12-01873-v3.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-02-19 10:18:48 CET Size: 1975037 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 417 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: Problem Description Destination: section.3 Item: Title: Solving Methodology Destination: section.4 Item: Title: Computational Experiments Destination: section.5 Item: Title: Analysis of Results Destination: section.6 Item: Title: Conclusions and Future Work Destination: section.7 Item: Title: References Destination: section.8 Info: Title: An Agile Adaptive Biased-Randomized Discrete-Event Heuristic for the Resource-Constrained Project Scheduling Problem Author: Xabier A. Martin, Rosa Herrero, Angel A. Juan and Javier Panadero Subject: In industries such as aircraft or train manufacturing, large-scale manufacturing companies often manage several complex projects. Each of these projects includes multiple tasks that share a set of limited resources. Typically, these tasks are also subject to time dependencies among them. One frequent goal in these scenarios is to minimize the makespan, or total time required to complete all the tasks within the entire project. Decisions revolve around scheduling these tasks, determining the sequence in which they are processed, and allocating shared resources to optimize efficiency while respecting the time dependencies among tasks. This problem is known in the scientific literature as the Resource-Constrained Project Scheduling Problem (RCPSP). Being an NP-hard problem with time dependencies and resource constraints, several optimization algorithms have already been proposed to tackle the RCPSP. In this paper, a novel discrete-event heuristic is introduced and later extended into an agile biased-randomized algorithm complemented with an adaptive capability to tune the parameters of the algorithm. The results underscore the effectiveness of the algorithm in finding competitive solutions for this problem within short computing times. 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