Ρεφανίδης, ΙωάννηςΜανωλάκης, ΔημήτρηςΔιπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2024.Submitted by ΔΗΜΗΤΡΙΟΣ ΜΑΝΩΛΑΚΗΣ (aid22011@uom.edu.gr) on 2024-03-11T12:46:29Z No. of bitstreams: 2 license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) ManolakisDimitrisMsc2024.pdf: 981023 bytes, checksum: bb7968f487249ad38586a1a94febfa76 (MD5)Made available in DSpace on 2024-03-12T13:02:12Z (GMT). No. of bitstreams: 3 license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) ManolakisDimitrisMsc2024.pdf: 2848060 bytes, checksum: 5ca2dec7f9cb68c4ed8b433581540080 (MD5) ManolakisDimitrisMsc2024Presentation.pdf: 981023 bytes, checksum: bb7968f487249ad38586a1a94febfa76 (MD5) Previous issue date: 2024-03-11This thesis tackles the problem of finding time optimal routes for trains over a railway network. The problem is defined as follows: A train has a known length. The position of the train is defined over parts of one or more consecutive track segments. There are a maximum speed, a maximum acceleration and a maximum deceleration capability for the train. Each track segment has a maximum allowed speed for any train being over it. A problem instance is defined by an initial and a goal state, which are two positions accompanied with desired speeds (being usually, but not necessarily, zero). In this study we are interested in minimizing the total duration of reaching the goal state from the initial one; other metrics such as fuel consumption could be considered. We solve this problem using basic kinematics and A*. We present two algorithms: The first one computes analytically in continuous space the optimal speed profile of the train for a problem defined over a given path. The second algorithm extends the first one over arbitrary graphs. A* empowered with a simple admissible heuristic is employed to find the optimal combination of speed profile and path.Rejected by Κυριακή Μπαλτά (balta@uom.gr), reason: Να αναρτηθεί το κείμενο της εργασίας σας, έχετε αναρτήσει μόνο την παρουσίαση της on 2024-03-12T07:41:17Z (GMT)Approved for entry into archive by Κυριακή Μπαλτά (balta@uom.gr) on 2024-03-12T13:02:12Z (GMT) No. of bitstreams: 3 license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) ManolakisDimitrisMsc2024.pdf: 2848060 bytes, checksum: 5ca2dec7f9cb68c4ed8b433581540080 (MD5) ManolakisDimitrisMsc2024Presentation.pdf: 981023 bytes, checksum: bb7968f487249ad38586a1a94febfa76 (MD5)Submitted by ΔΗΜΗΤΡΙΟΣ ΜΑΝΩΛΑΚΗΣ (aid22011@uom.edu.gr) on 2024-03-12T12:36:16Z No. of bitstreams: 3 license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) ManolakisDimitrisMsc2024.pdf: 2848060 bytes, checksum: 5ca2dec7f9cb68c4ed8b433581540080 (MD5) ManolakisDimitrisMsc2024Presentation.pdf: 981023 bytes, checksum: bb7968f487249ad38586a1a94febfa76 (MD5)http://dspace.lib.uom.gr/handle/2159/30239Πανεπιστήμιο ΜακεδονίαςAttribution-NonCommercial-NoDerivatives 4.0 Διεθνέςhttp://creativecommons.org/licenses/by-nc-nd/4.0/Heuristic SearchTrain-scheduling for railway networks using basic kinematics and A*Electronic Thesis or DissertationText2024Τεχνητή νοημοσύνηArtificial Intelligenceisi - Computer Science, Artificial Intelligence covers resources that focus on research and techniques to create machines that attempt to efficiently reason, problem-solve, use knowledge representation, and perform analysis of contradictory or ambiguous information. This category includes resources on artificial intelligence technologies such as expert systems, fuzzy systems, natural language processing, speech recognition, pattern recognition, computer vision, decision-support systems, knowledge bases, and neural networks.Μεταπτυχιακή εργασίαMaster thesisΠανεπιστήμιο ΜακεδονίαςGreek Aggregator OpenArchives.gr | National Documentation Centre (EKT)