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Title:Primerjava algoritmov za optimizacijo delovnega načrta
Authors:ID Roškar, Marko (Author)
ID Strnad, Damjan (Mentor) More about this mentor... New window
ID Kohek, Štefan (Comentor)
Files:.pdf MAG_Roskar_Marko_2026.pdf (2,09 MB)
MD5: 48688D42B595CAF116372B62C673AF8B
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V zaključnem delu obravnavamo problem načrtovanja poslovne poti, pri katerem je treba v omejenem delovnem dnevu obiskati podmnožico strank z maksimizacijo dobička ob upoštevanju časovne omejitve, obveznih strank in stroškov goriva. Problem formalno opredelimo z matematičnim modelom in rešimo s petimi pristopi: požrešno strategijo, mešanim celoštevilskim linearnim programiranjem, simuliranim ohlajanjem, prilagodljivim iskanjem po velikih soseščinah in genetskim algoritmom. Eksperimentalno ovrednotenje na 70 testnih instancah je pokazalo, da simulirano ohlajanje dosega najboljše rezultate z razdaljo do najboljše najdene rešitve pod 0,62 % pri vseh velikostih instanc.
Keywords:načrtovanje poslovne poti, simulirano ohlajanje, kombinatorična optimizacija, genetski algoritem, prilagodljivo iskanje po velikih soseščinah
Place of publishing:Maribor
Year of publishing:2026
PID:20.500.12556/DKUM-98105 New window
Publication date in DKUM:29.05.2026
Views:146
Downloads:27
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:16.05.2026

Secondary language

Language:English
Title:Comparison of algorithms for work schedule optimization
Abstract:This thesis addresses the business route planning problem, where a subset of customers must be visited within a limited working day to maximize profit while respecting time constraints, mandatory customers, and fuel costs. The problem is formally defined with a mathematical model and solved using five approaches: a greedy strategy, mixed integer linear programming, simulated annealing, adaptive large neighborhood search, and a genetic algorithm. Experimental evaluation on 70 test instances showed that simulated annealing achieves the best results with a gap to the best-found solution below 0.62 % across all instance sizes.
Keywords:business route planning, simulated annealing, combinatorial optimization, genetic algorithm, adaptive large neighborhood search


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