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doctoral thesis
Raspoređivanje zasnovano na prilagodljivim pravilima
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2005. urn:nbn:hr:168:435819
Jakobović, Domagoj
University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Electronics, Microelectronics, Computer and Intelligent Systems

Institutional repository: FER Repository

Cite this document

Jakobović, D. (2005). Raspoređivanje zasnovano na prilagodljivim pravilima (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:435819

Jakobović, Domagoj. "Raspoređivanje zasnovano na prilagodljivim pravilima." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2005. https://urn.nsk.hr/urn:nbn:hr:168:435819

Jakobović, Domagoj. "Raspoređivanje zasnovano na prilagodljivim pravilima." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2005. https://urn.nsk.hr/urn:nbn:hr:168:435819

Jakobović, D. (2005). 'Raspoređivanje zasnovano na prilagodljivim pravilima', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 06 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:435819

Jakobović D. Raspoređivanje zasnovano na prilagodljivim pravilima [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2005 [cited 2024 May 06] Available at: https://urn.nsk.hr/urn:nbn:hr:168:435819

D. Jakobović, "Raspoređivanje zasnovano na prilagodljivim pravilima", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2005. Available at: https://urn.nsk.hr/urn:nbn:hr:168:435819

Metadata
TitleRaspoređivanje zasnovano na prilagodljivim pravilima
Title (english)Scheduling based on adaptive rules
AuthorDomagoj Jakobović
MentorLeo Budin (mentor)
Committee memberVedran Mornar (član povjerenstva)
Committee memberLeo Budin (član povjerenstva)
Committee memberGoran Martinović (član povjerenstva)
Committee memberNikola Bogunović (član povjerenstva)
Committee memberBojana Dalbelo-Bašić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Electronics, Microelectronics, Computer and Intelligent Systems)
Zagreb
Defense date and country2005-12-07, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Universal decimal classification
(UDC)		004 - Computer science and technology. Computing. Data processing
Abstract
U radu se promatra problem definiranja prikladnih postupaka raspoređivanja za različita okruženja s obzirom na uvjete raspoređivanja i zadane kriterije. Predlaže se metodologija izvođenja algoritama raspoređivanja uz pomoć genetskog programiranja. Algoritmi raspoređivanja poprimaju oblik pravila u kojima se elementima u sustavu dodjeljuje prioritet na temelju kojega se aktivnosti pridružuju sredstvima. Koristeći genetsko programiranje, izvode se pravila za različita okruženja raspoređivanja: raspoređivanje na jednom stroju, na paralelnim jednolikim strojevima, nesrodnim strojevima te u okruženju proizvoljne obrade. Za pojedino okruženje postupak raspoređivanja definiran je u dva dijela: jedan dio predstavlja meta-algoritam koji koristi prioritete elemenata u sustavu kako bi pridruživao aktivnosti sredstvima, a drugi dio predstavlja funkciju koja određuje prioritete elemenata. Prioritetna funkcija dobiva se primjenom genetskog programiranja. Za svako okruženje definirani su skupovi ispitnih primjera za učenje i ocjenu, a predloženi algoritmi uspoređeni su sa postojećim algoritmima raspoređivanja. Algoritmi raspoređivanja izvedeni uz pomoć genetskog programiranja pokazuju sličnu ili bolju učinkovitost u usporedbi s postojećim algoritmima, a značajnu prednost ostvaruju u okolinama raspoređivanja za koje ne postoje prikladni postupci raspoređivanja. U radu je također opisan postupak vrednovanja podatkovnih elemenata rješenja genetskog programiranja te postupak prilagodbe primjene genetskih operatora križanja i mutacije. Predloženi postupci prilagodbe olakšavaju pronalaženje kvalitetnog rješenja i povećavaju uspješnost evolucijskog procesa.
Abstract (english)
In this work the problem of devising an appropriate scheduling policy for different environments is addressed. The methodology which uses genetic programming to evolve scheduling heuristics is described. The scheduling heuristics are developed in the form of scheduling rules which define dynamic priorities for the elements in the system. Sheduling rules for different environments are devised using genetic programming: one machine, parallel proportional machines, unrelated machines and job shop environment. Scheduling algorithms are defined with two components: one component represents meta-algorithm which operates in scheduling environment, and the other represents an appropriate scheduling policy which derives job or machine priorities. The scheduling policy is evolved with genetic programming. For each scheduling environment a set of learning and a set of evaluation scheduling instances is defined. Devised algorithms are compared with existing algorithms in each environment. The evolved algorithms exhibit similar or better efficiency in all cases, and a significant improvement is achieved in scheduling environments where there are no fitting algorithms. Additionally, a method for evaluation of terminals in genetic programming solution and adaptive probabilities for genetic operators crossover and mutation are devised. The adaptive methods increase the probability of finding a good solution and may speed up the evolution process.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:435819
Study programmeTitle: Computer Science
Study programme type: university
Study level: postgraduate
Academic / professional title: Doktor znanosti (Doktor znanosti)
Type of resourceText
File originBorn digital
Access conditionsClosed access
Terms of useLicence In copyright icon
Created on2019-07-16 07:37:24