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Bibliographic record number: 894458

Journal

Authors: Čorić, Rebeka; Đumić, Mateja; Jakobović, Domagoj
Title: Complexity Comparison of Integer Programming and Genetic Algorithms for Resource Constrained Scheduling Problems
( Complexity Comparison of Integer Programming and Genetic Algorithms for Resource Constrained Scheduling Problems )
Source: MIPRO 2017 Int. Conf. Proceedings. / Biljanović, Petar (ed). - Opatija : Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO , 2017. 1394-1400 (ISBN: 978-953-233-093-9).
ISSN: 1847-3938
Meeting: MIPRO 2017, 40th Jubilee International Convention
Location and date: Opatija, Hrvatska, 22.-26.05.2017
Keywords: scheduling, RCPSP, GA, IP
( scheduling, RCPSP, GA, IP )
Abstract:
Resource constrained project scheduling problem (RCPSP) is one of the most intractable combinatorial optimization problems. RCPSP belongs to the class of NP hard problems. Integer Programming (IP) is one of the exact solving methods that can be used for solving RCPSP. IP formulation uses binary decision variables for generating a feasible solution and with different boundaries eliminates some of solutions to reduce the solution space size. All exact methods, including IP, search through entire solution space so they are impractical for very large problem instances. Due to the fact that exact methods are not applicable to all problem instances, many heuristic approaches are developed, such as genetic algorithms. In this paper we compare the time complexity of IP formulations and genetic algorithms when solving the RCPSP. We present two different solution representations for genetic algorithms, permutation vector and vector of floating point numbers. Two formulations of IP and and their time and convergence results are compared for the aforementioned approaches.
Type of meeting: Predavanje
Type of presentation in a journal: Full-text (1500 words and more)
Type of peer-review: International peer-review
Original language: eng
Category: Znanstveni
Research fields:
Computer science
URL: http://docs.mipro-proceedings.com/proceedings/mipro_2017_proceedings.pdf
Contrib. to CROSBI by: Mateja Đumić (mdjumic@mathos.hr), 27. Ruj. 2017. u 11:59 sati



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