Exploring close-optimal solutions for the time constrained scheduling problem in project management

Kiriklidis, C; Kirytopoulos, K; Rokou, E

dc.contributor.author	Kiriklidis, C	en
dc.contributor.author	Kirytopoulos, K	en
dc.contributor.author	Rokou, E	en
dc.date.accessioned	2014-03-01T02:53:16Z
dc.date.available	2014-03-01T02:53:16Z
dc.date.issued	2011	en
dc.identifier.issn	21573611	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36205
dc.subject	genetic algorithm	en
dc.subject	project management	en
dc.subject	resource leveling	en
dc.subject	Time constraint project scheduling	en
dc.subject.other	Fitness functions	en
dc.subject.other	Project scheduling problem	en
dc.subject.other	Resource leveling	en
dc.subject.other	Resource usage	en
dc.subject.other	Time constrained scheduling	en
dc.subject.other	Time constraints	en
dc.subject.other	Two stage approach	en
dc.subject.other	Two-stage heuristic algorithms	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Heuristic algorithms	en
dc.subject.other	Industrial engineering	en
dc.subject.other	Project management	en
dc.title	Exploring close-optimal solutions for the time constrained scheduling problem in project management	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEEM.2011.6118035	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEEM.2011.6118035	en
heal.identifier.secondary	6118035	en
heal.publicationDate	2011	en
heal.abstract	This paper presents a new approach for the Time Constraint Project Scheduling Problem (TCPSP). A two stage heuristic algorithm was developed for this problem. During the first stage a genetic algorithm using as fitness function, a function composition of max resource usage and the differences between actual and desirable resource usage, is executed to get a set of solutions. The second stage consists of choosing the best chromosomes and moving the solution's set start times ±1 to randomly generate another set of solutions. Experimental results of the two stage approach are presented and compared to the single genetic algorithm results. © 2011 IEEE.	en
heal.journalName	IEEE International Conference on Industrial Engineering and Engineering Management	en
dc.identifier.doi	10.1109/IEEM.2011.6118035	en
dc.identifier.spage	844	en
dc.identifier.epage	847	en