Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Modular Lipschitzian and Contractive Maps Read chapter Read first chapter

2015 | OriginalPaper | Chapter

A Genetic Algorithm for Scheduling Alternative Tasks Subject to Technical Failure

Authors : Dalila B. M. M. Fontes, José Fernando Gonçalves

Published in: Optimization, Control, and Applications in the Information Age

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Nowadays, organizations are often faced with the development of complex and innovative projects. This type of projects often involves performing tasks which are subject to failure. Thus, in many such projects several possible alternative actions are considered and performed simultaneously. Each alternative is characterized by cost, duration, and probability of technical success. The cost of each alternative is paid at the beginning of the alternative and the project payoff is obtained whenever an alternative has been completed successfully. For this problem one wishes to find the optimal schedule, i.e., the starting time of each alternative, such that the expected net present value is maximized. This problem has been recently proposed in Ranjbar (Int Trans Oper Res 20(2):251–266, 2013), where a branch-and-bound approach is reported. Since the problem is NP-Hard, here we propose to solve the problem using genetic algorithms.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Modeling and Solving Vehicle Routing Problems with Many Available Vehicle Types
next chapter Discrete Competitive Facility Location: Modeling and Optimization Approaches
Footnotes
1
Since each alternative consists of a single activity, here and hereafter we will use indifferently alternative and activity.
 
2
The number of precedence-related activity pairs divided by the theoretically maximum number of such pairs in the network [20].
 
Literature
1.
Bean, J.C.: Genetic algorithms and random keys for sequencing and optimization. ORSA J. Comput. 6, 154–160 (1994)
2.
Colvin, M., Maravelias, C.T.: R&d pipeline management: task interdependencies and risk management. Eur. J. Oper. Res. 215(3), 616–628 (2011)
3.
Coolen, K., Wei, W., Nobibon, F.T., Leus, R.: Scheduling modular projects on a bottleneck resource. J. Sched. 17(1), 67–85 (2014)
4.
Creemers, S., Leus, R., De Reyck, B., Lambrecht, M.: Project scheduling for maximum npv with variable activity durations and uncertain activity outcomes. In: IEEE International Conference on Industrial Engineering and Engineering Management, 2008. IEEM 2008, pp. 183–187, 2008
5.
Creemers, S., De Reyck, B., Leus, R.: R&d project planning with multiple trials in uncertain environments. In: IEEE International Conference on Industrial Engineering and Engineering Management, 2009. IEEM 2009, pp. 325–329, 2009
6.
Creemers, S., De Reyck, B., Leus, R.: Project planning with alternative technologies in uncertain environments. FEB Research Report KBI_1314, 2013
7.
De Reyck, B., Leus, R.: R&d project scheduling when activities may fail. IIE Trans. 40(4), 367–384 (2008)
8.
De Reyck, B., Grushka-Cockayne, Y., Leus, R.: A new challenge in project scheduling: the incorporation of activity failures. Tijdschrift voor economie en management 52(3), 411 (2007)
9.
Demeulemeester, E., Vanhoucke, M., Herroelen, W.: Rangen: a random network generator for activity-on-the-node networks. J. Sched. 6(1), 17–38 (2003)
10.
Fontes, D.B.M.M., Gonçalves, J.F.: A multi-population hybrid biased random key genetic algorithm for hop-constrained trees in nonlinear cost flow networks. Optim. Lett. 7(6), 1303–1324 (2013)
12.
Gonçalves, J.F., Resende, M.G.C.: An evolutionary algorithm for manufacturing cell formation. Comput. Ind. Eng. 47, 247–273 (2004)
13.
Gonçalves, J.F., Resende, M.G.C.: Biased random-key genetic algorithms for combinatorial optimization. J. Heuristics 17, 487–525 (2011)
14.
Gonçalves, J.F., Resende, M.G.C.: A parallel multi-population biased random-key genetic algorithm for a container loading problem. Comput. Oper. Res. 39(2), 179–190 (2012)
17.
Gonçalves, J.F., Mendes, J.J.M., Resende, M.G.C.: A genetic algorithm for the resource constrained multi-project scheduling problem. Eur. J. Oper. Res. 189, 1171–1190 (2009)
18.
Gonçalves, J.F., Costa, M.D., Resende, M.G.C.: A biased random-key genetic algorithm for the minimization of open stacks problem. Int. Trans. Oper. Res. (2014, to appear) DOI: 10.1111/itor.12109
19.
Maravelias, C.T., Grossmann, I.E.: Simultaneous planning for new product development and batch manufacturing facilities. Ind. Eng. Chem. Res. 40(26), 6147–6164 (2001)
20.
Mastor, A.A.: An experimental investigation and comparative evaluation of production line balancing techniques. Manag. Sci. 16(11), 728–746 (1970)
21.
Miguel, J.L., Schaefer, E., Reklaitis, G.V.: Challenges and opportunities in enterprise-wide optimization in the pharmaceutical industry. Comput. Chem. Eng. 47, 19–28 (2012)
22.
Ranjbar, M.: A branch-and-bound algorithm for scheduling of new product development projects. Int. Trans. Oper. Res. 20(2), 251–266 (2013)
23.
Ranjbar, M., Davari, M.: An exact method for scheduling of the alternative technologies in r&d projects. Comput. Oper. Res. 40(1), 395–405 (2013)
24.
Roque, L.A.C., Fontes, D.B.M.M., Fontes, F.A.C.C.: A hybrid biased random key genetic algorithm approach for the unit commitment problem. J. Comb. Opt. 28(1), 140–166 (2014)
25.
Schmidt, C.W., Grossmann, I.E.: Optimization models for the scheduling of testing tasks in new product development. Ind. Eng. Chem. Res. 35(10), 3498–3510 (1996)
26.
Schmidt, C.W., Grossmann, I.E., Blau, G.E.: Optimization of industrial scale scheduling problems in new product development. Comput. Chem. Eng. 22, S1027–S1030 (1998)
27.
Sobek, D.K., Ward, A.C., Liker, J.K.: Toyota’s principles of set-based concurrent engineering. Sloan Manag. Rev. 40(2), 67–84 (1999)
28.
Sommer, S.C., Loch, C.H.: Selectionism and learning in projects with complexity and unforeseeable uncertainty. Manag. Sci. 50(10), 1334–1347 (2004)
29.
Spears, W.M., Dejong, K.A.: On the virtues of parameterized uniform crossover. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 230–236, 1991
Metadata
Title
A Genetic Algorithm for Scheduling Alternative Tasks Subject to Technical Failure
Authors
Dalila B. M. M. Fontes
José Fernando Gonçalves
Copyright Year
2015
Book
Optimization, Control, and Applications in the Information Age

Print ISBN: 978-3-319-18566-8

Electronic ISBN: 978-3-319-18567-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-18567-5_7

Premium Partner

    Image Credits