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An ant-based coordination mechanism for resource-constrained project scheduling with multiple agents and cash flow objectives

  • Andreas Fink
  • Jörg Homberger
Article

Abstract

We consider a multi-agent extension of the non-preemptive single-mode resource-constrained project scheduling problem with discounted cash flow objectives. Such a problem setting is related to project scheduling problems which involve different autonomous firms where project activities are uniquely assigned to the project parties (agents). Taking into account opportunistic agents and the resulting information asymmetry we propose a general decentralized negotiation approach which uses ideas from ant colony optimization. In the course of the negotiation the agents iteratively vote on proposed project schedules without disclosing preference information regarding cash flow values. Computational experiments serve to analyze the agent-based coordination mechanism in comparison to other approaches from the literature. The proposed mechanism turns out as an effective method for coordinating self-interested agents with conflicting goals which collaborate in resource-constrained projects.

Keywords

Resource-constrained project scheduling Decentralized coordination Ant colony optimization 

Notes

Acknowledgments

The authors are most grateful for the helpful comments of the anonymous reviewers which have contributed to improve the paper.

References

  1. Abbasi GY, Arabiat YA (2001) A heuristic to maximize the net present value for resource-constrained project scheduling problems. Project Manag J 32:17–24Google Scholar
  2. Błażewicz J, Lenstra JK, Rinnooy Kan AHG (1983) Scheduling subject to resource constraints: classification and complexity. Discret Appl Math 5:11–24MATHCrossRefGoogle Scholar
  3. Brucker P, Drexl A, Möhring R, Neumann K, Pesch E (1999) Resource-constrained project scheduling: notation, classification, models, and methods. Eur J Oper Res 112:3–41MATHCrossRefGoogle Scholar
  4. Bullnheimer B, Hartl RF, Strauss C (1999) A new rank based version of the ant system—a computational study. Cent Eur J Oper Res Econ 7:25–38MathSciNetMATHGoogle Scholar
  5. Chen W, Zhang J, Chung HS, Huang R, Liu O (2010) Optimizing discounted cash flows in project scheduling – an ant colony optimization approach. IEEE Trans Syst Man Cybern Part C (Appl Rev) 40:64–77CrossRefGoogle Scholar
  6. Colorni AM, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. In: Varela FJ, Bourgine P (eds) Proceedings of the first European conference on artificial life. Elsevier, New York, pp 134–142Google Scholar
  7. Debels D, Vanhoucke M (2007) A decomposition-based genetic algorithm for the resource-constrained project scheduling problem. Oper Res 55:457–469MATHCrossRefGoogle Scholar
  8. Deng L, Lin Y, Chen M (2010) Hybrid ant colony optimization for the resource-constrained project scheduling problem. J Syst Eng Electron 21:71–76Google Scholar
  9. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the travelling salesman problems. IEEE Trans Evol Comput 1:53–66CrossRefGoogle Scholar
  10. Ehtamo H, Verkama M, Hämäläinen RP (1999) How to select fair improving directions in a negotiation model over continuous issues. IEEE Trans Syst Man Cybern Part C (Appl Rev) 29:26–33CrossRefGoogle Scholar
  11. Ehtamo H, Kettunen E, Hämäläinen RP (2001) Searching for joint gains in multi-party negotiations. Eur J Oper Res 130:54–69MATHCrossRefGoogle Scholar
  12. Fink A (2004) Supply chain coordination by means of automated negotiations. In: Proceedings of the 37th Hawaii international conference on system sciences, IEEE, 10 p, doi: 10.1109/HICSS.2004.1265206
  13. Fink A (2006) Supply chain coordination by means of automated negotiation between autonomous agents. In: Chaib-draa B, Müller J (eds) Multiagent based supply chain management, studies in computational intelligence, vol 28. Springer, Berlin, pp 351–372CrossRefGoogle Scholar
  14. Fink A (2007) Barwertorientierte Projektplanung mit mehreren Akteuren mittels eines verhandlungs-basierten Koordinationsmechanismus. In: Oberweis A, Weinhardt C, Gimpel H, Koschmider A, Pankratius V, Schnizler B (eds) eOrganisation: Service-, Prozess-, Market-Engineering, vol 2, Universitätsverlag Karlsruhe, pp 465–482Google Scholar
  15. Hartmann S (1998) A competitive genetic algorithm for the resource-constrained project scheduling. Nav Res Logist 45:733–750MathSciNetMATHCrossRefGoogle Scholar
  16. Hartmann S, Briskorn D (2010) A survey of variants and extensions of the resource-constrained project scheduling problem. Eur J Oper Res 207:1–14MathSciNetMATHCrossRefGoogle Scholar
  17. He Z, Xu Y (2008) Multi-mode project payment scheduling problems with bonus-penalty structure. Eur J Oper Res 189:1191–1207MATHCrossRefGoogle Scholar
  18. Herroelen WS, Dommelen PV, Demeulemeester EL (1997) Project network models with discounted cash flows—a guided tour through recent developments. Eur J Oper Res 100:97–121MATHCrossRefGoogle Scholar
  19. Herroelen W, De Reyck B, Demeulemeester E (1998) Resource-constrained project scheduling: a survey of recent developments. Comput Oper Res 25:279–302MathSciNetMATHCrossRefGoogle Scholar
  20. Homberger J (2010) Decentralized multi-level uncapacitated lot-sizing by automated negotiation. 4OR: Q J Oper Res 8:155–180MathSciNetMATHCrossRefGoogle Scholar
  21. Homberger J (2011) A generic coordination mechanism for lot-sizing in supply chains. Electron Commerc Res 11:123–149CrossRefGoogle Scholar
  22. Homberger J (2012) A (μ, λ)-coordination mechanism for agent-based multi-project scheduling. OR Spectr 34:107–132MathSciNetMATHCrossRefGoogle Scholar
  23. Homberger J, Gehring H (2010) A pheromone-based negotiation mechanism for lot-sizing in supply chains. In: Proceedings of the 43rd Hawaii international conference on system sciences, IEEE, 10 p, doi: 10.1109/HICSS.2010.26
  24. Ito T, Hattori H, Klein M (2007) Multi-issue negotiation protocol for agents: Exploring nonlinear utility spaces. In: Proceedings of the twentieth international joint conference on artificial intelligence, pp 1347–1352. Hyderabad, IndiaGoogle Scholar
  25. Johnson DS, Aragon CR, McGeoch LA, Schevon C (1989) Optimization by simulated annealing: an experimental evaluation; part 1, graph partitioning. Oper Res 37:865–892MATHCrossRefGoogle Scholar
  26. Jones GT (2006) Hybrid computational models for the mediated negotiation of complex contracts. In: Proceeding of 14th annual conference of the North American association for computational social and organizational science, Notre Dame, INGoogle Scholar
  27. Kimms A (2001) Maximizing the net present value of a project under resource constraints using a Lagrangian relaxation based heuristic with tight upper bounds. Ann Oper Res 102:221–236MathSciNetMATHCrossRefGoogle Scholar
  28. Klein M, Faratin P, Sayama H, Bar-Yam Y (2003a) Negotiating complex contracts. Group Decis Negot 12:111–125CrossRefGoogle Scholar
  29. Klein M, Faratin P, Sayama H, Bar-Yam Y (2003b) Protocols for negotiating complex contracts. IEEE Intell Syst 18:32–38CrossRefGoogle Scholar
  30. Kolisch R (1996) Serial and parallel resource-constrained project scheduling methods revisited: theory and computation. Eur J Oper Res 90:320–333MATHCrossRefGoogle Scholar
  31. Kolisch R, Hartmann S (2006) Experimental investigation of heuristics for resource-constrained project scheduling: an update. Eur J Oper Res 174:23–37MATHCrossRefGoogle Scholar
  32. Kolisch R, Padman R (2001) An integrated survey of deterministic project scheduling. Omega 29:249–272CrossRefGoogle Scholar
  33. Kolisch R, Sprecher A (1996) PSPLIB—a project scheduling library. Eur J Oper Res 96:205–216CrossRefGoogle Scholar
  34. Lai G, Sycara K (2009) A generic framework for automated multi-attribute negotiation. Group Decis Negot 18:169–187CrossRefGoogle Scholar
  35. Merkle D, Middendorf M, Schmeck H (2000) Pheromone evaluation in ant colony optimization. In: IECON 2000. 26th annual conference of the IEEE Industrial Electronics Society, vol 4, pp 2726–2731Google Scholar
  36. Mika M, Waligora G, Weglarz J (2005) Simulated annealing and tabu search for multi-mode resource-constrained project scheduling with positive discounted cash flows and different payment models. Eur J Oper Res 164:639–668MathSciNetMATHCrossRefGoogle Scholar
  37. Nisan N, Roughgarden T, Tardos E, Vazirani VV (eds) (2007) Algorithmic game theory. Cambridge University Press, New YorkMATHGoogle Scholar
  38. Özdamar L, Ulusoy G (1995) A survey on the resource-constrained project scheduling problem. IIE Trans 27:574–586CrossRefGoogle Scholar
  39. Rubinstein A (1982) Perfect equilibrium in a bargaining model. Econometrica 50:97–109MathSciNetMATHCrossRefGoogle Scholar
  40. Russell AH (1970) Cash flows in networks. Manag Sci 16:357–373MATHCrossRefGoogle Scholar
  41. Russell RA (1986) A comparison of heuristics for scheduling projects with cash flows and resource restrictions. Manag Sci 32:1291–1300CrossRefGoogle Scholar
  42. Shou Y–Y (2006) Ant colony algorithm for scheduling resource constrained projects with discounted cash flows. Proceedings of the fifth IEEE international conference on machine learning and cybernetics, Dalian, pp 176–180Google Scholar
  43. Stadtler H (2009) A framework for collaborative planning and state-of-the-art. OR Spectr 31:5–30MathSciNetMATHCrossRefGoogle Scholar
  44. Stützle T, Hoos H (2000) MAX-MIN ant system. Futur Gener Comput Syst 16:889–914CrossRefGoogle Scholar
  45. Tung HW, Lin RJ (2005) Automated contract negotiation using a mediation service. In: Proceedings of the 7th IEEE international conference on E-commerce technology. Munich, Germany, pp 374–377Google Scholar
  46. Ulusoy G, Özdamar L (1995) A heuristic scheduling algorithm for improving the duration and net present value of a project. Int J Oper Prod Manag 15:89–98CrossRefGoogle Scholar
  47. Vanhoucke M (2010) A scatter search heuristic for maximizing the net present value of a resource-constrained project with fixed activity cash flows. Int J Prod Res 48:1983–2001MATHCrossRefGoogle Scholar
  48. Vanhoucke M, Demeulemeester E, Herroelen W (2001) On maximizing the net present value of a project under renewable resource constraints. Manag Sci 47:1113–1121MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Helmut-Schmidt-University HamburgHamburgGermany
  2. 2.Stuttgart University of Applied SciencesStuttgartGermany

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