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A sealed bid auction-based two-stage approach for a decentralized multiproject scheduling problem with resource transfers

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Abstract

This study considers the transfer of shared resources among multiple geographically dispersed projects. To formulate this problem, we establish a two-stage decision-making model including the local decision-making stage and the global coordination decision-making stage and develop a two-stage approach (TA) to solve this model. In the local decision-making stage, each project agent (PA) uses a beetle antenna search algorithm (BASA) to generate an initial local schedule to minimize the completion time of each individual project. In the global coordination decision-making stage, a sealed bid auction-based approach with minimizing idle times scheme is developed to transfer the shared resources and to minimize the average delay of multiple projects. The performance of the proposed method is tested on a standard set of 140 problem instances. Computational experiments show that, compared with the branch and bound algorithm and two meta-heuristic algorithms, BASA can obtain high-quality solutions in all project instances. Compared to the existing algorithm for solving the decentralized multiproject scheduling problem with resource transfers, our proposed TA method can obtain lower average project delays and total project makespans on most problem subsets. These new, best results can be used as a benchmark for other methods for solving the same problem.

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References

  1. Lova A, Tormos P (2011) Analysis of scheduling schemes and heuristic rules performance in resource-constrained multi-project scheduling. Ann Oper Res 102(1-4):263–286. https://doi.org/10.1023/A:1010966401888

    MATH  Google Scholar 

  2. Kruger D, Scholl A (2009) A heuristic solution framework for the resource constrained (multi-) project scheduling problem with sequence-dependent transfer times. Eur J Oper Res 197(2):492–508. https://doi.org/10.1016/j.ejor.2008.07.036

    Article  MATH  Google Scholar 

  3. Kruger D, Scholl A (2010) Managing and modeling general resource transfers in (multi-) project scheduling. OR Spectrum 32(2):369–394. https://doi.org/10.1007/s00291-008-0144-5

    Article  MATH  Google Scholar 

  4. Elalouf A, Levner E, Cheng TCE (2013) Routing and dispatching of multiple mobile agents in integrated enterprises. Int J Prod Econ 145(1):96–106. https://doi.org/10.1016/j.ijpe.2013.01.003

    Article  Google Scholar 

  5. Han D, Yang B, Li J, Wang J, Sun M, Zhou Q (2017) A multi-agent-based system for two-stage scheduling problem of offshore project. Adv Mech Eng 9(10):168781401772088. https://doi.org/10.1177/1687814017720882

    Article  Google Scholar 

  6. Jennings NR, Wooldridge M (1995) Applying agent technology. Appl Artif Intell 9(4):357–369. https://doi.org/10.1080/08839519508945480

    Article  Google Scholar 

  7. Lau JS, Huang GQ, Mak KL, Liang L (2006) Agent-based modeling of supply chains for distributed scheduling. IEEE Trans Syst Man Cybern Part A- Syst Hum 36(5):847–861. https://doi.org/10.1109/TSMCA.2005.854231

    Article  Google Scholar 

  8. Leitao P (2009) Agent-based distributed manufacturing control: a state-of-the-art survey. Eng Appl Artif Intell 22(7):979–991. https://doi.org/10.1016/j.engappai.2008.09.005

    Article  Google Scholar 

  9. Bearzotti LA, Salomone E, Chiotti OJ (2012) An autonomous multi-agent approach to supply chain event management. Int J Prod Econ 135(1):468–478. https://doi.org/10.1016/j.ijpe.2011.08.023

    Article  Google Scholar 

  10. Kaplanoglu V (2014) Multi-agent based approach for single machine scheduling withsequence-dependent setup times and machine maintenance. Appl Soft Comput 23:165–179. https://doi.org/10.1016/j.asoc.2014.06.020

    Article  Google Scholar 

  11. Adhau S, Mittal ML, Mittal A (2013) A multi-agent system for decentralized multi-project scheduling with resource transfers. Int J Prod Econ 146(2):646–661. https://doi.org/10.1016/j.ijpe.2013.08.013

    Article  Google Scholar 

  12. Homberger J (2009) A (μ,λ)-coordination mechanism for agent-based multi-project scheduling. OR Spectrum 34 (1):107–132. https://doi.org/10.1007/s00291-009-0178-3

    Article  MathSciNet  Google Scholar 

  13. Deblaere F, Demeulemeester E, Herroelen W (2011) RESCON: Educational Project scheduling software. Comput Appl Eng Educ 19(2):327–336. https://doi.org/10.1002/cae.20314

    Article  MATH  Google Scholar 

  14. Herroelen W, Demeulemeester E, De Reyck B (2001) A note on the paper ”Resource-constrained project scheduling: notation, classification, models and methods” by Brucker others. Eur J Oper Res 128 (3):679–688. https://doi.org/10.1016/S0377-2217(99)00392-6

    Article  MATH  Google Scholar 

  15. Tirkolaee EB, Goli A, Hematian M, Sangaiah AK, Han T (2019) Multi-objective multi-mode resource constrained project scheduling problem using Pareto-based algorithms. Computing 101(6):547–570. https://doi.org/10.1007/s00607-018-00693-1

    Article  MathSciNet  MATH  Google Scholar 

  16. Lotfi R, Yadegari Z, Hosseini SH, Khameneh AH, Weber GW (2020) A robust time-cost-quality-energy-environment trade-off with resource-constrained in project management: a case study for a bridge construction project. J Ind Manag Optim 13(5):1–22. https://doi.org/10.3934/jimo.2020158

    MATH  Google Scholar 

  17. Van Eynde R, Vanhoucke M (2020) Resource-constrained multi-project scheduling: benchmark datasets and decoupled scheduling. J Scheduling 23(8):301–325. https://doi.org/10.1007/s10951-020-00651-w

    Article  MathSciNet  Google Scholar 

  18. Li YY, Lin J, Wang ZJ (2021) Multi-skill resource constrained project scheduling using a multi-objective discrete Jaya algorithm. Appl Intell. https://doi.org/10.1007/s10489-021-02608-8

  19. Yang KK, Sum CC (1993) A comparison of resource allocation and activity scheduling rules in a dynamic multi-project environment. J Oper Manag 11(2):207–218. https://doi.org/10.1016/0272-6963(93)90023-I

    Article  Google Scholar 

  20. Mittal ML, Kanda A (2009) Scheduling of multiple projects with resource transfers. Int J Math Oper Res 1(3):303–325. https://doi.org/10.1504/ijmor.2009.024288

    Article  MATH  Google Scholar 

  21. Poppenborg J, Knust S (2016) A flow-based tabu search algorithm for the RCPSP with transfer times. OR Spectrum 38(2):305–334. https://doi.org/10.1007/s00291-015-0402-2

    Article  MathSciNet  MATH  Google Scholar 

  22. Suresh M, Dutta P, Jain K (2015) Resource constrained Multi-Project scheduling problem with resource transfer times. Asia Pac J Oper Res 32(6):1550048. https://doi.org/10.1142/S0217595915500487

    Article  MathSciNet  MATH  Google Scholar 

  23. Rostami M, Bagherpour M (2020) A lagrangian relaxation algorithm for facility location of resource-constrained decentralized multi-project scheduling problems. Oper Res 20(2):857–897. https://doi.org/10.1007/s12351-017-0358-x

    Google Scholar 

  24. Rostami M, Bagherpour M, Mazdeh MM, Makui A (2017) Resource Pool Location for Periodic Services in Decentralized Multi-project scheduling problems. J Comput Civil Eng 31(5):04017022. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000671

    Article  Google Scholar 

  25. Adhau S, Mittal ML, Mittal A (2012) A multi-agent system for distributed multi-project scheduling: an auction-based negotiation approach. Eng Appl Artif Intell 25(8):1738–1751. https://doi.org/10.1016/j.engappai.2011.12.003

    Article  Google Scholar 

  26. Confessore G, Giordani S, Rismondo S (2007) A market- based multi-agent system model for decentralized multi-project scheduling. Ann Oper Res 150 (1):115–135. https://doi.org/10.1007/s10479-006-0158-9

    Article  MathSciNet  MATH  Google Scholar 

  27. Homberger J (2007) A multi-agent system for the decentralized resource constrained multi-project scheduling problem. Int Trans Oper Res 14(6):565–589. https://doi.org/10.1111/j.1475-3995.2007.00614.x

    Article  MATH  Google Scholar 

  28. Lee YH, Kumara SRT, Chatterjee K (2003) Multiagent based dynamic resource scheduling for distributed multiple projects using a market mechanism. J Intell Manuf 14(5):471–484. https://doi.org/10.1023/A:1025753309346

    Article  Google Scholar 

  29. Li FF, Xu Z (2018) A multi-agent system for distributed multi-project scheduling with two-stage decomposition. PLoS One 13(10):e205445. https://doi.org/10.1371/journal.pone.0205445

    Article  Google Scholar 

  30. Song W, Kang D, Zhang J, Xi H (2017) A multi-unit combinatorial auction based approach for decentralized multi-project scheduling. Auton Agents Multi- Agent Syst 31:1548–1577. https://doi.org/10.1007/s10458-017-9370-z

    Article  Google Scholar 

  31. Tosselli L, Bogado V, Martinez E (2020) A repeated-negotiation game approach to distributed (re) scheduling of multiple projects using decoupled learning. Simul Model Pract Theory 98:101980. https://doi.org/10.1016/j.simpat.2019.101980

    Article  Google Scholar 

  32. Zheng Z, Guo Z, Zhu YN, Zhang XY (2014) A critical chains based distributed multi-project scheduling approach. Neurocomputing 143(16):282–293. https://doi.org/10.1016/j.neucom.2014.04.056

    Article  Google Scholar 

  33. Rahman HF, Chakrabortty RK, Ryan MJ (2020) Memetic algorithm for solving resource constrained project scheduling problems. Autom Constr 111:103052. https://doi.org/10.1016/j.autcon.2019.103052

    Article  Google Scholar 

  34. Chen RM (2011) Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem. Expert Syst Appl 38(6):7102–7111. https://doi.org/10.1016/j.eswa.2010.12.059

    Article  Google Scholar 

  35. Chu Z, Xu Z, Xie F (2019) Experimental evaluation of overlapping strategy for the multimode Resource-Constrained project scheduling problem. Arab J Sci Eng 44(3):2503–2517. https://doi.org/10.1007/s13369-018-3211-5

    Article  Google Scholar 

  36. Liu Z, Yang L, Deng R, Tian J (2017) An effective approach with feasible space decomposition to solve resource-constrained project scheduling problems. Autom Constr 75:1–9. https://doi.org/10.1016/j.autcon.2016.11.012

    Article  Google Scholar 

  37. Elsayed S, Sarker R, Ray T, Coello CC (2017) Consolidated optimization algorithm for resource-constrained project scheduling problems. Inf Sci 418-419:346–362. https://doi.org/10.1016/j.ins.2017.08.023

    Article  Google Scholar 

  38. Chand S, Huynh Q, Singh H, Ray T, Wagner M (2018) On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems. Inf Sci 432:146–163. https://doi.org/10.1016/j.ins.2017.12.013

    Article  MathSciNet  MATH  Google Scholar 

  39. Kurtulus IS, Davis EW (1982) Multi-project scheduling: Categorization of heuristic rules performance. Manage Sci 28(2):161–172. https://doi.org/10.1287/mnsc.28.2.161

    Article  MATH  Google Scholar 

  40. Klein R, Scholl A (2000) PROGRESS: Optimally Solving the generalized resource-constrained project scheduling problem. Math Methods Oper Res 52(3):467–488. https://doi.org/10.1007/s001860000093

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. We are also very grateful to Dr. Dongning Liu and Dr. Feifei Li for spending a lot of time improving the methods, experiments, and language of this article during the revision process. This research was supported by the National Natural Science Foundation of China (Grant number [71571005]).

Funding

This research was supported by the National Natural Science Foundation of China (Grant number [71571005]).

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Conceptualization: [Song Zhao]; Methodology: [Song Zhao]; Formal analysis and investigation: [Song Zhao]; Writing - original draft preparation: [Song Zhao]; Writing - review and editing: [Zhe Xu]; Funding acquisition: [Zhe Xu]; Resources: [Zhe Xu]; Supervision: [Zhe Xu].

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Correspondence to Zhe Xu.

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Zhao, S., Xu, Z. A sealed bid auction-based two-stage approach for a decentralized multiproject scheduling problem with resource transfers. Appl Intell 52, 18081–18100 (2022). https://doi.org/10.1007/s10489-022-03424-4

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