Open Access
RAIRO-Oper. Res.
Volume 55, Number 4, July-August 2021
Page(s) 2093 - 2128
Published online 08 July 2021
  • S. Adhau, M.L. Mittal and A. Mittal, A multi-agent system for distributed multi-project scheduling: an auction-based negotiation approach. Eng. Appl. Artif. Intell. 25 (2012) 1738–1751. [Google Scholar]
  • P.G. Balaji and D. Srinivasan, An introduction to multi-agent systems, edited by D. Srinivasan and L.C. Jain. In: Vol. 310 ofInnovations in Multi-Agent Systems and Applications – 1. Studies in Computational Intelligence. Springer, Berlin, Heidelberg (2010). DOI: 10.1007/978-3-642-14435-61. [Google Scholar]
  • O. Bellenguez and E. Néron, Lower bounds for the multi-skill project scheduling problem with hierarchical levels of skills, edited by E. Burke and M. Trick. In: Vol. 3616 of Practice and Theory of Automated Timetabling V. PATAT 2004. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg (2005) 229–243. [Google Scholar]
  • J. Blazewicz, J.K. Lenstra and A. Kan, Scheduling subject to resource constraints: classification and complexity. Disc. Appl. Math. 5 (1983) 11–24. [Google Scholar]
  • A. Brandolese, A. Brun and A.P. Staudacher, A multi-agent approach for the capacity allocation problem. Int. J. Prod. Econ. 66 (2000) 269–285. [Google Scholar]
  • J. Böcker, J. Lind and B. Zirkler, Using a multi-agent approach to optimize the train coupling and sharing system. Eur. J. Oper. Res. 134 (2001) 242–252. [Google Scholar]
  • J. Cai, Z. Peng, S. Ding and J. Sun, A robust genetic algorithm to solve multi-skill resource constrained project scheduling problem with transfer time and uncertainty skills. In: 2020 IEEE 16th International Conference on Control & Automation (ICCA), Singapore, 9–11 Oct. (2020). DOI: 10.1109/ICCA51439.2020.9264319. [Google Scholar]
  • S. Carpitella, A. Certa, J. Izquierdo and C. Manuela La Fata, k-out-of-n systems: an exact formula for the stationary availability and multi-objective configuration design based on mathematical programming and TOPSIS. J. Comput. Appl. Math. 330 (2018) 1007–1015. [Google Scholar]
  • Y.M. Chen and S.C. Wang, Framework of agent-based intelligence system with two-stage decision-making process for distributed dynamic scheduling. Appl. Soft Comput. 7 (2007) 229–245. [Google Scholar]
  • W. Chen, Y.J. Shi, H.F. Teng, X.P. Lan and L.C. Hu, An efficient hybrid algorithm for resource-constrained project scheduling. Inf. Sci. 180 (2010) 1031–1039. [Google Scholar]
  • R. Chen, C. Liang, D. Gu and J. Leung, A multi-objective model for multi-project scheduling and multi-skilled staff assignment for IT product development considering competency evolution. Int. J. Prod. Res. 55 (2017) 6207–6234. [Google Scholar]
  • G. Confessore, S. Giordani and S. Rismondo, A market-based multi-agent system model for decentralized multi-project scheduling. Ann. Oper. Res. 150 (2007) 115–135. [Google Scholar]
  • I. Correia and F. Saldanha-da-Gama, The impact of fixed and variable costs in a multi-skill project scheduling problem: an empirical study. Comput. Ind. Eng. 72 (2014) 230–238. [Google Scholar]
  • H. Dang Quoc, L.N. The, C.N. Doan and T.P. Thanh, New effective differential evolution algorithm for the project scheduling problem. In: 2020 2nd International Conference on Computer Communication and the Internet (ICCCI), Nagoya, Japan, 26–29 June (2020). DOI: 10.1109/ICCCI49374.2020.9145982. [Google Scholar]
  • H. Dang Quoc, L.N. The, C.N. Doan and T.P. Thanh, New Cuckoo Search algorithm for the resource constrained project scheduling problem. In: 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), Ho Chi Minh City, Vietnam, 14–15 Oct. (2020). DOI: 10.1109/RIVF48685.2020.9140728. [Google Scholar]
  • H. Dang Quoc, L.N. The, C.N. Doan and N. Xiong, Effective evolutionary algorithm for solving the real-resource-constrained scheduling problem. J. Adv. Transp. 2020 (2020). DOI: 10.1155/2020/8897710. [CrossRef] [Google Scholar]
  • H. Dai, W. Cheng and P. Guo, An improved tabu search for multi-skill resource-constrained project scheduling problems under step-deterioration. Arab. J. Sci. Eng. 43 (2018) 3279–3290. [Google Scholar]
  • H. Dai, W. Cheng, W. Yang and Y. Wang, A general variable neighbourhood search for multi-skill resource-constrained project scheduling problem with step-deterioration. Int. J. Ind. Syst. Eng. 34 (2020) 145–164. [Google Scholar]
  • K. Deb, A. Pratap, S. Agrawal and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6 (2000) 182–197. [Google Scholar]
  • Y. Fu, H. Wang, G. Tian, Z. Li and H. Hu, Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm. J. Intell. Manuf. 30 (2019) 2257–2272. [Google Scholar]
  • J. Gao, R. Chen and W. Deng, An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem. Int. J. Prod. Res. 51 (2013) 641–651. [Google Scholar]
  • W.J. Gutjahr, S. Katzensteiner, P. Reiter, C. Stummer and M. Denk, Competence-driven project portfolio selection, scheduling and staff assignment. Cent. Eur. J. Oper. Res. 16 (2008) 281–306. [Google Scholar]
  • V. Hajipour, E. Mehdizadeh and R. Tavakkoli-Moghaddam, A novel Pareto-based multi-objective vibration damping optimization algorithm to solve multi-objective optimization problems. Sci. Iran. Trans. E 21 (2014) 2368–2378. [Google Scholar]
  • D. Han, B. Yang, J. Li, J. Wang, M. Sun and Q. Zhou, A multi-agent-based system for two-stage scheduling problem of offshore project. Adv. Mech. Eng. 9 (2017) 1–17. [Google Scholar]
  • S. Hartmann and D. Briskorn, A survey of variants and extensions of the resource-constrained project scheduling problem. Eur. J. Oper. Res. 207 (2010) 1–14. [Google Scholar]
  • S. Hartmann and R. Kolisch, Experimental investigation of heuristics for resource-constrained project scheduling: an update. Eur. J. Oper. Res. 174 (2006) 23–37. [CrossRef] [Google Scholar]
  • S. He, E. Qi and G. Li, A study on the project scheduling based on multi-agent systems. Math. Pract. Theory 1 (2005) 43–47. [Google Scholar]
  • C. Hermerl and R. Kolisch, Scheduling and staffing multiple projects with a multi-skilled workforce. OR Spectr. 32 (2010) 343–368. [Google Scholar]
  • J. Homberger, A multi-agent system for the decentralized resource-constrained multi-project scheduling problem. Int. Trans. Oper. Res. 14 (2007) 565–599. [Google Scholar]
  • A.H. Hosseinian and V. Baradaran, A multi-objective multi-agent optimization algorithm for the community detection problem. J. Inf. Syst. Telecommun. 6 (2019) 169–179. [Google Scholar]
  • A.H. Hosseinian and V. Baradaran, Detecting communities of workforces for the multi-skill resource-constrained project scheduling problem: a dandelion solution approach. J. Ind. Syst. Eng. (JISE) 12 (2019) 72–99. [Google Scholar]
  • A.H. Hosseinian and V. Baradaran, An evolutionary algorithm based on a hybrid multi-attribute decision making method for the multi-mode multi-skilled resource-constrained project scheduling problem. J. Optim. Ind. Eng. 12 (2019) 155–178. [Google Scholar]
  • A.H. Hosseinian and V. Baradaran, P-GWO and MOFA: two new algorithms for the MSRCPSP with the deterioration effect and financial constraints (case study of a gas treating company). Appl. Intell. 50 (2020) 2151–2176. [Google Scholar]
  • A.H. Hosseinian and V. Baradaran, Modified Pareto archived evolution strategy for the multi-skill project scheduling problem with generalized precedence relations. J. Ind. Eng. Manage. Stud. (JIEMS) 7 (2020) 59–86. [Google Scholar]
  • A.H. Hosseinian, V. Baradaran and M. Bashiri, Modeling of the time-dependent multi-skilled RCPSP considering learning effect: an evolutionary solution approach. J. Model. Manage. (JM2) 14 (2019) 521–558. [Google Scholar]
  • S. Javanmard, B. Afshar-Nadjafi and S.T.A. Niaki, Preemptive multi-skilled resource investment project scheduling problem; mathematical modelling and solution approaches. Comput. Chem. Eng. 96 (2016) 55–68. [Google Scholar]
  • N.R. Jennings, K. Sycara and M. Wooldridge, A roadmap of agent research and development. Auton. Agents Multi-Agent Syst. 1 (1998) 7–38. [Google Scholar]
  • R.L. Kadri and F.F. Boctor, An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: the single mode case. Eur. J. Oper. Res. 265 (2018) 454–462. [CrossRef] [Google Scholar]
  • H. Kazemipoor, R. Tavakkoli-Moghaddam, P. Shahrezaei and A. Azaron, A differential evolution algorithm to solve multi-skilled project portfolio scheduling problems. Int. J. Adv. Manuf. Tech. 64 (2013) 1099–1111. [Google Scholar]
  • G. Knotts and M. Dror, Agent-based project scheduling: computational study of large problems. IIE Trans. 35 (2003) 143–159. [Google Scholar]
  • G. Knotts, M. Dror and B. Hartman, Agent-based project scheduling. IIE Trans. 32 (2000) 387–401. [Google Scholar]
  • D. Krüger and A. Scholl, A heuristic solution framework for the resource constrained (multi-) project scheduling problem with sequence-dependent transfer times. Eur. J. Oper. Res. 197 (2009) 492–508. [Google Scholar]
  • D. Krüger and A. Scholl, Managing and modelling general resource transfers in (multi-) project scheduling. OR Spectr. 32 (2010) 369–394. [Google Scholar]
  • M. Laszczyk and P. Myszkowski, Improved selection in evolutionary multi-objective optimization of multi-skill resource-constrained project scheduling problem. Inf. Sci. 481 (2019) 412–431. [Google Scholar]
  • Y.H. Lee, K. Chatterjee and S.R.T. Kumara, Multi-agent based dynamic resource scheduling for distributed multiple project using a market mechanism. J. Intell. Manuf. 14 (2003) 471–484. [Google Scholar]
  • K.Y. Li and R.J. Willis, An iterative scheduling technique for resource constrained project scheduling. Eur. J. Oper. Res. 56 (1992) 370–379. [CrossRef] [Google Scholar]
  • H. Li and K. Womer, Scheduling projects with multi-skilled personnel by a hybrid MILP/CP benders decomposition algorithm. J. Sched. 12 (2009) 281–298. [CrossRef] [Google Scholar]
  • J. Li, H. Jing and Y.Y. Tang, Multi-agent oriented constraint satisfaction. Artif. Intell. 136 (2002) 101–144. [Google Scholar]
  • J. Lin, L. Zhu and K. Gao, A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem. Expert Syst. App. 140 (2020) 112915. [Google Scholar]
  • S. Liu and C. Wang, Optimizing linear project scheduling with multi-skilled crews. Autom. Construct. 24 (2012) 16–23. [Google Scholar]
  • H.R. Maghsoudlou, B. Afshar-Nadjafi and S.T.A. Niaki, A multi-objective invasive weeds optimization algorithm for solving multi-skill multi-mode resource constrained project scheduling problem. Comput. Chem. Eng. 8 (2016) 157–169. [Google Scholar]
  • H.R. Maghsoudlou, B. Afshar-Nadjafi and S.T.A. Niaki, Multi-skilled project scheduling with level-dependent rework risk; three multi-objective mechanisms based on cuckoo search. Appl. Soft Comput. 54 (2017) 46–61. [Google Scholar]
  • A. Majumder, A. Singh and A. Goyal, Application of response surface methodology for Glucan production from leuconostoc dextranicum and its structural characterization. Carbohydr. Polym. 75 (2009) 150–156. [Google Scholar]
  • S. Martin, D. Ouelhadj, P. Beullens, E. Ozcan, A.A. Juan and E.K. Burke, A multi-agent based cooperative approach to scheduling and routing. Eur. J. Oper. Res. 254 (2016) 169–178. [Google Scholar]
  • E. Mehdizadeh, S.T.A. Niaki and M. Hemati, A bi-objective aggregate production planning problem with learning effect and machine deterioration: modeling and solution. Comput. Oper. Res. 91 (2018) 21–36. [Google Scholar]
  • E. Mehmanchi and S. Shadrokh, Solving a new mixed integer non-linear programming model of the multi-skilled project scheduling problem considering learning and forgetting effect. In: Proceedings of the 2013 IEEE IEEM. Bangkok, Thailand (2013). DOI: 10.1109/IEEM.2013.6962442. [Google Scholar]
  • C. Montoya, O. Bellenguez, E. Pinson and D. Rivera, Branch-and-price approach for the multi-skill project scheduling problem. Optim. Lett. 8 (2014) 1721–1734. [Google Scholar]
  • P. Myszkowski, M. Skowronski, L.P. Olech and K. Oslizlo, Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem. Soft Comput. 19 (2015) 3599–3619. [Google Scholar]
  • P.B. Myszkowski, L.P. Olech, M. Laszczyk and M.E. Skowronski, Hybrid differential evolution and greedy algorithm (DEGR) for solving multi-skill resource-constrained project scheduling problem. Appl. Soft Comput. 62 (2018) 1–14. [Google Scholar]
  • C. Pessan, O. Morineau and E. Neron, Multi-skill project scheduling problem and total productive maintenance. In: Proceedings of 3rd Multidisciplinary International Conference on Scheduling: Theory and Application (MISTA 2007), Paris, France (2007) 608–610. [Google Scholar]
  • J. Poppenborg and S. Knust, A flow-based tabu search algorithm for the RCPSP with transfer times. OR Spectr. 38 (2016) 305–335. [Google Scholar]
  • M. Rabiee, F. Jolai, H. Asefi, P. Fattahi and S. Lim, A biogeography-based optimisation algorithm for a realistic no-wait hybrid flow shop with unrelated parallel machines to minimise mean tardiness. Int. J. Comput. Integr. Manuf. 29 (2016) 1007–1024. [Google Scholar]
  • S.H.A. Rahmati, V. Hajipour and S.T.A. Niaki, A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem. Appl. Soft Comput. 13 (2013) 1728–1740. [Google Scholar]
  • J.R. Schott, Fault tolerant design using single and multicriteria genetic algorithms optimization, Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA (1995). [Google Scholar]
  • B.H. Tabrizi, R. Tavvakoli-Moghaddam and S.F. Ghaderi, A two-phase method for a multi-skilled project scheduling problem with discounted cash flows. Sci. Iran. 21 (2014) 1083–1095. [Google Scholar]
  • F. Tao, Y.J. Laili, L. Zhang and A.Y.C. Nee, QMAEA: a quantum multi-agent evolutionary algorithm for multi-objective combinatorial optimization. Simulation 90 (2014) 182–204. [Google Scholar]
  • Y. Tian, T. Xiong, Z. Liu, P. Deng and L. Wan, Novel feedback-based operators in solving multi-skill resource-constrained project scheduling problem. In: 2020 Chinese Control And Decision Conference (CCDC), Hefei, China, 2020, 22–24 Aug. (2020) 296–301. DOI: 10.1109/CCDC49329.2020.9164711. [Google Scholar]
  • M. Tritschler, A. Naber and R. Kolisch, A hybrid metaheuristic for resource-constrained project scheduling with flexible resource profiles. Eur. J. Oper. Res. 262 (2017) 262–273. [Google Scholar]
  • L. Wang and X.L. Zheng, A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem. Swarm Evol. Comput. 38 (2018) 54–63. [Google Scholar]
  • M. Wooldridge and N.R. Jennings, Intelligent agents: theory and practice. Knowl. Eng. Rev. 10 (1995) 115–152. [Google Scholar]
  • Y. Yan, T. Kuphal and J. Bode, Application of multi-agent system in project management. Int. J. Prod. Econ. 68 (2000) 185–197. [Google Scholar]
  • R. Zamani, A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem. Eur. J. Oper. Res. 229 (2013) 552–559. [Google Scholar]
  • X.L. Zheng and L. Wang, A multi-agent optimization algorithm for resource constrained project scheduling problem. Expert Syst. App. 42 (2015) 6039–6049. [Google Scholar]
  • H. Zheng, L. Wang and X. Zheng, Teaching–learning-based optimization algorithm for multi-skill resource constrained project scheduling problem. Soft Comput. 21 (2015) 1537–1548. [Google Scholar]
  • W.C. Zhong, J. Liu, M.Z. Xue and L.C. Jiao, A multi-agent genetic algorithm for global numerical optimization. IEEE Trans. Syst. Man Cybern. B, Cybern. 34 (2004) 229–244. [Google Scholar]
  • L. Zhu, J. Lin and Z.-J. Wang, A discrete oppositional multi-verse optimization algorithm for multi-skill resource constrained project scheduling problem. Appl. Soft Comput. 85 (2019) 105805. [Google Scholar]
  • E. Zitzler and L. Thiele, Multi-objective optimization using evolutionary algorithms a comparative case study, edited by A.E. Eiben, T. Back, M. Schoenauer and H.P. Schwefel. In: Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V). Berlin, Germany (1998) 292–301. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.