Abstract
Analysing and managing workflows across organisations can be a complex task. One of the main challenges is that planners often have a limited overview of shared resources and dependencies in collaborative workflows that extend beyond their local context. In this paper, we use an emergency handling workflow across multiple organisations as a case study to demonstrate how our recently developed formal language \(\mathcal {R}{\textsc {pl}}\) and its accompanying tool \(\mathcal {R}{\textsc {pl}}\textrm{Tool}\) can be adopted to facilitate decision making on resource allocation in cross-organisational workflows. We formally model the workflow in \(\mathcal {R}{\textsc {pl}}\), and simulate the model with multiple concurrency levels and resource configurations regarding their availability and efficiency using \(\mathcal {R}{\textsc {pl}}\textrm{Tool}\), allowing the decision-makers to observe how the changes in these scenarios impact the behaviour and the performance of the workflow.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
van der Aalst, W.M., ter Hofstede, A.H.: YAWL: yet another workflow language. Inf. Syst. 30(4), 245–275 (2005). https://doi.org/10.1016/j.is.2004.02.002
Agha, G.: Actors: A Model of Concurrent Computation in Distributed Systems. MIT Press (1986). https://doi.org/10.7551/mitpress/1086.001.0001
Ahuja, H.N., Nandakumar, V.: Simulation model to forecast project completion time. J. Constr. Eng. Manag. 111(4), 325–342 (1985). https://doi.org/10.1061/(ASCE)0733-9364(1985)111:4(325)
Ali, M.R., Lamo, Y., Pun, V.K.I: Cost analysis for a resource sensitive workflow modelling language. Sci. Comput. Program. 225, 102896 (2023). https://doi.org/10.1016/j.scico.2022.102896
Ali, M.R., Pun, V.K.I: Cost analysis for an actor-based workflow modelling language. In: Campos, S., Minea, M. (eds.) SBMF 2021. LNCS, vol. 13130, pp. 104–121. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92137-8_7
Ali, M.R., Pun, V.K.I: A static analyser for resource sensitive workflow models. In: David, C., Sun, M. (eds.) TASE 2023. LNCS, vol. 13931, pp. 305–312. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35257-7_18
Badiru, A.B.: A simulation approach to PERT network analysis. Simulation 57(4), 245–255 (1991). https://doi.org/10.1177/003754979105700409
Boctor, F.F.: Heuristics for scheduling projects with resource restrictions and several resource-duration modes. Int. J. Prod. Res. 31(11), 2547–2558 (1993). https://doi.org/10.1080/00207549308956882
Chan, W.T., Chua, D.K.H., Kannan, G.: Construction resource scheduling with genetic algorithms. J. Constr. Eng. Manag. 122(2), 125–132 (1996). https://doi.org/10.1061/(ASCE)0733-9364(1996)122:2(125)
Debois, S., Hildebrandt, T., Sandberg, L.: Experience report: constraint-based modelling and simulation of railway emergency response plans. Procedia Comput. Sci. 83, 1295–1300 (2016). https://doi.org/10.1016/j.procs.2016.04.269, the 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016)/The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016)/Affiliated Workshops
van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005). https://doi.org/10.1007/11494744_25
Durán, F., Falcone, Y., Rocha, C., Salaün, G., Zuo, A.: From static to dynamic analysis and allocation of resources for BPMN processes. In: Bae, K. (ed.) WRLA 2022. LNCS, vol. 13252, pp. 3–21. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-12441-9_1
Freitas, A.P., Pereira, J.L.: Process simulation support in bpm tools: the case of BPMN (2015). https://api.semanticscholar.org/CorpusID:61670021
Gavish, B., Pirkul, H.: Algorithms for the multi-resource generalized assignment problem. Manag. Sci. 37(6), 695–713 (1991). https://doi.org/10.1287/mnsc.37.6.695
Gholamy, A., Kreinovich, V.: How many Monte-Carlo simulations are needed to adequately process interval uncertainty: an explanation of the smart electric grid-related simulation results. J. Innov. Technol. Educ. 5(1), 1–5 (2018). https://scholarworks.utep.edu/cs_techrep/1214
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Exploring processes and deviations. In: Fournier, F., Mendling, J. (eds.) BPM 2014. LNBIP, vol. 202, pp. 304–316. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15895-2_26
Leemans, S.J.J., Tax, N.: Causal reasoning over control-flow decisions in process models. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds.) CAiSE 2022. LNCS, vol. 13295, pp. 183–200. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-07472-1_11
Leu, S.S., Yang, C.H.: A genetic-algorithm-based resource-constrained construction scheduling system. Constr. Manag. Econ. 17(6), 767–776 (1999). https://doi.org/10.1080/014461999371105
Li, J., González, M., Zhu, Y.: A hybrid simulation optimization method for production planning of dedicated remanufacturing. Int. J. Prod. Econ. 117(2), 286–301 (2009). https://doi.org/10.1016/j.ijpe.2008.11.005
Liu, G.: Petri nets modeling message passing and resource sharing. In: Liu, G. (ed.) Petri Nets, pp. 99–121. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-6309-4_4
Liu, G., Jiang, C., Zhou, M., Xiong, P.: Interactive Petri nets. IEEE Trans. Syst. Man Cybern. Syst. 43(2), 291–302 (2013). https://doi.org/10.1109/TSMCA.2012.2204741
Narendra, T., Agarwal, P., Gupta, M., Dechu, S.: Counterfactual reasoning for process optimization using structural causal models. In: Hildebrandt, T., van Dongen, B.F., Röglinger, M., Mendling, J. (eds.) BPM 2019. LNBIP, vol. 360, pp. 91–106. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26643-1_6
Padilla, E.M., Carr, R.I.: Resource strategies for dynamic project management. J. Constr. Eng. Manag. 117(2), 279–293 (1991). https://doi.org/10.1061/(ASCE)0733-9364(1991)117:2(279)
Pearl, J.: Causality. Cambridge University Press, Cambridge (2009). https://doi.org/10.1017/CBO9780511803161
Ritter, F.E., Schoelles, M.J., Quigley, K.S., Klein, L.C.: Determining the number of simulation runs: treating simulations as theories by not sampling their behavior. In: Rothrock, L., Narayanan, S. (eds.) Human-in-the-Loop Simulations, pp. 97–116. Springer, London (2011). https://doi.org/10.1007/978-0-85729-883-6_5
Stinson, J.P.: A branch and bound algorithm for a general class of resource-constrained scheduling problems. Unpublished Ph.D. Dissertation, University of North Carolina at Chapel Hill (1976). https://ci.nii.ac.jp/ncid/BB03308422
Talbot, F.B.: Resource-constrained project scheduling with time-resource tradeoffs: the nonpreemptive case. Manag. Sci. 28(10), 1197–1210 (1982). https://doi.org/10.1287/mnsc.28.10.1197
Zeng, Q.T., Lu, F.M., Liu, C., Meng, D.C.: Modeling and analysis for cross-organizational emergency response systems using Petri nets. Chin. J. Comput 36(11), 2290–2302 (2013). https://api.semanticscholar.org/CorpusID:63737243
Zeng, Q., Liu, C., Duan, H., Zhou, M.: Resource conflict checking and resolution controller design for cross-organization emergency response processes. IEEE Trans. Syst. Man Cybern. Syst. 50(10), 3685–3700 (2020). https://doi.org/10.1109/TSMC.2019.2906335
Zeng, Q., Sun, S.X., Duan, H., Liu, C., Wang, H.: Cross-organizational collaborative workflow mining from a multi-source log. Decis. Support Syst. 54(3), 1280–1301 (2013). https://doi.org/10.1016/j.dss.2012.12.001
Zhang, H., Li, H.: Simulation-based optimization for dynamic resource allocation. Autom. Constr. 13(3), 409–420 (2004). https://doi.org/10.1016/j.autcon.2003.12.005
Acknowledgements
This work is part of the CroFlow project: Enabling Highly Automated Cross-Organisational Workflow Planning, funded by the Research Council of Norway (grant no. 326249).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 IFIP International Federation for Information Processing
About this paper
Cite this paper
Ali, M.R., Lamo, Y., Pun, V.K.I. (2024). Simulation-Based Decision Support for Cross-Organisational Workflows. In: Castellani, I., Tiezzi, F. (eds) Coordination Models and Languages. COORDINATION 2024. Lecture Notes in Computer Science, vol 14676. Springer, Cham. https://doi.org/10.1007/978-3-031-62697-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-62697-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-62696-8
Online ISBN: 978-3-031-62697-5
eBook Packages: Computer ScienceComputer Science (R0)
