Skip to main content
SpringerLink
Log in

Service provider portfolio selection for project management using a BP neural network

  • S.I.: Artificial Intelligence in Operations Management
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Service provider portfolio selection (SPPS) can be a major challenge for organizations to achieve project success. Hence, organizations need to decide on which service provider portfolio (SPP) is appropriate for project management (PM). However, there has been limited research on how to select a SPP in PM. To address this research gap, we establish a novel model for SPPS based on a BP neural network integrated with entropy-AHP from the perspective of the comprehensive economic benefit. This model employs a BP neural network due to its robustness and memory and nonlinear mapping abilities. Furthermore, we implement the proposed model for a construction project to verify the effectiveness. Our results indicate that the model performs well with a prediction accuracy of 97%. Moreover, the model is confirmed to be robust as it still achieves high prediction accuracy when the input data are disturbed randomly.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Ai, F. Y., & Wang, M. Z. (2018). Economic benefit evaluation of industrial enterprises based on BP neural network optimization algorithm. Latin American Applied Research, 48(3), 223–227.

    Article  Google Scholar 

  • Amenta, P., Ishizaka, A., Lucadamo, A., Marcarelli, G., & Vyas, V. (2020). Computing a common preference vector in a complex multi-actor and multi-group decision system in Analytic Hierarchy Process context. Annals of Operations Research, 284(1), 33–62. https://doi.org/10.1007/s10479-019-03258-3.

    Article  Google Scholar 

  • Ayhan, B. U., & Tokdemir, O. B. (2019). Safety assessment in megaprojects using artificial intelligence. Safety Science, 118, 273–287. https://doi.org/10.1016/j.ssci.2019.05.027.

    Article  Google Scholar 

  • Bian, J., Liao, Y., Wang, Y. Y., & Tao, F. (2020). Analysis of firm CSR strategies. European Journal of Operational Research.

  • Busch, A., Pape, U., & Ruther, M. (2002). Enterprise application integration-success factor for service provider. Industrie Management, 18(1), 40–43.

    Google Scholar 

  • Buyukkaramikli, N. C., van Ooijen, H. P. G., & Bertrand, J. W. M. (2015). Integrating inventory control and capacity management at a maintenance service provider. Annals of Operations Research, 231(1), 185–206. https://doi.org/10.1007/s10479-013-1506-1.

    Article  Google Scholar 

  • Buyukozkan, G., Karabulut, Y., & Arsenyan, J. (2017). RFID service provider selection: An integrated fuzzy MCDM approach. Measurement, 112, 88–98. https://doi.org/10.1016/j.measurement.2017.08.018.

    Article  Google Scholar 

  • Buyukozkan, G., Ruan, D., & Feyzioglu, O. (2007). Evaluating e-learning Web site quality in a fuzzy environment. International Journal of Intelligent Systems, 22(5), 567–586. https://doi.org/10.1002/int.20214.

    Article  Google Scholar 

  • Cai, X., Qian, Y., Bai, Q., & Liu, W. (2020). Exploration on the financing risks of enterprise supply chain using Back Propagation neural network. Journal of Computational and Applied Mathematics. https://doi.org/10.1016/j.cam.2019.112457.

    Article  Google Scholar 

  • Cao, Q., Parry, M. E., & Leggio, K. B. (2011). The three-factor model and artificial neural networks: Predicting stock price movement in China. Annals of Operations Research, 185(1), 25–44. https://doi.org/10.1007/s10479-009-0618-0.

    Article  Google Scholar 

  • Carlini, N., & Wagner, D. (2016). Towards evaluating the robustness of neural networks. arXiv, 10 pp.-10 pp.

  • Chai, J., Liu, J. N. K., & Ngai, E. W. T. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40(10), 3872–3885. https://doi.org/10.1016/j.eswa.2012.12.040.

    Article  Google Scholar 

  • Charalambous, C., Charitou, A., & Kaourou, F. (2000). Comparative analysis of artificial neural network models: Application in bankruptcy prediction. Annals of Operations Research, 99, 403–425. https://doi.org/10.1023/a:1019292321322.

    Article  Google Scholar 

  • Chen, C. (2020). A novel multi-criteria decision-making model for building material supplier selection based on entropy-AHP weighted TOPSIS. Entropy. https://doi.org/10.3390/e22020259.

    Article  Google Scholar 

  • Chen, T., Shen, D., Jin, Y., Li, H., Yu, Z., Feng, H., et al. (2017). Comprehensive evaluation of environ-economic benefits of anaerobic digestion technology in an integrated food waste-based methane plant using a fuzzy mathematical model. Applied Energy, 208, 666–677. https://doi.org/10.1016/j.apenergy.2017.09.082.

    Article  Google Scholar 

  • Choi, J., & Contractor, F. J. (2019). Improving the progress of research & development (R&D) projects by selecting an optimal alliance structure and partner type. British Journal of Management, 30(4), 791–809. https://doi.org/10.1111/1467-8551.12267.

    Article  Google Scholar 

  • Cui, K., & Jing, X. (2019). Research on prediction model of geotechnical parameters based on BP neural network. Neural Computing & Applications, 31(12), 8205–8215. https://doi.org/10.1007/s00521-018-3902-6.

    Article  Google Scholar 

  • Fahimullah, M., Faheem, Y., & Ahmad, N. (2019). Collaboration formation and profit sharing between software development firms: A shapley value based cooperative game. IEEE Access, 7, 42859–42873. https://doi.org/10.1109/access.2019.2908459.

    Article  Google Scholar 

  • Fang, W., & Ji, W. (2011). Study on selection of hydropower development project partner based on set pair analysis theory. Water Resources and Power, 29(12), 122–123.

    Google Scholar 

  • Gao, J., Zheng, Y., & Li, J. (2018). Comprehensive evaluation of economic benefits of pumped storage power plants by improved order relation analysis method. Journal of Yangtze River Scientific Research Institute, 35(4), 137–142.

    Google Scholar 

  • Gardas, B. B., Raut, R. D., Jagtap, A. H., & Yadav, P. (2019). Service provider’s rationalisation for the performance improvement of the organisation: A case study. International Journal of Productivity and Quality Management, 26(1), 21–33.

    Article  Google Scholar 

  • Gireesha, O., Somu, N., Krithivasan, K., & Sriram, V. S. S. (2020). IIVIFS-WASPAS: An integrated multi-criteria decision-making perspective for cloud service provider selection. Future Generation Computer Systems-the International Journal of Escience, 103, 91–110. https://doi.org/10.1016/j.future.2019.09.053.

    Article  Google Scholar 

  • Guo, Q. (2011). Appraise of Industry’s enterprises’ economic benefit based on unascertained measure theory. Mathematics in Practice and Theory, 41(2), 45–50.

    Google Scholar 

  • Guzman-Barcenas, J., Alfredo Hernandez, J., Arias-Martinez, J., Baptista-Gonzalez, H., Ceballos-Reyes, G., & Irles, C. (2016). Estimation of umbilical cord blood leptin and insulin based on anthropometric data by means of artificial neural network approach: Identifying key maternal and neonatal factors. BMC Pregnancy and Childbirth. https://doi.org/10.1186/s12884-016-0967-z.

    Article  Google Scholar 

  • Hu, M. Y., Hung, M. S., Patuwo, B. E., & Shanker, M. S. (1999). Estimating the performance of Sino-Hong Kong joint ventures using neural network ensembles. Annals of Operations Research, 87, 213–232. https://doi.org/10.1023/a:1018928902137.

    Article  Google Scholar 

  • Jain, B. A., & Nag, B. N. (1998). A neural network model to predict long-run operating performance of new ventures. Annals of Operations Research, 78, 83–110. https://doi.org/10.1023/a:1018910402737.

    Article  Google Scholar 

  • Ji, Y., Chang, S., Qi, Y., Li, Y., Li, H. X., & Qi, K. (2019). A BIM-Based Study on the Comprehensive benefit analysis for prefabricated building projects in China. Advances in Civil Engineering,. https://doi.org/10.1155/2019/3720191.

    Article  Google Scholar 

  • Kasap, N., Turan, H. H., Savran, H., Tektas-Sivrikaya, B., & Delen, D. (2018). Provider selection and task allocation in telecommunications with QoS degradation policy. Annals of Operations Research, 263(1–2), 311–337. https://doi.org/10.1007/s10479-016-2213-5.

    Article  Google Scholar 

  • Kim, C. H., & Kim, Y. C. (2020). Application of artificial neural network over nickel-based catalyst for combined steam-carbon dioxide of methane reforming (CSDRM). Journal of Nanoscience and Nanotechnology, 20(9), 5716–5719. https://doi.org/10.1166/jnn.2020.17627.

    Article  Google Scholar 

  • Kim, S., Ramkumar, M., & Subramanian, N. (2019). Logistics service provider selection for disaster preparation: A socio-technical systems perspective. Annals of Operations Research, 283(1–2), 1259–1282. https://doi.org/10.1007/s10479-018-03129-3.

    Article  Google Scholar 

  • Kordova, S., Katz, E., & Frank, M. (2019). Managing development projects-The partnership between project managers and systems engineers. Systems Engineering, 22(3), 227–242. https://doi.org/10.1002/sys.21474.

    Article  Google Scholar 

  • Lazarevska, M., Knezevic, M., Cvetkovska, M., & Trombeva-Gavriloska, A. (2014). Application of artificial neural networks in civil engineering. Tehnicki Vjesnik-Technical Gazette, 21(6), 1353–1359.

    Google Scholar 

  • Leow, L. K., Chew, L.-L., Chong, V. C., & Dhillon, S. K. (2015). Automated identification of copepods using digital image processing and artificial neural network. BMC Bioinformatics,. https://doi.org/10.1186/1471-2105-16-s18-s4.

    Article  Google Scholar 

  • Liu, H., He, Y., Hu, Q., Guo, J., & Luo, L. (2020). Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization. Plos One. https://doi.org/10.1371/journal.pone.0235980.

    Article  Google Scholar 

  • Liu, L., & Ran, W. (2020). Research on supply chain partner selection method based on BP neural network. Neural Computing & Applications, 32(6), 1543–1553. https://doi.org/10.1007/s00521-019-04136-6.

    Article  Google Scholar 

  • Ma, F., Sun, T., Liu, L., & Jing, H. (2020). Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network. Future Generation Computer Systems-the International Journal of Escience, 111, 17–26. https://doi.org/10.1016/j.future.2020.04.036.

    Article  Google Scholar 

  • Matias, T., Souza, F., Araujo, R., & Antunes, C. H. (2014). Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine. Neurocomputing, 129, 428–436. https://doi.org/10.1016/j.neucom.2013.09.016.

    Article  Google Scholar 

  • Melander, L., & Tell, F. (2019). Inter-firm and intra-firm coordination of buyer-supplier collaborations in new product development under conflicts of interest. Journal of Business & Industrial Marketing, 34(4), 850–861. https://doi.org/10.1108/jbim-09-2017-0216.

    Article  Google Scholar 

  • Melchiorre, C., Matteucci, M., & Remondo, J. (2006). Artificial Neural Networks and robustness analysis in landslide susceptibility zonation. In 2006 IEEE international joint conference on neural network proceedings, vols 1–10 (pp. 4375, IEEE International Joint Conference on Neural Networks (IJCNN)).

  • Oki, E., Kaneko, R., Kitsuwan, N., Kurimoto, T., & Urushidani, S. (2017). Cloud provider selection models for cloud storage services to satisfy availability requirements. IEICE Transactions on Communications, E100B(8), 1406–1418. https://doi.org/10.1587/transcom.2016EBP3403.

    Article  Google Scholar 

  • Project Management Institute. (2013). A guide to the project management body of knowledge (PMBOK Guide). Fifth Ed., vol. 44, Pennsylvania: Project Management Institute.

  • Ren, W., Wu, K., Gu, Q., & Hu, Y. (2020). Intelligent decision making for service providers selection in maintenance service network: An adaptive fuzzy-neuro approach. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2019.105263.

    Article  Google Scholar 

  • Saaty, T. L. (1978). Modelling unstructured decision problems-the theory of analytical hierarchies. Mathematics and Computers in Simulation, 20(3), 147–158. https://doi.org/10.1016/0378-4754(78)90064-2.

    Article  Google Scholar 

  • Saidi, A., & Mirzaei, M. (2014). Application of gold-labeled antibody biosensor in simultaneous determination of total aflatoxins using artificial neural network. Journal of the Iranian Chemical Society, 11(2), 391–398. https://doi.org/10.1007/s13738-013-0310-9.

    Article  Google Scholar 

  • Satyanarayana, G., Naidu, G. S., & Babu, N. H. (2018). Artificial neural network and regression modelling to study the effect of reinforcement and deformation on volumetric wear of red mud nano particle reinforced aluminium matrix composites synthesized by stir casting. Boletin De La Sociedad Espanola De Ceramica Y Vidrio, 57(3), 91–100. https://doi.org/10.1016/j.bsecv.2017.09.006.

    Article  Google Scholar 

  • Siddiqui, Z. A., & Tyagi, K. (2018). Study on service selection effort estimation in service oriented architecture-based applications powered by information entropy weight fuzzy comprehensive evaluation model. IET Software, 12(2), 76–84. https://doi.org/10.1049/iet-sen.2016.0141.

    Article  Google Scholar 

  • Singh, R. K., Gunasekaran, A., & Kumar, P. (2018). Third party logistics (3PL) selection for cold chain management: A fuzzy AHP and fuzzy TOPSIS approach. Annals of Operations Research, 267(1–2), 531–553. https://doi.org/10.1007/s10479-017-2591-3.

    Article  Google Scholar 

  • Tijanic, K., Car-Pusic, D., & Sperac, M. (2020). Cost estimation in road construction using artificial neural network. Neural Computing & Applications, 32(13), 9343–9355. https://doi.org/10.1007/s00521-019-04443-y.

    Article  Google Scholar 

  • Vamvakas, P., Tsiropoulou, E. E., & Papavassiliou, S. (2018). Dynamic provider selection & power resource management in competitive wireless communication markets. Mobile Networks & Applications, 23(1), 86–99. https://doi.org/10.1007/s11036-017-0885-y.

    Article  Google Scholar 

  • Wang, H.-M., Xing, M., & Duan, C.-M. (2010). Study on post-evaluation of economic benefits for power plant construction project based on fuzzy neural network. East China Electric Power, 38(9), 1428–1431.

    Google Scholar 

  • Wang, J., & Deng, X. (2020). Comprehensive economic benefit evaluation method of coastal enterprises based on AHP. Journal of Coastal Research. https://doi.org/10.2112/si103-006.1.

    Article  Google Scholar 

  • Wang, S., Zhang, N., Wu, L., & Wang, Y. (2016). Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renewable Energy, 94, 629–636. https://doi.org/10.1016/j.renene.2016.03.103.

    Article  Google Scholar 

  • Wang, X. S., Peng, G. Z., & Cheng, Y. H. (2003). Fuzzy synthetic evaluation of economic benefit of enterprise based on neural network. In 2003 IEEE International symposium on computational intelligence in robotics and automation, vols I-Iii, Proceedings.

  • Wang, Y.-R., & Gibson, G. E., Jr. (2010). A study of preproject planning and project success using ANNs and regression models. Automation in Construction, 19(3), 341–346. https://doi.org/10.1016/j.autcon.2009.12.007.

    Article  Google Scholar 

  • Wu, Y., Tao, Y., Deng, Z., Zhou, J., Xu, C., & Zhang, B. (2020). A fuzzy analysis framework for waste incineration power plant comprehensive benefit evaluation from refuse classification perspective. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2020.120734.

    Article  Google Scholar 

  • Xia, R., Huang, X., & Li, M. (2016). Starch foam material performance prediction based on a radial basis function artificial neural network trained by bare-bones particle swarm optimization with an adaptive disturbance factor. Journal of Applied Polymer Science. https://doi.org/10.1002/app.44252.

    Article  Google Scholar 

  • Yan, T., Yang, Y., Dooley, K., & Chae, S. (2020). Trading-off innovation novelty and information protection in supplier selection for a new product development project: Supplier ties as signals. Journal of Operations Management. https://doi.org/10.1002/joom.1079.

    Article  Google Scholar 

  • Yang, J., Su, J., & Song, L. (2019). Selection of manufacturing enterprise innovation design project based on consumer’s green preferences. Sustainability. https://doi.org/10.3390/su11051375.

    Article  Google Scholar 

  • Zhang, H., Li, Y., & Zhang, H. (2019). Risk early warning safety model for sports events based on back propagation neural network machine learning. Safety Science, 118, 332–336. https://doi.org/10.1016/j.ssci.2019.05.011.

    Article  Google Scholar 

  • Zhao, Y., Xiang, J., Xu, J., Li, J., & Zhang, N. (2019). Study on the comprehensive benefit evaluation of transnational power networking projects based on multi-project stakeholder perspectives. Energies. https://doi.org/10.3390/en12020249.

    Article  Google Scholar 

  • Zhong, Y., & Wu, X. (2020). Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects. Plos One. https://doi.org/10.1371/journal.pone.0229739.

    Article  Google Scholar 

  • Zhou, X., Hu, Y., Deng, Y., Chan, F. T. S., & Ishizaka, A. (2018). A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP. Annals of Operations Research, 271(2), 1045–1066. https://doi.org/10.1007/s10479-018-2769-3.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Economics and Management, Chang’an University, Xi’an, 710061, Shaanxi, China

    Libiao Bai, Kanyin Zheng, Zhiguo Wang & Jiale Liu

Authors
  1. Libiao Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kanyin Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiguo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiale Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Libiao Bai.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bai, L., Zheng, K., Wang, Z. et al. Service provider portfolio selection for project management using a BP neural network. Ann Oper Res 308, 41–62 (2022). https://doi.org/10.1007/s10479-020-03878-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-020-03878-0

Keywords

Search

Navigation