Skip to main content
SpringerLink
Log in

Dynamic resource allocation and collaborative scheduling in R&D and manufacturing processes of high-end equipment with budget constraint

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

This paper investigates the collaborative scheduling problem of research and development (R&D) and manufacturing processes in the context of high-end equipment, and the objective is to minimize the makespan. A unique feature is the dual usage of a limited resource budget, which can increase the quantity of both researchers and assembly lines. This brings new challenges to resource allocation and related collaborative scheduling. A mixed-integer programming model is formulated and further simplified based on the network structure, and the CPLEX solver is used to obtain the optimal solutions for the simplified model with the small-scale cases. Furthermore, an improved Variable Neighborhood Search (IVNS) algorithm is developed to obtain the near-optimal solutions for small-scale and large-scale cases. The neighborhood structure operates the R&D and manufacturing parts of solution. Variable neighborhood descent with different amount and sequence of neighborhood structures is designed to enhance the effectiveness and efficiency of the proposed algorithm. Computational experiments are conducted to evaluate the performance of the proposed algorithm. The experimental results show that the proposed algorithm performs well in terms of the solution quality and running time.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Alcaraz, J., Maroto, C.: A robust genetic algorithm for resource allocation in project scheduling[J]. Annal. Op. Res. 102(1), 83–109 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bai, X., Yan, W., Ge, S.S., et al.: An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field[J]. Inf. Sci. 453, 227–238 (2018)

    Article  Google Scholar 

  3. Balouka, N., Cohen, I.: A robust optimization approach for the multi-mode resource-constrained project scheduling problem[J]. Eur. J. Op. Res. 291(2), 457–470 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bektas, Z., Kayalıca, M.O., Kayakutlu, G.: A hybrid heuristic algorithm for optimal energy scheduling of grid-connected micro grids[J]. Energy Syst. 12(4), 877–893 (2021)

    Article  Google Scholar 

  5. Beçsikci, U., Bilge, Ü., Ulusoy, G.: Multi-mode resource constrained multi-project scheduling and resource portfolio problem[J]. Eur. J. Op. Res. 240(1), 22–31 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chakrabortty, R.K., Sarker, R.A., Essam, D.L.: Single mode resource constrained project scheduling with unreliable resources[J]. Op. Res. 20(3), 1369–1403 (2020)

    Google Scholar 

  7. Chand, S., Huynh, Q., Singh, H., et al.: On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems[J]. Inf. Sci. 432, 146–163 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  8. Gafarov, E.R., Lazarev, A.A., Werner, F.: Single machine scheduling problems with financial resource constraints: some complexity results and properties[J]. Math. Soc. Sci. 62(1), 7–13 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  9. Gedik, R., Rainwater, C., Nachtmann, H., et al.: Analysis of a parallel machine scheduling problem with sequence dependent setup times and job availability intervals[J]. Eur. J. Op. Res. 251(2), 640–650 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  10. Ilić, A., UroŠević, D., Brimberg, J., et al.: A general variable neighborhood search for solving the uncapacitated single allocation p-hub median problem[J]. Eur. J. Op. Res. 206(2), 289–300 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  11. Jomaa, W., Eddaly, M., Jarboui, B.: Variable neighborhood search algorithms for the permutation flowshop scheduling problem with the preventive maintenance[J]. Op. Res. 21(4), 2525–2542 (2021)

    Google Scholar 

  12. Kong, M., Pei, J., Liu, X., et al.: Green manufacturing: order acceptance and scheduling subject to the budgets of energy consumption and machine launch[J]. J. Cleaner Prod. 248, 119300 (2020)

    Article  Google Scholar 

  13. Kadri, R.L., Boctor, F.F.: An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: The single mode case[J]. Eur. J. Op. Res. 265(2), 454–462 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  14. Kyriakidis, T.S., Kopanos, G.M., Georgiadis, M.C.: MILP formulations for single-and multi-mode resource-constrained project scheduling problems[J]. Computers Chem. Eng. 36, 369–385 (2012)

    Article  Google Scholar 

  15. Li, J., Ge, B., Jiang, J., et al.: High-end weapon equipment portfolio selection based on a heterogeneous network model[J]. J. Global Optim. 78(4), 743–761 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  16. Lu, S., Pei, J., Liu, X., et al.: A hybrid DBH-VNS for high-end equipment production scheduling with machine failures and preventive maintenance activities[J]. J. Comput. Appl. Math. 384, 113195 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  17. Mejía, G., Yuraszeck, F.: A self-tuning variable neighborhood search algorithm and an effective decoding scheme for open shop scheduling problems with travel/setup times[J]. Eur. J. Op. Res. 285(2), 484–496 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  18. MladenoviĆ, N., Hansen, P.: Variable neighborhood search[J]. Computers Op. Res. 24(11), 1097–1100 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  19. Pei, J., MladenoviĆ, N., UroŠeviĆ, D., et al.: Solving the traveling repairman problem with profits: a novel variable neighborhood search approach[J]. Inf. Sci. 507, 108–123 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  20. Pei, J., Song, Q., Liao, B., et al.: Parallel-machine serial-batching scheduling with release times under the effects of position-dependent learning and time-dependent deterioration[J]. Annal. Op. Res. 298, 407–444 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  21. Ren, Y., Zhang, C., Zhao, F., et al.: An MCDM-based multiobjective general variable neighborhood search approach for disassembly line balancing problem[J]. IEEE Trans. Syst., Man, Cybern.: Syst. 50(10), 3770–3783 (2018)

    Google Scholar 

  22. Shabtay, D., Zofi, M.: Single machine scheduling with controllable processing times and an unavailability period to minimize the makespan[J]. Int. J. Prod. Econ. 198, 191–200 (2018)

    Article  Google Scholar 

  23. Shahidi-Zadeh, B., Tavakkoli-Moghaddam, R., Taheri-Moghadam, A., et al.: Solving a bi-objective unrelated parallel batch processing machines scheduling problem: a comparison study[J]. Computers Op. Res. 88, 71–90 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  24. Słowinski, R.: Multiobjective network scheduling with efficient use of renewable and nonrenewable resources[J]. Eur. J. Op. Res. 7(3), 265–273 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  25. Su, Y., Xie, K., Wang, H., et al.: Airline disruption management: a review of models and solution methods[J]. Engineering 7(4), 435–447 (2021)

    Article  Google Scholar 

  26. Tang, L., Meng, Y.: Data analytics and optimization for smart industry[J]. Front. Eng. Manage. 8(2), 157–171 (2021)

    Article  Google Scholar 

  27. Thevenin, S., Zufferey, N.: Learning variable neighborhood search for a scheduling problem with time windows and rejections[J]. Discr. Appl. Math. 261, 344–353 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  28. Wang, Q., Liu, C., Zheng, L.: A column-generation-based algorithm for a resource-constrained project scheduling problem with a fractional shared resource[J]. Eng. Optim. 5, 871 (2019)

    Google Scholar 

  29. We, J.: On certain models of resource allocation problems[J]. Kybernetes 9(1), 61–66 (1980)

    Article  Google Scholar 

  30. Yang, S., Wang, J., Shi, L., et al.: Engineering management for high-end equipment intelligent manufacturing[J]. Front. Eng. Manage. 5(4), 420–450 (2018)

    Article  Google Scholar 

  31. Yu, H., Gao, Y., Wang, L., et al.: A hybrid particle swarm optimization algorithm enhanced with nonlinear inertial weight and Gaussian mutation for job shop scheduling Problems[J]. Mathematics 8(8), 1355 (2020)

    Article  Google Scholar 

  32. Zandieh, M., Adibi, M.A.: Dynamic job shop scheduling using variable neighbourhood search[J]. Int. J. Prod. Res. 48(8), 2449–2458 (2010)

    Article  MATH  Google Scholar 

  33. Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive[J]. IEEE Trans. Evolutionary Comput. 13(5), 945–958 (2009)

    Article  Google Scholar 

  34. Zhang, X., Tan, Y., Yang, Z.: Joint optimization of the high-end equipment development task process and resource allocation[J]. Nat. Comput. 19(4), 811–823 (2020)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 71922009, 71871080, and 72071057), Innovative Research Groups of the National Natural Science Foundation of China (No. 71521001), and Basic Science Center of the National Natural Science Foundation of China (No. 72188101). This work was also supported by Data Science and Smart Society Governance Laboratory, Ministry of Education. The work of P.M. Pardalos was conducted within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE).

Author information

Authors and Affiliations

  1. School of Management, Hefei University of Technology; Key Laboratory of Process Optimization and Intelligent Decision-making, Research Center for Intelligent Decision-Making & Information System Technologies, Ministry of Education, Hefei, China

    Zhanyin Li, Jun Pei, Ping Yan & Ya Zhou

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  3. Higher School of Economics, LATNA, Nizhny Novgorod, Russia

    Panos M. Pardalos

Authors
  1. Zhanyin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Pei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ping Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ya Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Panos M. Pardalos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Pei.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 3 (PDF 1062 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Pei, J., Yan, P. et al. Dynamic resource allocation and collaborative scheduling in R&D and manufacturing processes of high-end equipment with budget constraint. Optim Lett 17, 955–980 (2023). https://doi.org/10.1007/s11590-022-01886-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-022-01886-6

Keywords

Search

Navigation