Skip to main content
SpringerLink
Log in

Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

This study develops an enhanced ant colony optimization (E-ACO) meta-heuristic to accomplish the integrated process planning and scheduling (IPPS) problem in the job-shop environment. The IPPS problem is represented by AND/OR graphs to implement the search-based algorithm, which aims at obtaining effective and near-optimal solutions in terms of makespan, job flow time and computation time taken. In accordance with the characteristics of the IPPS problem, the mechanism of ACO algorithm has been enhanced with several modifications, including quantification of convergence level, introduction of node-based pheromone, earliest finishing time-based strategy of determining the heuristic desirability, and oriented elitist pheromone deposit strategy. Using test cases with comprehensive consideration of manufacturing flexibilities, experiments are conducted to evaluate the approach, and to study the effects of algorithm parameters, with a general guideline for ACO parameter tuning for IPPS problems provided. The results show that with the specific modifications made on ACO algorithm, it is able to generate encouraging performance which outperforms many other meta-heuristics.

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

Similar content being viewed by others

References

  • Arnaout, J. P., Musa, R., & Rabadi, G. (2014). A two-stage ant colony optimization algorithm to minimize the makespan on unrelated parallel machines—part ii: Enhancements and experimentations. Journal of Intelligent Manufacturing, 25(1), 43–53.

    Article  Google Scholar 

  • Arnaout, J. P., Rabadi, G., & Musa, R. (2010). A two-stage ant colony optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times. Journal of Intelligent Manufacturing, 21(6), 693–701.

    Article  Google Scholar 

  • Balasubramanian, S., Maturana, F. P., & Norrie, D. H. (1996). Multi-agent planning and coordination for distributed concurrent engineering. International Journal of Cooperative Information Systems, 05(02n03), 153–179.

    Article  Google Scholar 

  • Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2(4), 353–373.

    Article  Google Scholar 

  • Chiang, C. W., Huang, Y. Q., & Wang, W. Y. (2008). Ant colony optimization with parameter adaptation for multi-mode resource-constrained project scheduling. Journal of Intelligent and Fuzzy Systems, 19(4–5), 345–358.

    Google Scholar 

  • Dorigo, M. (1992). Optimization, Learning and Natural Algorithms. Italy: Politecnico di Milano.

    Google Scholar 

  • Dorigo, M., & Gambardella, L. M. (1996). A study of some properties of ant-Q parallel problem solving from nature-PPSN IV. Berlin: Springer.

    Google Scholar 

  • Dorigo, M., Maniezzo, V., & Colorni, A. (1991). The ant system: An autocatalytic optimizing process. Technical report.

  • Gu, P., Balasubramanian, S., & Norrie, D. H. (1997). Bidding-based process planning and scheduling in a multi-agent system. Computers and Industrial Engineering, 32(2), 477–496.

    Article  Google Scholar 

  • Guo, Y. W., Li, W. D., Mileham, A. R., & Owen, G. W. (2009). Applications of particle swarm optimisation in integrated process planning and scheduling. Robotics and Computer-Integrated Manufacturing, 25(2), 280–288.

    Article  Google Scholar 

  • Ho, Y. C., & Moodie, C. L. (1996). Solving cell formation problems in a manufacturing environment with flexible processing and routing capabilities. International Journal of Production Research, 34(10), 2901–2923.

    Article  Google Scholar 

  • Iwata, K., Murotsu, Y., & Oba, F. (1978). Optimization of selection of machine-tool, loading sequence of parts and machining conditions in job-shop type machining systems. Annals of the CIRP, 27, 447–451.

    Google Scholar 

  • Khoshnevis, B., & Chen, Q. M. (1991). Integration of process planning and scheduling functions. Journal of Intelligent Manufacturing, 2(3), 165–175.

    Article  Google Scholar 

  • Kim, Y. K., Kim, J. Y., & Shin, K. S. (2007). An asymmetric multileveled symbiotic evolutionary algorithm for integrated FMS scheduling. Journal of Intelligent Manufacturing, 18(6), 631–645.

    Article  Google Scholar 

  • Kim, Y. K., Park, K., & Ko, J. (2003). A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Computers and Operations Research, 30(8), 1151–1171.

    Article  Google Scholar 

  • Kis, T. (2003). Job-shop scheduling with processing alternatives. European Journal of Operational Research, 151(2), 307–332.

    Article  Google Scholar 

  • Ko, C. S., Kim, T., & Hwang, H. (2001). External partner selection using tabu search heuristics in distributed manufacturing. International Journal of Production Research, 39(17), 3959–3974.

    Article  Google Scholar 

  • Korytkowski, P., Rymaszewski, S., & Wisniewski, T. (2013). Ant colony optimization for job shop scheduling using multi-attribute dispatching rules. International Journal of Advanced Manufacturing Technology, 67(1–4), 231–241.

    Article  Google Scholar 

  • Kumar, M., & Rajotia, S. (2003). Integration of scheduling with computer aided process planning. Journal of Materials Processing Technology, 138(1–3), 297–300.

    Article  Google Scholar 

  • Kumar, R., Tiwari, M. K., & Shankar, R. (2003). Scheduling of flexible manufacturing systems: An ant colony optimization approach. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture, 217(10), 1443–1453.

    Article  Google Scholar 

  • Lawrynowicz, A. (2008). Integration of production planning and scheduling using an expert system and a genetic algorithm. Journal of the Operational Research Society, 59(4), 455–463.

    Article  Google Scholar 

  • Lee, H., & Kim, S.-S. (2001). Integration of process planning and scheduling using simulation based genetic algorithms. The International Journal of Advanced Manufacturing Technology, 18(8), 586–590.

    Article  Google Scholar 

  • Leung, C. W., Wong, T. N., Mak, K. L., & Fung, R. Y. K. (2010). Integrated process planning and scheduling by an agent-based ant colony optimization. Computers and Industrial Engineering, 59(1), 166–180.

    Article  Google Scholar 

  • Li, X., Gao, L., & Li, W. (2012). Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling. Expert Systems with Applications, 39(1), 288–297.

    Article  Google Scholar 

  • Li, X., Shao, X., Gao, L., & Qian, W. (2010). An effective hybrid algorithm for integrated process planning and scheduling. International Journal of Production Economics, 126(2), 289–298.

    Article  Google Scholar 

  • Lin, C. W., Lin, Y. K., & Hsieh, H. T. (2013). Ant colony optimization for unrelated parallel machine scheduling. International Journal of Advanced Manufacturing Technology, 67(1–4), 35–45.

    Article  Google Scholar 

  • Liu, X.-J., Yi, H., & Ni, Z.-H. (2013). Application of ant colony optimization algorithm in process planning optimization. Journal of Intelligent Manufacturing, 24(1), 1–13.

    Article  Google Scholar 

  • Merkle, D., Middendorf, M., & Schmeck, H. (2002). Ant colony optimization for resource-constrained project scheduling. IEEE Transactions on Evolutionary Computation, 6(4), 333–346.

    Article  Google Scholar 

  • Morad, N., & Zalzala, A. (1999). Genetic algorithms in integrated process planning and scheduling. Journal of Intelligent Manufacturing, 10(2), 169–179.

    Article  Google Scholar 

  • Nasr, N., & Elsayed, E. A. (1990). Job shop scheduling with alternative machines. International Journal of Production Research, 28(9), 1595–1609.

    Article  Google Scholar 

  • Palmer, G. J. (1996). A simulated annealing approach to integrated production scheduling. Journal of Intelligent Manufacturing, 7(3), 163–176.

    Article  Google Scholar 

  • Qiao, L. H., & Lv, S. P. (2012). An improved genetic algorithm for integrated process planning and scheduling. International Journal of Advanced Manufacturing Technology, 58(5–8), 727–740.

    Google Scholar 

  • Seo, Y., & Egbelu, P. J. (1996). Process plan selection based on product mix and production volume. International Journal of Production Research, 34(9), 2639–2655.

    Article  Google Scholar 

  • Shao, X. Y., Li, X. Y., Gao, L., & Zhang, C. Y. (2009). Integration of process planning and scheduling—A modified genetic algorithm-based approach. Computers and Operations Research, 36(6), 2082–2096.

    Article  Google Scholar 

  • Stützle, T. (1997). MAX–MIN ant system for quadratic assignment problems: Technical report AIDA-97-4. FB Infomatik, TU Darmstadt, Germany: FG Intellektik.

  • Stützle, T., López-Ibánez, M., Pellegrini, P., Maur, M., de Oca, M. M., Birattari, M., et al. (2012). Parameter adaptation in ant colony optimization. In Autonomous search (pp. 191–215). Springer.

  • Tavares Neto, R., Godinho Filho, M., & da Silva, F. (2013). An ant colony optimization approach for the parallel machine scheduling problem with outsourcing allowed. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0811-5.

  • Tehrani Nik Nejad, H., Sugimura, N., Iwamura, K., & Tanimizu, Y. (2010). Multi agent architecture for dynamic incremental process planning in the flexible manufacturing system. Journal of Intelligent Manufacturing, 21(4), 487–499.

    Article  Google Scholar 

  • Usher, J. M. (2003). Negotiation-based routing in job shops via collaborative agents. Journal of Intelligent Manufacturing, 14(5), 485–499.

    Article  Google Scholar 

  • Ventura, J., & Yoon, S.-H. (2013). A new genetic algorithm for lot-streaming flow shop scheduling with limited capacity buffers. Journal of Intelligent Manufacturing, 24(6), 1185–1196.

    Article  Google Scholar 

  • Wang, J., Fan, X., Zhang, C., & Wan, S. (2014). A graph-based ant colony optimization approach for integrated process planning and scheduling. Chinese Journal of Chemical Engineering, 22(7), 748–753.

    Article  Google Scholar 

  • Weintraub, A., Cormier, D., Hodgson, T., King, R., Wilson, J., & Zozom, A. (1999). Scheduling with alternatives: A link between process planning and scheduling. Iie Transactions, 31(11), 1093–1102.

    Google Scholar 

  • Wong, T. N., Leung, C. W., Mak, K. L., & Fung, R. Y. K. (2006a). An agent-based negotiation approach to integrate process planning and scheduling. International Journal of Production Research, 44(7), 1331–1351.

    Article  Google Scholar 

  • Wong, T. N., Leung, C. W., Mak, K. L., & Fung, R. Y. K. (2006b). Dynamic shopfloor scheduling in multi-agent manufacturing systems. Expert Systems with Applications, 31(3), 486–494.

    Article  Google Scholar 

  • Wong, T. N., Leung, C. W., Mak, K. L., & Fung, R. Y. K. (2006c). Integrated process planning and scheduling/rescheduling—an agent-based approach. International Journal of Production Research, 44(18–19), 3627–3655.

  • Wong, T. N., Zhang, S. C., Wang, G., & Zhang, L. P. (2012). Integrated process planning and scheduling—Multi-agent system with two-stage ant colony optimisation algorithm. International Journal of Production Research, 50(21), 6188–6201.

    Article  Google Scholar 

  • Wu, W.-H., Cheng, S.-R., Wu, C.-C., & Yin, Y. (2012). Ant colony algorithms for a two-agent scheduling with sum-of processing times-based learning and deteriorating considerations. Journal of Intelligent Manufacturing, 23(5), 1985–1993.

    Article  Google Scholar 

  • Zhang, H. (2012a). Ant colony optimization for multimode resource-constrained project scheduling. Journal of Management in Engineering, 28(2), 150–159.

    Article  Google Scholar 

  • Zhang, S. (2012b). An enhanced ant colony optimization approach for integrating process planning and scheduling based on multi-agent system. (Master of Phylosophy M.Phil. thesis), The Uinversity of Hong Kong, Hong Kong SAR, China.

  • Zhu, H. Y., Ye, W. H., & Bei, G. X. (2009, 26–29 Nov. 2009). A particle swarm optimization for integrated process planning and scheduling. Paper presented at the IEEE 10th international conference on computer-aided industrial design and conceptual design, 2009. CAID and CD 2009.

Download references

Acknowledgments

The work described in this paper is fully supported by a grant from the Research Grants Council of Hong Kong (Project Code HKU 718809E).

Author information

Authors and Affiliations

  1. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong

    S. Zhang & T. N. Wong

Authors
  1. S. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. N. Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. N. Wong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, S., Wong, T.N. Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning. J Intell Manuf 29, 585–601 (2018). https://doi.org/10.1007/s10845-014-1023-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-014-1023-3

Keywords

Search

Navigation