Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

SpringerLink
Log in

Multi-objective casting production scheduling problem by a neighborhood structure enhanced discrete NSGA-II: an application from real-world workshop

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Casting production scheduling problem has attracted increasing research attention in recent years to facilitate the profits, efficiency, and environment issues of casting industry. Casting is often characterized by the properties of intensive energy consumption and complex process routes, which motivate the in-depth investigation on construction of practical multi-objective scheduling models and development of effective algorithms. In this paper, for the first time, the multi-objective casting production scheduling problem (MOCPSP) is constructed to simultaneously minimize objectives of defective rate, makespan, and total energy consumption. Moreover, a neighborhood structure enhanced discrete NSGA-II (N-NSGA-II) is designed to better cope with the proposed MOCPSP. In the N-NSGA-II, the advantage of selection mechanism of NSGA-II is fully utilized for selecting non-dominate solution, three neighborhood structures are elaborately designed to strengthen the ability of the local search, and a novel solution generating approach is proposed to increase the diversity of solutions for global search. Finally, a real-world case is illustrated to evaluate the performance of the N-NSGA-II. Computational results show that the proposed N-NSGA-II obtains a wider range of non-dominated solutions with better quality compared to other well-known multi-objective algorithms.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

  • Akturk MS, Ilhan T (2011) Single cnc machine scheduling with controllable processing times to minimize total weighted tardiness. Comput Oper Res 38(4):771–781

    Article  MathSciNet  MATH  Google Scholar 

  • Asefi H, Jolai F, Rabiee M, Araghi MET (2014) A hybrid nsga-ii and vns for solving a bi-objective no-wait flexible flowshop scheduling problem. Int J Adv Manuf Technol 75:1017–1033

    Article  Google Scholar 

  • Atan MO, Akturk MS (2008) Single cnc machine scheduling with controllable processing times and multiple due dates. Int J Prod Res 46(21):6087–6111

    Article  MATH  Google Scholar 

  • Chaudhry IA, Khan AA (2016) A research survey: review of flexible job shop scheduling techniques. Int Trans Oper Res 23(3):551–591

    Article  MathSciNet  MATH  Google Scholar 

  • Che, A., Lv, K., Levner, E., Kats, V.: Energy consumption minimization for single machine scheduling with bounded maximum tardiness. In: 2015 IEEE 12th international conference on networking, sensing and control, pp. 146–150 (2015)

  • Chen X, An Y, Zhang Z, Li Y (2020) An approximate nondominated sorting genetic algorithm to integrate optimization of production scheduling and accurate maintenance based on reliability intervals. J Manuf Syst 54:227–241

    Article  Google Scholar 

  • Cheng R, Gen M, Tsujimura Y (1996) A tutorial survey of job-shop scheduling problems using genetic algorithms-i: representation. Comput Ind Eng 30(4):983–997

    Article  Google Scholar 

  • Dai M, Tang D, Giret A, Salido MA (2019) Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robot Comput-Integrat Manuf 59:143–157

    Article  Google Scholar 

  • Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problems with box constraints. IEEE Trans Evol Comput 18(4):577–601

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Fattahi P, Mehrabad MS, Jolai F (2007) Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. J Intell Manuf 18(3):331–342

    Article  Google Scholar 

  • Gen M, Lin L (2014) Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey. J Intell Manuf 25(5):849–866

    Article  MathSciNet  Google Scholar 

  • Hajej Z, Rezg N, Gharbi A (2018) Quality issue in forecasting problem of production and maintenance policy for production unit. Int J Prod Res 56:6147–6163

    Article  Google Scholar 

  • Jiang S, Zheng Z, Liu M (2018) A preference-inspired multi-objective soft scheduling algorithm for the practical steelmaking-continuous casting production. Comput Ind Eng 115:582–594

    Article  Google Scholar 

  • Kayan RK, Akturk MS (2005) A new bounding mechanism for the cnc machine scheduling problems with controllable processing times. Eur J Oper Res 167(3):624–643

    Article  MathSciNet  MATH  Google Scholar 

  • Khouja M, Mehrez A (1994) Economic production lot size model with variable production rate and imperfect quality. J Oper Res Soc 45:1405–1417

    Article  MATH  Google Scholar 

  • Lei D, Li M, Wang L (2019) A two-phase meta-heuristic for multiobjective flexible job shop scheduling problem with total energy consumption threshold. IEEE Trans Syst Man Cybern 49(3):1097–1109

    Google Scholar 

  • Li J, Pan Q, Liang Y (2010) An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Comput Ind Eng 59(4):647–662

    Article  Google Scholar 

  • Li J, Pan Q, Suganthan PN, Chua TJ (2011) A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem. Int J Adv Manuf Technol 52(5):683–697

    Article  Google Scholar 

  • Liu G, Yang H, Cheng M (2017) A three-stage decomposition approach for energy-aware scheduling with processing-time-dependent product quality. Int J Prod Res 55(11):3073–3091

    Article  Google Scholar 

  • Lu C, Li X, Gao L, Liao W, Yi J (2017) An effective multi-objective discrete virus optimization algorithm for flexible job-shop scheduling problem with controllable processing times. Comput Ind Eng 104:156–174

    Article  Google Scholar 

  • Luo S, Zhang L, Fan Y (2019) Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. J Clean Prod 234:1365–1384

    Article  Google Scholar 

  • Mansouri SA, Aktas E, Besikci U (2016) Green scheduling of a two-machine flowshop: trade-off between makespan and energy consumption. Eur J Oper Res 248(3):772–788

    Article  MathSciNet  MATH  Google Scholar 

  • Marchi B, Zanoni S, Zavanella L, Jaber MY (2019) Supply chain models with greenhouse gases emissions, energy usage, imperfect process under different coordination decisions. Int J Prod Econ 211:145–153

    Article  Google Scholar 

  • Mokhtari H, Hasani A (2017) An energy-efficient multi-objective optimization for flexible job-shop scheduling problem. Comput Chem Eng 104:339–352

    Article  Google Scholar 

  • Moslehi G, Mahnam M (2011) A pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. Int J Prod Econ 129(1):14–22

    Article  Google Scholar 

  • Ouaret S, Kenne J, Gharbi A (2018) Production and replacement policies for a deteriorating manufacturing system under random demand and quality. Eur J Oper Res 264(2):623–636

    Article  MathSciNet  MATH  Google Scholar 

  • Peng T, Xu X (2014) Energy-efficient machining systems: a critical review. Int J Adv Manuf Technol 72(9):1389–1406

    Article  Google Scholar 

  • Piroozfard H, Wong KY, Wong WP (2018) Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resour Conserv Recycl 128:267–283

    Article  Google Scholar 

  • Qin H, Fan P, Tang H, Huang P, Fang B, Pan S (2019) An effective hybrid discrete grey wolf optimizer for the casting production scheduling problem with multi-objective and multi-constraint. Comput Ind Eng 128:458–476

    Article  Google Scholar 

  • Rezaeimalek M, Tavakkolimoghaddam R, Siadat A, Dantan J (2018) A novel model for the integrated planning of part quality inspection and preventive maintenance in a linear-deteriorating serial multi-stage manufacturing system. Int J Adv Manuf Technol 96:3633–3650

    Article  Google Scholar 

  • Shao X, Liu W, Liu Q, Zhang C (2013) Hybrid discrete particle swarm optimization for multi-objective flexible job-shop scheduling problem. Int J Adv Manuf Technol 67:2885–2901

    Article  Google Scholar 

  • Shen X, Han Y, Fu J (2017) Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problems. Soft Comput 21:6531–6554

    Article  Google Scholar 

  • Soto C, Dorronsoro B, Fraire HH, Cruzreyes L, Gomezsantillan C, Rangel N (2020) Solving the multi-objective flexible job shop scheduling problem with a novel parallel branch and bound algorithm. Swarm Evol Comput 53:100632

    Article  Google Scholar 

  • Tambe PP, Mohite S, Kulkarni MS (2013) Optimisation of opportunistic maintenance of a multi-component system considering the effect of failures on quality and production schedule: A case study. Int J Adv Manuf Technol 69(5):1743–1756

    Article  Google Scholar 

  • Tang H, Chen R, Li Y, Peng Z, Guo S, Du Y (2019) Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete pso-sa: an application from a casting workshop. Appl Soft Comput 78:176–194

    Article  Google Scholar 

  • Turkyilmaz, A., Şenvar, O., Unal, I., Bulkan, S.: A research survey: heuristic approaches for solving multi objective flexible job shop problems. J Intell Manuf. pp. 1–35 (2020)

  • Uruk Z, Gultekin H, Akturk MS (2013) Two-machine flowshop scheduling with flexible operations and controllable processing times. Comput Oper Res 40(2):639–653

    Article  MathSciNet  MATH  Google Scholar 

  • Vilcot G, Billaut J (2008) A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem. Eur J Oper Res 190(2):398–411

    Article  MathSciNet  MATH  Google Scholar 

  • Wu X, Shen X, Li C (2019) The flexible job-shop scheduling problem considering deterioration effect and energy consumption simultaneously. Comput Ind Eng 135:1004–1024

    Article  Google Scholar 

  • Xia W, Wu Z (2005) An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Comput Ind Eng 48:409–425

    Article  Google Scholar 

  • Xu W, Cao L (2019) Optimal maintenance control of machine tools for energy efficient manufacturing. Int J Adv Manuf Technol 104(9):3303–3311

    Article  Google Scholar 

  • Yuan Y, Xu H (2015) Multiobjective flexible job shop scheduling using memetic algorithms. IEEE Trans Autom Sci Eng 12(1):336–353

    Article  Google Scholar 

  • Zhang G, Gao L, Shi Y (2011) An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Syst Appl 38(4):3563–3573

    Article  Google Scholar 

  • Zhang G, Shao X, Li P, Gao L (2009) An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput Ind Eng 56(4):1309–1318

    Article  Google Scholar 

  • Zhang G, Zhang L, Song X, Wang Y, Zhou C (2019) A variable neighborhood search based genetic algorithm for flexible job shop scheduling problem. Clust Comput 22(5):11561–11572

    Article  Google Scholar 

  • Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195

    Article  Google Scholar 

  • Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput 3(4):257–271

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their sincere thanks to the referees for their valuable suggestions and comments. The authors would also like to thank Prof. Qian He from Guilin University of Electronic Technology for his help and advice on the revision and proofreading of the manuscript.

Funding

This work is supported by the National Key R & D Program of China (Grant No.2018YFB1308200), National Natural Science Foundation of China (No.62073127)

Author information

Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, 410082, China

    Weihua Tan, Xiaofang Yuan & Yuhui Yang

  2. College of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Lianghong Wu

Authors
  1. Weihua Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaofang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuhui Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lianghong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaofang Yuan.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tan, W., Yuan, X., Yang, Y. et al. Multi-objective casting production scheduling problem by a neighborhood structure enhanced discrete NSGA-II: an application from real-world workshop. Soft Comput 26, 8911–8928 (2022). https://doi.org/10.1007/s00500-021-06697-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06697-y

Keywords

Search

Navigation