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Bi-objective hybrid flow shop scheduling with common due date

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Abstract

In this paper, the problem of hybrid flow shop scheduling with common due dates (HFSCDD) is studied, and the objectives are to minimize the total waiting time and the total earliness/tardiness issues that arise. This study was motivated by a real-life shop floor, with the predefined goal of meeting the requirements of the final product in many manufacturing industries. Where the final product is assembled from multiple components and, the assembly is only initiated when all components of the product are complete in number. These interrelated components have common due dates. In this study, we developed a mathematical model of HFSCDD which made up of “n” jobs that were processed in “m” machines, located on “I” stages by taking into consideration the common due dates. This problem is classified as being NP-hard, and so an efficient modified genetic algorithm is developed to solve it. The proposed modify GA is developed based on the NSGA II method for large sized problems. The results of the proposed algorithm have been compared with PSO and GA algorithms and showed that the proposed algorithm achieved better performance than existing solutions, since the waiting time and the earliness/tardiness are significantly reduced. This is facilitated by the simultaneous production of components for the same product.

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References

  • Asefi H, Jolai F, Rabiee M, Araghi MT (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 

  • Azizi A, Vatankhah Barenji A, Hashmipour M (2016) Optimizing radio frequency identification network planning through ring probabilistic logic neurons. Adv Mech Eng 8:1687814016663476

    Article  Google Scholar 

  • Barenji AV (2013) An RFID-based distributed control system for flexible manufacturing system. Eastern Mediterranean University (EMU)-Doğu Akdeniz Üniversitesi (DAÜ)

  • Barenji AV, Barenji RV, Hashemipour M (2016) Flexible testing platform for employment of RFID-enabled multi-agent system on flexible assembly line. Adv Eng Softw 91:1–11

    Article  Google Scholar 

  • Barenji AV, Barenji RV, Roudi D, Hashemipour M (2017) A dynamic multi-agent-based scheduling approach for SMEs. Int J Adv Manuf Technol 89:3123–3137

    Article  Google Scholar 

  • Cheng R, Gen M, Tsujimura Y (1999) A tutorial survey of job-shop scheduling problems using genetic algorithms, part II: hybrid genetic search strategies. Comput Ind Eng 36:343–364

    Article  Google Scholar 

  • Coello CAC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Springer, New York

    Google Scholar 

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

    Article  Google Scholar 

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995 MHS’95, IEEE, pp 39–43

  • Engin O, Ceran G, Yilmaz MK (2011) An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems. Appl Soft Comput 11:3056–3065

    Article  Google Scholar 

  • Figielska E (2014) A heuristic for scheduling in a two-stage hybrid flowshop with renewable resources shared among the stages. Eur J Oper Res 236:433–444

    Article  Google Scholar 

  • Grabowski J, Pempera J (2000) Sequencing of jobs in some production system. Eur J Oper Res 125:535–550

    Article  Google Scholar 

  • Gupta JN (1988) Two-stage, hybrid flowshop scheduling problem. J Oper Res Soc 39:359–364

    Article  Google Scholar 

  • Hidri L (2016) Note on the hybrid flowshop scheduling problem with multiprocessor tasks. Int J Prod Econ 182:531–534

    Article  Google Scholar 

  • Iyer SK, Saxena B (2004) Improved genetic algorithm for the permutation flowshop scheduling problem. Comput Oper Res 31:593–606

    Article  Google Scholar 

  • Jun S, Park J (2015) A hybrid genetic algorithm for the hybrid flow shop scheduling problem with nighttime work and simultaneous work constraints: a case study from the transformer industry. Expert Syst Appl 42:6196–6204

    Article  Google Scholar 

  • Kim Y-D, Joo B-J, Shin J-H (2009) Heuristics for a two-stage hybrid flowshop scheduling problem with ready times and a product-mix ratio constraint. J Heuristics 15:19–42

    Article  Google Scholar 

  • Lei D, Guo X (2016) Hybrid flow shop scheduling with not-all-machines options via local search with controlled deterioration. Comput Oper Res 65:76–82

    Article  Google Scholar 

  • Li D, Meng X, Liang Q, Zhao J (2015) A heuristic-search genetic algorithm for multi-stage hybrid flow shop scheduling with single processing machines and batch processing machines. J Intell Manuf 26:873–890

    Article  Google Scholar 

  • Liang F, Fung RY (2016) Coordination mechanism in real-time scheduling of virtual cellular manufacturing systems. Proc Inst Mech Eng Part B J Eng Manuf 230:534–547

    Article  Google Scholar 

  • Linn R, Zhang W (1999) Hybrid flow shop scheduling: a survey. Comput Ind Eng 37:57–61

    Article  Google Scholar 

  • Luo H, Du B, Huang GQ, Chen H, Li X (2013) Hybrid flow shop scheduling considering machine electricity consumption cost. Int J Prod Econ 146:423–439

    Article  Google Scholar 

  • Meng T, Pan Q-K, Li J-Q, Sang H-Y (2017) An improved migrating birds optimization for an integrated lot-streaming flow shop scheduling problem. Swarm Evolut Comput 38:64–78

    Article  Google Scholar 

  • Murugan P, Kannan S, Baskar S (2009) NSGA-II algorithm for multi-objective generation expansion planning problem. Electr Power Syst Res 79:622–628

    Article  Google Scholar 

  • Naderi B, Ruiz R, Zandieh M (2010) Algorithms for a realistic variant of flowshop scheduling. Comput Oper Res 37:236–246

    Article  Google Scholar 

  • Pan Q-K, Wang L, Mao K, Zhao J-H, Zhang M (2013) An effective artificial bee colony algorithm for a real-world hybrid flowshop problem in steelmaking process. IEEE Trans Autom Sci Eng 10:307–322

    Article  Google Scholar 

  • Pan Q-K, Wang L, Li J-Q, Duan J-H (2014) A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation. Omega 45:42–56

    Article  Google Scholar 

  • Pan Q-K, Gao L, Li X-Y, Gao K-Z (2017) Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times. Appl Math Comput 303:89–112

    Google Scholar 

  • Rabiee M, Rad RS, Mazinani M, Shafaei R (2014) An intelligent hybrid meta-heuristic for solving a case of no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines. Int J Adv Manuf Technol 71:1229–1245

    Article  Google Scholar 

  • Rahimi-Vahed A, Dangchi M, Rafiei H, Salimi E (2009) A novel hybrid multi-objective shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem. Int J Adv Manuf Technol 41:1227–1239

    Article  Google Scholar 

  • Ruiz R, Vázquez-Rodríguez JA (2010) The hybrid flow shop scheduling problem. Eur J Oper Res 205:1–18

    Article  Google Scholar 

  • Sha D, Lin H-H (2010) A multi-objective PSO for job-shop scheduling problems. Expert Syst Appl 37:1065–1070

    Article  Google Scholar 

  • Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on Evolutionary computation, 1999 CEC 99. IEEE, pp 1945–1950

  • Solimanpur M, Elmi A (2011) A tabu search approach for group scheduling in buffer-constrained flow shop cells. Int J Comput Integr Manuf 24:257–268

    Article  Google Scholar 

  • Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2:221–248

    Article  Google Scholar 

  • Tavakkoli-Moghaddam R, Rahimi-Vahed A, Mirzaei AH (2007) A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: weighted mean completion time and weighted mean tardiness. Inf Sci 177:5072–5090

    Article  Google Scholar 

  • Thuerer M, Stevenson M, Silva C, Land M, Filho MG (2013) Workload control and order release in two-level multi-stage job shops: an assessment by simulation. Int J Prod Res 51:869–882

    Article  Google Scholar 

  • Vatankhah Barenji A, Vatankhah BR (2017) Improving multi-agent manufacturing control system by indirect communication based on ant agents. Proc Inst Mech Eng Part I J Syst Control Eng 231:447–458

    Google Scholar 

  • Wang X, Tang L (2009) A tabu search heuristic for the hybrid flowshop scheduling with finite intermediate buffers. Comput Oper Res 36:907–918

    Article  Google Scholar 

  • Wang S-Y, Wang L, Liu M, Xu Y (2015) An order-based estimation of distribution algorithm for stochastic hybrid flow-shop scheduling problem. Int J Comput Integr Manuf 28:307–320

    Article  Google Scholar 

  • Zhang B, Q-K Pan, Gao L, X-l Zhang, Sang H-Y, Li J-Q (2017) An effective modified migrating birds optimization for hybrid flowshop scheduling problem with lot streaming. Appl Soft Comput 52:14–27

    Article  Google Scholar 

  • Zhou J, Yao X (2017) A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition. Int J Adv Manuf Technol 88:3371–3387

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge that the project was supported by the National Science Foundation of China (Nos. 71501187, 51405089) and the grant from the Humanities and Social Sciences Foundation of the Ministry of Education in China (No. 14YJC630179), and also, the Pearl River S&T Nova Program of Guangzhou (No. 201710010004). Post-doctoral foundation (XXX)

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Zhi Li, Ali Vatankhah Barenji & J. J. Liu

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

    Zhi Li, Ray Y. Zhong & George Q. Huang

  3. Academy of Chinese Energy Strategy, China University of Petroleum-Beijing, No. 18 Fuxue Road, Changping, Beijing, 102249, China

    C. X. Yu

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  1. Zhi Li
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Correspondence to Ali Vatankhah Barenji.

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Li, Z., Zhong, R.Y., Barenji, A.V. et al. Bi-objective hybrid flow shop scheduling with common due date. Oper Res Int J 21, 1153–1178 (2021). https://doi.org/10.1007/s12351-019-00470-8

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  • DOI: https://doi.org/10.1007/s12351-019-00470-8

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