Processing, Please wait...

  • Home
  • About Us
  • Search:
  • Advanced Search

Growing Science » Journal of Project Management » NSGA-II simheuristic to solve a multi-objective flexible flow shop problem under stochastic machine breakdowns

Journals

  • IJIEC (678)
  • MSL (2637)
  • DSL (606)
  • CCL (460)
  • USCM (1087)
  • ESM (391)
  • AC (543)
  • JPM (215)
  • IJDS (802)
  • JFS (81)

JPM Volumes

    • Volume 1 (8)
      • Issue 1 (5)
      • Issue 2 (3)
    • Volume 2 (13)
      • Issue 1 (4)
      • Issue 2 (3)
      • Issue 3 (3)
      • Issue 4 (3)
    • Volume 3 (17)
      • Issue 1 (4)
      • Issue 2 (5)
      • Issue 3 (4)
      • Issue 4 (4)
    • Volume 4 (24)
      • Issue 1 (4)
      • Issue 2 (8)
      • Issue 3 (8)
      • Issue 4 (4)
    • Volume 5 (20)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • Volume 6 (20)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • Volume 7 (21)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (6)
    • Volume 8 (21)
      • Issue 1 (6)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • Volume 9 (35)
      • Issue 1 (6)
      • Issue 2 (5)
      • Issue 3 (9)
      • Issue 4 (15)
    • Volume 10 (36)
      • Issue 1 (15)
      • Issue 2 (21)

Keywords

Supply chain management(156)
Jordan(154)
Vietnam(147)
Customer satisfaction(119)
Performance(108)
Supply chain(105)
Service quality(95)
Tehran Stock Exchange(94)
Competitive advantage(91)
SMEs(85)
optimization(81)
Financial performance(81)
Job satisfaction(78)
Factor analysis(78)
Trust(77)
Knowledge Management(76)
Genetic Algorithm(74)
TOPSIS(73)
Social media(72)
Organizational performance(71)


» Show all keywords

Authors

Naser Azad(82)
Mohammad Reza Iravani(64)
Zeplin Jiwa Husada Tarigan(52)
Endri Endri(44)
Muhammad Alshurideh(40)
Hotlan Siagian(36)
Muhammad Turki Alshurideh(35)
Jumadil Saputra(35)
Barween Al Kurdi(32)
Hassan Ghodrati(31)
Mohammad Khodaei Valahzaghard(30)
Ahmad Makui(30)
Dmaithan Almajali(30)
Ni Nyoman Kerti Yasa(29)
Basrowi Basrowi(29)
Shankar Chakraborty(29)
Prasadja Ricardianto(28)
Sulieman Ibraheem Shelash Al-Hawary(27)
Ali Harounabadi(26)
Haitham M. Alzoubi(26)


» Show all authors

Countries

Iran(2149)
Indonesia(1208)
India(762)
Jordan(726)
Vietnam(489)
Malaysia(415)
Saudi Arabia(400)
United Arab Emirates(209)
Thailand(142)
China(130)
United States(100)
Turkey(97)
Ukraine(93)
Egypt(86)
Canada(83)
Pakistan(81)
Nigeria(72)
Peru(70)
United Kingdom(69)
Taiwan(65)


» Show all countries

Journal of Project Management

ISSN 2371-8374 (Online) - ISSN 2371-8366 (Print)
Quarterly Publication
Volume 9 Issue 4 pp. 493-512 , 2024

NSGA-II simheuristic to solve a multi-objective flexible flow shop problem under stochastic machine breakdowns Pages 493-512 Right click to download the paper Download PDF

Authors: Daniel Felipe Rodríguez-Espinosa, Daniela Cruz-Vargas, Daniel Esteban Delgado-Merchán, David Hernando Gonzalez-Estupiñán, Eliana María González-Neir

DOI: 10.5267/j.jpm.2024.6.002

Keywords: Stochastic flexible flow shop, Machine breakdowns, NSGA-II, Tardy jobs, Makespan

Abstract: This study proposes a simheuristic that hybridizes NSGA-II with Monte Carlo simulation to address a stochastic flexible flow shop problem featuring stochastic machine breakdowns. In real-world scenarios, machine breakdowns frequently occur, resulting in negative impacts such as time loss, late deliveries, decreased productivity, and order accumulation. Therefore, this study considers the times between failures and times to repair as stochastic parameters. Multiple objectives are concurrently addressed, including expected makespan, expected tardy jobs, and the standard deviation of tardy jobs. A mathematical model was formulated for the deterministic version of the problem and separately solved for the minimization of tardy jobs and the minimization of makespan in small instances. Subsequently, the proposed simheuristic was executed for both small and large instances. The results demonstrate that the NSGA-II simheuristic enhances outcomes across all objective functions compared to the simulation of optimal solutions provided by the mathematical models in small instances, yielding average GAPs of -16.64%, -21.87%, and -53.33% for expected tardy jobs, expected makespan, and standard deviation of tardy jobs, respectively. Furthermore, the simheuristic outperforms the simulation of solutions given by seven dispatching rules, showcasing average improvements of 48.01%, 48.18%, and 95.63% for the same objectives, respectively.



How to cite this paper
Rodríguez-Espinosa, D., Cruz-Vargas, D., Delgado-Merchán, D., Gonzalez-Estupiñán, D & González-Neir, E. (2024). NSGA-II simheuristic to solve a multi-objective flexible flow shop problem under stochastic machine breakdowns.Journal of Project Management, 9(4), 493-512.

Refrences
Abu-Marrul, V., Martinelli, R., Hamacher, S., & Gribkovskaia, I. (2023). Simheuristic algorithm for a stochastic paral-lel machine scheduling problem with periodic re-planning assessment. Annals of Operations Research, 320(2), 547–572. https://doi.org/10.1007/s10479-022-04534-5
Ahmadi, E., Zandieh, M., Farrokh, M., & Emami, S. M. (2016). A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms. Computers & Operations Research, 73, 56–66. https://doi.org/10.1016/j.cor.2016.03.009
Allahverdi, A., Aydilek, A., & Aydilek, H. (2016). Minimizing the number of tardy jobs on a two-stage assembly flow-shop. Journal of Industrial and Production Engineering, 33(6), 391–403. https://doi.org/10.1080/21681015.2016.1151466
Almeder, C., & Hartl, R. F. (2013). A metaheuristic optimization approach for a real-world stochastic flexible flow shop problem with limited buffer. International Journal of Production Economics, 145(1), 88–95. https://doi.org/10.1016/j.ijpe.2012.09.014
Al-Turki, U. M., Saleh, H., Deyab, T., & Almoghathawi, Y. (2012). Resource Allocation and Job Dispatching for Unre-liable Flexible Flow Shop Manufacturing System. Advanced Materials Research, 445, 947–952. https://doi.org/10.4028/www.scientific.net/AMR.445.947
Azadeh, A., Goodarzi, A. H., Kolaee, M. H., & Jebreili, S. (2018). An efficient simulation–neural network–genetic algo-rithm for flexible flow shops with sequence-dependent setup times, job deterioration and learning effects. Neural Computing and Applications, 6, 1–15. https://doi.org/10.1007/s00521-018-3368-6
Behnamian, J., Fatemi Ghomi, S. M. T., & Zandieh, M. (2009). A multi-phase covering Pareto-optimal front method to multi-objective scheduling in a realistic hybrid flowshop using a hybrid metaheuristic. Expert Systems with Applica-tions, 36(8), 11057–11069. https://doi.org/10.1016/j.eswa.2009.02.080
Brunner, E., Dette, H., & Munk, A. (1997). Box-Type Approximations in Nonparametric Factorial Designs. Journal of the American Statistical Association, 92(440), 1494–1502. https://doi.org/10.1080/01621459.1997.10473671
Caldeira, R. H., & Gnanavelbabu, A. (2021). A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times. SIMULATION, 97(3), 215–236. https://doi.org/10.1177/0037549720968891
Chen, C.-L., & Chen, C.-L. (2009). Bottleneck-based heuristics to minimize total tardiness for the flexible flow line with unrelated parallel machines. Computers & Industrial Engineering, 56(4), 1393–1401. https://doi.org/10.1016/j.cie.2008.08.016
Choi, S. H., & Wang, K. (2012). Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach. Computers & Industrial Engineering, 63(2), 362–373. https://doi.org/10.1016/j.cie.2012.04.001
Das, K. (2008). A comparative study of exponential distribution vs Weibull distribution in machine reliability analysis in a CMS design. Computers & Industrial Engineering, 54(1), 12–33. https://doi.org/10.1016/j.cie.2007.06.030
de León, A. D., Lalla-Ruiz, E., Melián-Batista, B., & Moreno-Vega, J. M. (2021). A simulation–optimization framework for enhancing robustness in bulk berth scheduling. Engineering Applications of Artificial Intelligence, 103, 104276. https://doi.org/10.1016/j.engappai.2021.104276
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II. In CEUR Workshop Proceedings (Vol. 1133, pp. 849–858). https://doi.org/10.1007/3-540-45356-3_83
Ebrahimi, M., Fatemi Ghomi, S. M. T., & Karimi, B. (2014). Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates. Applied Mathematical Modelling, 38(9–10), 2490–2504. https://doi.org/10.1016/j.apm.2013.10.061
Fu, Y., Zhou, M., Guo, X., & Qi, L. (2020). Scheduling Dual-Objective Stochastic Hybrid Flow Shop With Deteriorating Jobs via Bi-Population Evolutionary Algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 5037–5048. https://doi.org/10.1109/TSMC.2019.2907575
Gonzalez-Neira, E. M., Ferone, D., Hatami, S., & Juan, A. A. (2017). A biased-randomized simheuristic for the distrib-uted assembly permutation flowshop problem with stochastic processing times. Simulation Modelling Practice and Theory, 79, 23–36. https://doi.org/10.1016/j.simpat.2017.09.001
González-Neira, E. M., García-Cáceres, R. G., Caballero-Villalobos, J. P., Molina-Sánchez, L. P., & Montoya-Torres, J. R. (2016). Stochastic flexible flow shop scheduling problem under quantitative and qualitative decision criteria. Computers & Industrial Engineering, 101, 128–144. https://doi.org/10.1016/j.cie.2016.08.026
González-Neira, E. M., Montoya-Torres, J. R., & Barrera, D. (2017). Flow-shop scheduling problem under uncertainties: Review and trends. International Journal of Industrial Engineering Computations, 8(4), 399–426. https://doi.org/10.5267/j.ijiec.2017.2.001
Han, W., Deng, Q., Gong, G., Zhang, L., & Luo, Q. (2021). Multi-objective evolutionary algorithms with heuristic de-coding for hybrid flow shop scheduling problem with worker constraint. Expert Systems with Applications, 168, 114282. https://doi.org/10.1016/j.eswa.2020.114282
Holthaus, O. (1999). Scheduling in job shops with machine breakdowns: An experimental study. Computers and Indus-trial Engineering, 36(1), 137–162. https://doi.org/10.1016/S0360-8352(99)00006-6
Hsieh, J.-C., Chang, P.-C., & Hsu, L.-C. (2003). Scheduling of drilling operations in printed circuit board factory☆. Computers & Industrial Engineering, 44(3), 461–473. https://doi.org/10.1016/S0360-8352(02)00231-0
Huang, Y., Deng, L., Wang, J., Qiu, W., & Liu, J. (2023). Modeling and solution for hybrid flow-shop scheduling prob-lem by two-stage stochastic programming. Expert Systems with Applications, 233. https://doi.org/10.1016/j.eswa.2023.120846
Ji, M., Yang, Y., Duan, W., Wang, S., & Liu, B. (2016). Scheduling of no-wait stochastic distributed assembly flowshop by hybrid PSO. 2016 IEEE Congress on Evolutionary Computation (CEC), 2649–2654. https://doi.org/10.1109/CEC.2016.7744120
Jiang, S., Liu, M., Hao, J., & Qian, W. (2015). A bi-layer optimization approach for a hybrid flow shop scheduling prob-lem involving controllable processing times in the steelmaking industry. Computers & Industrial Engineering, 87, 518–531. https://doi.org/10.1016/j.cie.2015.06.002
Juan, A. A., Barrios, B. B., Vallada, E., Riera, D., & Jorba, J. (2014). A simheuristic algorithm for solving the permuta-tion flow shop problem with stochastic processing times. Simulation Modelling Practice and Theory, 46, 101–117. https://doi.org/10.1016/j.simpat.2014.02.005
Juan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics: Extending metaheu-ristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72. https://doi.org/10.1016/j.orp.2015.03.001
Kianfar, K., Fatemi Ghomi, S. M. T., & Oroojlooy Jadid, A. (2012). Study of stochastic sequence-dependent flexible flow shop via developing a dispatching rule and a hybrid GA. Engineering Applications of Artificial Intelligence, 25(3), 494–506. https://doi.org/10.1016/j.engappai.2011.12.004
Kim, D.-W., Na, D.-G., & Frank Chen, F. (2003). Unrelated parallel machine scheduling with setup times and a total weighted tardiness objective. Robotics and Computer-Integrated Manufacturing, 19(1–2), 173–181. https://doi.org/10.1016/S0736-5845(02)00077-7
Lin, J. T., & Chen, C.-M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semi-conductor back-end manufacturing. Simulation Modelling Practice and Theory, 51, 100–114. https://doi.org/10.1016/j.simpat.2014.10.008
Lin, J. T., Chen, C.-M., Chiu, C.-C., & Fang, H.-Y. (2013). Simulation optimization with PSO and OCBA for semicon-ductor back-end assembly. Journal of Industrial and Production Engineering, 30(7), 452–460. https://doi.org/10.1080/21681015.2013.860926
Lin, Y.-K., & Huang, D.-H. (2020). Reliability analysis for a hybrid flow shop with due date consideration. Reliability Engineering & System Safety, 199, 105905. https://doi.org/10.1016/j.ress.2017.07.008
Liu, Q., Ullah, S., & Zhang, C. (2011). An improved genetic algorithm for robust permutation flowshop scheduling. The International Journal of Advanced Manufacturing Technology, 56(1–4), 345–354. https://doi.org/10.1007/s00170-010-3149-6
Liu, Y., Shen, W., Zhang, C., & Sun, X. (2023). Agent-based simulation and optimization of hybrid flow shop consider-ing multi-skilled workers and fatigue factors. Robotics and Computer-Integrated Manufacturing, 80. https://doi.org/10.1016/j.rcim.2022.102478
Minella, G., Ruiz, R., & Ciavotta, M. (2011). Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems. Computers & Operations Research, 38(11), 1521–1533. https://doi.org/10.1016/j.cor.2011.01.010
Mirabi, M., Ghomi, S. M. T. F., & Jolai, F. (2013). A two-stage hybrid flowshop scheduling problem in machine break-down condition. Journal of Intelligent Manufacturing, 24(1), 193–199. https://doi.org/10.1007/s10845-011-0553-1
Pinedo, M. L. (2012). Scheduling: Theory, algorithms and systems. In Springer (4th ed., Vol. 4). Springer Science & Business Media. https://doi.org/10.1007/978-1-4614-2361-4
Qin, W., Zhang, J., & Song, D. (2018). An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time. Journal of Intelligent Manufacturing, 29(4), 891–904. https://doi.org/10.1007/s10845-015-1144-3
Rahmani, D., Heydari, M., Makui, A., & Zandieh, M. (2013). A new approach to reducing the effects of stochastic dis-ruptions in flexible flow shop problems with stability and nervousness. International Journal of Management Sci-ence and Engineering Management, 8(3), 173–178. https://doi.org/10.1080/17509653.2013.812332
Rajendran, C., & Chaudhuri, D. (1992). Scheduling in n-job, m-stage flowshop with parallel processors to minimize makespan. International Journal of Production Economics, 27(2), 137–143. https://doi.org/10.1016/0925-5273(92)90006-S
Rodríguez-Espinosa, C. A., González-Neira, E. M., & Zambrano-Rey, G. M. (2023). A simheuristic approach using the NSGA-II to solve a bi-objective stochastic flexible job shop problem. Journal of Simulation, 1–25. https://doi.org/10.1080/17477778.2023.2231877
Rooeinfar, R., Raissi, S., & Ghezavati, V. (2019). Stochastic flexible flow shop scheduling problem with limited buffers and fixed interval preventive maintenance: a hybrid approach of simulation and metaheuristic algorithms. SIMULA-TION, 95(6), 509–528. https://doi.org/10.1177/0037549718809542
Ruiz, R., & Maroto, C. (2006). A genetic algorithm for hybrid flowshops with sequence dependent setup times and ma-chine eligibility. European Journal of Operational Research, 169(3), 781–800. https://doi.org/10.1016/j.ejor.2004.06.038
Schulz, S. (2019). A Genetic Algorithm to Solve the Hybrid Flow Shop Scheduling Problem with Subcontracting Options and Energy Cost Consideration (pp. 263–273). https://doi.org/10.1007/978-3-319-99993-7_23
Silva, C., Reis, V., Morais, A., Brilenkov, I., Vaza, J., Pinheiro, T., Neves, M., Henriques, M., Varela, M. L., Pereira, G., Dias, L., Fernandes, N. O., & Carmo-Silva, S. (2017). A comparison of production control systems in a flexible flow shop. Procedia Manufacturing, 13, 1090–1095. https://doi.org/10.1016/j.promfg.2017.09.169
Singh, D., & Shukla, R. (2020). Multi-objective optimization of selected non-traditional machining processes using NSGA-II. Decision Science Letters, 421–438. https://doi.org/10.5267/j.dsl.2020.3.003
Tang, D., Dai, M., Salido, M. A., & Giret, A. (2015). Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in Industry, 81, 82–95. https://doi.org/10.1016/j.compind.2015.10.001
Tavakkoli-Moghaddam, R., Taheri, F., Bazzazi, M., Izadi, M., & Sassani, F. (2009). Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence con-straints. Computers & Operations Research, 36(12), 3224–3230. https://doi.org/10.1016/j.cor.2009.02.012
Urlings, T., Ruiz, R., & Stützle, T. (2010). Shifting representation search for hybrid flexible flowline problems. Europe-an Journal of Operational Research, 207(2), 1086–1095. https://doi.org/10.1016/j.ejor.2010.05.041
Wang, H., Fu, Y., Huang, M., Huang, G. Q., & Wang, J. (2017). A NSGA-II based memetic algorithm for multiobjective parallel flowshop scheduling problem. Computers & Industrial Engineering, 113, 185–194. https://doi.org/10.1016/j.cie.2017.09.009
Wang, K., & Choi, S. H. (2010). A decomposition-based approach to flexible flow shop scheduling under stochastic set-up times. 2010 5th IEEE International Conference Intelligent Systems, 55–60. https://doi.org/10.1109/IS.2010.5548328
Wang, K., & Choi, S. H. (2014). A holonic approach to flexible flow shop scheduling under stochastic processing times. Computers & Operations Research, 43(1), 157–168. https://doi.org/10.1016/j.cor.2013.09.013
Wang, K., Choi, S. H., & Qin, H. (2014). An estimation of distribution algorithm for hybrid flow shop scheduling under stochastic processing times. International Journal of Production Research, 52(24), 7360–7376. https://doi.org/10.1080/00207543.2014.930535
Wang, K., Choi, S. H., Qin, H., & Huang, Y. (2013). A cluster-based scheduling model using SPT and SA for dynamic hybrid flow shop problems. The International Journal of Advanced Manufacturing Technology, 67(9–12), 2243–2258. https://doi.org/10.1007/s00170-012-4645-7
Wang, S., Wang, L., Liu, M., & Xu, Y. (2015). An order-based estimation of distribution algorithm for stochastic hybrid flow-shop scheduling problem. International Journal of Computer Integrated Manufacturing, 28(3), 307–320. https://doi.org/10.1080/0951192X.2014.880803
Wang, Y., & Xie, N. (2021). Flexible flow shop scheduling with interval grey processing time. Grey Systems, 11(4), 779–795. https://doi.org/10.1108/GS-09-2020-0123
Yu, C., Andreotti, P., & Semeraro, Q. (2020). Multi-objective scheduling in hybrid flow shop: Evolutionary algorithms using multi-decoding framework. Computers and Industrial Engineering, 147. https://doi.org/10.1016/j.cie.2020.106570
Zandieh, M., & Gholami, M. (2009). An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. International Journal of Production Research, 47(24), 6999–7027. https://doi.org/10.1080/00207540802400636
Zandieh, M., & Hashemi, A. (2015). Group scheduling in hybrid flexible flowshop with sequence-dependent setup times and random breakdowns via integrating genetic algorithm and simulation. International Journal of Industrial and Systems Engineering, 21(3), 377. https://doi.org/10.1504/IJISE.2015.072273
  • 85
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: Journal of Project Management | Year: 2024 | Volume: 9 | Issue: 4 | Views: 337 | Reviews: 0

Related Articles:
  • A hybrid genetic algorithm for the hybrid flow shop scheduling problem with ...
  • A hybrid genetic-gravitational search algorithm for a multi-objective flow ...
  • Evaluating the performance of constructive heuristics for the blocking flow ...
  • Solving group scheduling problem in no-wait flexible flowshop with random m ...
  • A heuristic algorithm for scheduling in a flow shop environment to minimize ...

Add Reviews

Name:*
E-Mail:
Review:
Bold Italic Underline Strike | Align left Center Align right | Insert smilies Insert link URLInsert protected URL Select color | Add Hidden Text Insert Quote Convert selected text from selection to Cyrillic (Russian) alphabet Insert spoiler
winkwinkedsmileam
belayfeelfellowlaughing
lollovenorecourse
requestsadtonguewassat
cryingwhatbullyangry
Security Code: *
Include security image CAPCHA.
Refresh Code

® 2010-2025 GrowingScience.Com