Processing, Please wait...

  • Home
  • About Us
  • Search:
  • Advanced Search

Growing Science » International Journal of Industrial Engineering Computations » Enhancing efficiency and adaptability in mixed model line balancing through the fusion of learning effects and worker prerequisites

Journals

  • IJIEC (777)
  • MSL (2643)
  • DSL (690)
  • CCL (528)
  • USCM (1092)
  • ESM (421)
  • AC (562)
  • JPM (293)
  • IJDS (952)
  • JFS (101)
  • HE (32)
  • SCI (26)

IJIEC Volumes

    • Volume 1 (17)
      • Issue 1 (9)
      • Issue 2 (8)
    • Volume 2 (68)
      • Issue 1 (12)
      • Issue 2 (20)
      • Issue 3 (20)
      • Issue 4 (16)
    • Volume 3 (76)
      • Issue 1 (9)
      • Issue 2 (15)
      • Issue 3 (20)
      • Issue 4 (12)
      • Issue 5 (20)
    • Volume 4 (50)
      • Issue 1 (14)
      • Issue 2 (10)
      • Issue 3 (12)
      • Issue 4 (14)
    • Volume 5 (47)
      • Issue 1 (13)
      • Issue 2 (12)
      • Issue 3 (12)
      • Issue 4 (10)
    • Volume 6 (39)
      • Issue 1 (7)
      • Issue 2 (12)
      • Issue 3 (10)
      • Issue 4 (10)
    • Volume 7 (47)
      • Issue 1 (10)
      • Issue 2 (14)
      • Issue 3 (10)
      • Issue 4 (13)
    • Volume 8 (30)
      • Issue 1 (9)
      • Issue 2 (7)
      • Issue 3 (8)
      • Issue 4 (6)
    • Volume 9 (32)
      • Issue 1 (9)
      • Issue 2 (6)
      • Issue 3 (7)
      • Issue 4 (10)
    • Volume 10 (34)
      • Issue 1 (8)
      • Issue 2 (10)
      • Issue 3 (8)
      • Issue 4 (8)
    • Volume 11 (36)
      • Issue 1 (9)
      • Issue 2 (8)
      • Issue 3 (9)
      • Issue 4 (10)
    • Volume 12 (29)
      • Issue 1 (9)
      • Issue 2 (6)
      • Issue 3 (8)
      • Issue 4 (6)
    • Volume 13 (41)
      • Issue 1 (10)
      • Issue 2 (8)
      • Issue 3 (10)
      • Issue 4 (13)
    • Volume 14 (50)
      • Issue 1 (11)
      • Issue 2 (15)
      • Issue 3 (9)
      • Issue 4 (15)
    • Volume 15 (55)
      • Issue 1 (19)
      • Issue 2 (15)
      • Issue 3 (12)
      • Issue 4 (9)
    • Volume 16 (75)
      • Issue 1 (12)
      • Issue 2 (15)
      • Issue 3 (19)
      • Issue 4 (29)
    • Volume 17 (51)
      • Issue 1 (21)
      • Issue 2 (30)

Keywords

Supply chain management(168)
Jordan(165)
Vietnam(151)
Customer satisfaction(120)
Performance(115)
Supply chain(112)
Service quality(98)
Competitive advantage(97)
Tehran Stock Exchange(94)
SMEs(89)
optimization(87)
Sustainability(86)
Artificial intelligence(85)
Financial performance(84)
Trust(83)
TOPSIS(83)
Job satisfaction(81)
Genetic Algorithm(78)
Factor analysis(78)
Social media(78)


» Show all keywords

Authors

Naser Azad(82)
Zeplin Jiwa Husada Tarigan(66)
Mohammad Reza Iravani(64)
Endri Endri(45)
Muhammad Alshurideh(42)
Hotlan Siagian(40)
Dmaithan Almajali(37)
Jumadil Saputra(36)
Muhammad Turki Alshurideh(35)
Ahmad Makui(33)
Barween Al Kurdi(32)
Hassan Ghodrati(31)
Basrowi Basrowi(31)
Sautma Ronni Basana(31)
Mohammad Khodaei Valahzaghard(30)
Shankar Chakraborty(29)
Ni Nyoman Kerti Yasa(29)
Haitham M. Alzoubi(28)
Sulieman Ibraheem Shelash Al-Hawary(28)
Prasadja Ricardianto(28)


» Show all authors

Countries

Iran(2192)
Indonesia(1311)
Jordan(813)
India(793)
Vietnam(510)
Saudi Arabia(478)
Malaysia(444)
China(231)
United Arab Emirates(226)
Thailand(160)
United States(114)
Ukraine(110)
Turkey(110)
Egypt(106)
Peru(94)
Canada(93)
Morocco(86)
Pakistan(85)
United Kingdom(80)
Nigeria(78)


» Show all countries

International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print)
Quarterly Publication
Volume 15 Issue 2 pp. 541-552 , 2024

Enhancing efficiency and adaptability in mixed model line balancing through the fusion of learning effects and worker prerequisites Pages 541-552 Right click to download the paper Download PDF

Authors: Esam Alhomaidhi

DOI: 10.5267/j.ijiec.2023.12.008

Keywords: Mixed-model Line balancing, Learning effect, Heuristic, Task requirements, Cost optimization

Abstract: This research introduces a comprehensive scheme to tackle the Mixed-Model Assembly Line Balancing Problem (MALBPLW) within manufacturing contexts. The primary aim is to optimize assembly line task assignments by integrating both the learning effect and worker prerequisites. The learning effect recognizes the enhanced efficiency of workers over time due to learning and experience. A novel mathematical model and solution approach are proposed, encompassing factors like cycle time, task interdependencies, worker classifications, and the learning effect. The model endeavors to minimize the overall costs related to both workers and workstations while simultaneously maximizing production efficiency. Experimental assessments are conducted to evaluate the efficacy of this proposed approach. Diverse manufacturing scenarios are inspected, comparing and analyzing cost reductions and production efficiency. The outcomes highlight the effectiveness of this approach in achieving enhanced cost-effectiveness and resource utilization in contrast to conventional methods. This study contributes significantly to advancing assembly line balancing and production planning techniques by presenting a pragmatic framework for optimizing resource usage and reducing costs in manufacturing environments. The knowledge extracted from these discoveries can significantly assist professionals in the industry seeking to improve manufacturing processes and strengthen competitiveness.

How to cite this paper
Alhomaidhi, E. (2024). Enhancing efficiency and adaptability in mixed model line balancing through the fusion of learning effects and worker prerequisites.International Journal of Industrial Engineering Computations , 15(2), 541-552.

Refrences
Alhomaidi, E., & Askin, R. G. (2022). The Assembly Line Balancing Problem in the Presence of Task Learning and Demand Fulfilment (ALBLDP). In IIE Annual Conference. Proceedings (pp. 1-6). Institute of Industrial and Systems Engineers (IISE).‏
Battaïa, O., Delorme, X., Dolgui, A., Hagemann, J., Horlemann, A., Kovalev, S., & Malyutin, S. (2015). Workforce minimization for a mixed-model assembly line in the automotive industry. International Journal of Production Economics, 170, 489-500.‏
Battaïa, O., & Dolgui, A. (2013). A taxonomy of line balancing problems and their solution approaches. International journal of production economics, 142(2), 259-277.‏
Battaïa, O., & Dolgui, A. (2022). Hybridizations in line balancing problems: A comprehensive review on new trends and formulations. International Journal of Production Economics, 108673.‏
Boysen, N., Fliedner, M., & Scholl, A. (2007). A classification of assembly line balancing problems. European journal of operational research, 183(2), 674-693.‏
Boysen, N., Fliedner, M., & Scholl, A. (2008). Assembly line balancing: Which model to use when? International journal of production economics, 111(2), 509-528.‏
Boysen, N., Schulze, P., & Scholl, A. (2022). Assembly line balancing: What happened in the last fifteen years? European Journal of Operational Research, 301(3), 797-814.‏
Chen, J. C., Chen, Y. Y., Chen, T. L., & Kuo, Y. H. (2019). Applying two-phase adaptive genetic algorithm to solve multi-model assembly line balancing problems in TFT–LCD module process. Journal of Manufacturing Systems, 52, 86-99.‏
Chutima, P., & Chimklai, P. (2012). Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimization with negative knowledge. Computers & Industrial Engineering, 62(1), 39-55.‏
Cohen, Y., Vitner, G., & Sarin, S. C. (2006). Optimal allocation of work in assembly lines for lots with homogenous learning. European Journal of Operational Research, 168(3), 922-931.‏
Delice, Y., Kızılkaya Aydoğan, E., Özcan, U., & İlkay, M. S. (2017). A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing. Journal of Intelligent Manufacturing, 28, 23-36.‏
Delorme, X., Dolgui, A., Kovalev, S., & Kovalyov, M. Y. (2019). Minimizing the number of workers in a paced mixed-model assembly line. European Journal of Operational Research, 272(1), 188-194.‏
Dolgui, A., Kovalev, S., Kovalyov, M. Y., Malyutin, S., & Soukhal, A. (2018). Optimal workforce assignment to operations of a paced assembly line. European Journal of Operational Research, 264(1), 200-211.‏
Glock, C. H., Grosse, E. H., Jaber, M. Y., & Smunt, T. L. (2019). Applications of learning curves in production and operations management: A systematic literature review. Computers & Industrial Engineering, 131, 422-441.‏
Gökçen, H., Ağpak, K., & Benzer, R. (2006). Balancing of parallel assembly lines. International Journal of Production Economics, 103(2), 600-609.‏
Gökċen, H., & Erel, E. (1998). Binary integer formulation for mixed-model assembly line balancing problem. Computers & industrial engineering, 34(2), 451-461.‏
Hazır, Ö., Delorme, X., & Dolgui, A. (2015). A review of cost and profit oriented line design and balancing problems and solution approaches. Annual Reviews in Control, 40, 14-24.‏
Koltai, T., & Kalló, N. (2017). Analysis of the effect of learning on the throughput-time in simple assembly lines. Computers & industrial engineering, 111, 507-515.‏
Li, Y. (2017). The type-II assembly line rebalancing problem considering stochastic task learning. International Journal of Production Research, 55(24), 7334-7355.‏
Li, Y., & Boucher, T. O. (2017). Assembly line balancing problem with task learning and dynamic task reassignment. The International Journal of Advanced Manufacturing Technology, 88, 3089-3097.‏
Otto, C., & Otto, A. (2014). Extending assembly line balancing problem by incorporating learning effects. International Journal of Production Research, 52(24), 7193-7208.‏
Salveson, M. E. (1955). The assembly-line balancing problem. Transactions of the American Society of Mechanical Engineers, 77(6), 939-947.‏
Scholl, A., & Scholl, A. (1999). Balancing and sequencing of assembly lines (pp. 34-351). Heidelberg: Physica-Verlag.‏
Sternatz, J. (2014). Enhanced multi-Hoffmann heuristic for efficiently solving real-world assembly line balancing problems in automotive industry. European Journal of Operational Research, 235(3), 740-754.‏
Sun, H., & Fan, S. (2018). Car sequencing for mixed-model assembly lines with consideration of changeover complexity. Journal of manufacturing systems, 46, 93-102.‏
Thomopoulos, N. T. (1970). Mixed model line balancing with smoothed station assignments. Management science, 16(9), 593-603.‏
Tiacci, L., & Mimmi, M. (2018). Integrating ergonomic risks evaluation through OCRA index and balancing/sequencing decisions for mixed model stochastic asynchronous assembly lines. Omega, 78, 112-138.‏
Toksarı, M. D., İşleyen, S. K., Güner, E., & Baykoç, Ö. F. (2008). Simple and U-type assembly line balancing problems with a learning effect. Applied Mathematical Modelling, 32(12), 2954-2961.‏
Vilarinho, P. M., & Simaria, A. S. (2002). A two-stage heuristic method for balancing mixed-model assembly lines with parallel workstations. International journal of production research, 40(6), 1405-1420.‏
Wee, T. S., & Magazine, M. J. (1982). Assembly line balancing as generalized bin packing. Operations Research Letters, 1(2), 56-58.‏
Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of the aeronautical sciences, 3(4), 122-128.‏
Yelle, L.E., 1979. The learning curve: Historical review and comprehensive survey. Decision sciences 10, 302–328.
Zhang, B., Xu, L., & Zhang, J. (2020). A multi-objective cellular genetic algorithm for energy-oriented balancing and sequencing problem of mixed-model assembly line. Journal of Cleaner Production, 244, 118845.‏
  • 17
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: International Journal of Industrial Engineering Computations | Year: 2024 | Volume: 15 | Issue: 2 | Views: 1047 | Reviews: 0

Related Articles:
  • Mixed-model assembly line balancing problem in multi-demand scenarios
  • An application of Aluminum windows assembly line problem using FLB: An appl ...
  • A new type of problem to stabilize an assembly setup
  • Multi-objective assembly line balancing using genetic algorithm
  • Optimum assembly line balancing: A stochastic programming approach

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

® 2010-2026 GrowingScience.Com