Processing, Please wait...

  • Home
  • 📊 Statistics
  • About Us
  • 📺 Tutorial
  • Search:
  • Advanced Search

Growing Science » Uncertain Supply Chain Management » Robust design of critical factors of multi-stage supply chain operations management

📚 Highly Cited Articles

  • Jaya Algorithm
  • Rao Algorithm
  • TLBO Algorithm
  • Discrete Firefly
  • ChatGPT and Blended Learning

Journals

  • IJIEC (777)
  • MSL (2648)
  • DSL (690)
  • CCL (544)
  • USCM (1099)
  • ESM (428)
  • AC (562)
  • JPM (323)
  • IJDS (992)
  • JFS (101)
  • HE (42)
  • SCI (41)

USCM Volumes

    • Volume 14 (22)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (7)
    • Volume 13 (62)
      • Issue 1 (15)
      • Issue 2 (15)
      • Issue 3 (15)
      • Issue 4 (17)
    • Volume 12 (244)
      • Issue 1 (55)
      • Issue 2 (59)
      • Issue 3 (63)
      • Issue 4 (67)
    • Volume 11 (190)
      • Issue 1 (42)
      • Issue 2 (45)
      • Issue 3 (50)
      • Issue 4 (53)
    • Volume 10 (150)
      • Issue 1 (28)
      • Issue 2 (32)
      • Issue 3 (44)
      • Issue 4 (46)
    • Volume 9 (117)
      • Issue 1 (25)
      • Issue 2 (26)
      • Issue 3 (32)
      • Issue 4 (34)
    • Volume 8 (82)
      • Issue 1 (20)
      • Issue 2 (15)
      • Issue 3 (17)
      • Issue 4 (30)
    • Volume 7 (57)
      • Issue 1 (8)
      • Issue 2 (19)
      • Issue 3 (14)
      • Issue 4 (16)
    • Volume 6 (25)
      • Issue 1 (7)
      • Issue 2 (6)
      • Issue 3 (6)
      • Issue 4 (6)
    • Volume 5 (26)
      • Issue 1 (6)
      • Issue 2 (6)
      • Issue 3 (8)
      • Issue 4 (6)
    • Volume 4 (31)
      • Issue 1 (10)
      • Issue 2 (6)
      • Issue 3 (6)
      • Issue 4 (9)
    • Volume 3 (39)
      • Issue 1 (9)
      • Issue 2 (13)
      • Issue 3 (10)
      • Issue 4 (7)
    • Volume 2 (32)
      • Issue 1 (7)
      • Issue 2 (5)
      • Issue 3 (10)
      • Issue 4 (10)
    • Volume 1 (22)
      • Issue 1 (4)
      • Issue 2 (6)
      • Issue 3 (6)
      • Issue 4 (6)

🔑 Keywords

Supply chain management(168)
Jordan(167)
Vietnam(153)
Customer satisfaction(122)
Performance(116)
Supply chain(113)
Competitive advantage(98)
Service quality(98)
Artificial intelligence(96)
Tehran Stock Exchange(94)
Sustainability(91)
SMEs(91)
optimization(88)
Trust(84)
Financial performance(84)
TOPSIS(83)
Job satisfaction(81)
Knowledge Management(80)
Social media(79)
Genetic Algorithm(78)


» Show all keywords

✍️ Authors

Naser Azad(82)
Zeplin Jiwa Husada Tarigan(67)
Mohammad Reza Iravani(64)
Endri Endri(45)
Muhammad Alshurideh(42)
Hotlan Siagian(40)
Dmaithan Almajali(38)
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)
Haitham M. Alzoubi(29)
Shankar Chakraborty(29)
Ni Nyoman Kerti Yasa(29)
Sulieman Ibraheem Shelash Al-Hawary(28)
Prasadja Ricardianto(28)


» Show all authors

🌍 Countries

1. Algeria (52)
2. Angola (1)
3. Argentina (22)
4. Armenia (2)
5. Australia (52)
6. Austria (2)
7. Bahrain (26)
8. Bangladesh (56)
9. Belarus (3)
10. Belgium (3)
11. Benin (2)
12. Benin Republic (1)
13. Bhutan (1)
14. Bosnia and Herzegovina (1)
15. Botswana (8)
16. Brazil (39)
17. Brunei (1)
18. Bulgaria (1)
19. Burkina Faso (1)
20. Cameroon (1)
Total: 121 countries

Show all countries

Uncertain Supply Chain Management

ISSN 2291-6830 (Online) - ISSN 2291-6822 (Print)
Quarterly Publication
Volume 3 Issue 2 pp. 159-164 , 2015

Robust design of critical factors of multi-stage supply chain operations management Pages 159-164 Right click to download the paper Download PDF

Authors: Shahrzad Erfani, Razieh Keshavarzfard

doi 10.5267/j.uscm.2014.12.005
Crossmark

Keywords: Operation management, Optimization, Robust design of parameter, Simulation, Supply chain

Abstract: Today, supply chains have been widely welcomed by industry researchers and the results of applying it may increase throughput, reduce cost, increase speed to meet customers’ needs and create competitive opportunities. This paper identifies a scientific method, which operates efficiently and effectively manages supply chain operations. In this paper, a computer simulation model is analyzed for analyzing the supply chain and the results are examined, accordingly. Using Taguchi design of experiment and running the proposed model under L27 scenarios, a two-objective optimization was performed on the estimated response surfaces, leading to a 60% increase in productivity and 40% reduction in waiting time.

How to cite this paper

Erfani, S & Keshavarzfard, R. (2015). Robust design of critical factors of multi-stage supply chain operations management.Uncertain Supply Chain Management, 3(2), 159-164.

References
Brito, T. B., Silva, R. C. D. S., Botter, R. C., Pereira, N. N., & Medina, A. C. (2010, December). Discrete event simulation combined with multi-criteria decision analysis applied to steel plant logistics system planning. In Proceedings of the Winter Simulation Conference (pp. 2126-2137). Winter Simulation Conference.

Cooper, M. C., Lambert, D. M., & Pagh, J. D. (1997). Supply chain management: more than a new name for logistics. The International Journal of Logistics Management, 8(1), 1-14

Hao, Q., & Shen, W. (2008). Implementing a hybrid simulation model for a Kanban-based material handling system. Robotics and Computer-Integrated Manufacturing, 24(5), 635-646.

Lee, E., & Farahmand, K. (2010, December). Simulation of a base stock inventory management system integrated with transportation strategies of a logistic network. In Simulation Conference (WSC), Proceedings of the 2010 Winter (pp. 1934-1945). IEEE.

Pawlewski, P., & Fertsch, M. (2010, December). Modeling and simulation method to find and eliminate bottlenecks in production logistics systems. In Simulation Conference (WSC), Proceedings of the 2010 Winter (pp. 1946-1956). IEEE

Shang, J. S., Li, S., & Tadikamalla*, P. (2004). Operational design of a supply chain system using the Taguchi method, response surface methodology, simulation, and optimization. International Journal of Production Research, 42(18), 3823-3849.

Sharda, B., & Bury, S. J. (2010, December). Bottleneck analysis of a chemical plant using discrete event simulation. In Proceedings of the Winter Simulation Conference (pp. 1547-1555). Winter Simulation Conference.

Tsay, A. A., Nahmias, S., & Agrawal, N. (1999). Modeling supply chain contracts: A review. In Quantitative models for supply chain management (pp. 299-336). Springer US.
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: Uncertain Supply Chain Management | Year: 2015 | Volume: 3 | Issue: 2 | Views: 2349 | Reviews: 0

Related Articles:
  • The impact of the national standardization system on ranking the supply chain stages improvement
  • Proactive inventory policy intervention to mitigate risk within cooperative supply chains
  • A new method for converting extended version of petri nets to fuzzy extended markup language
  • Performance measurement of supply chain flexibility using witness
  • Utilizing simulation to evaluate production line performance under varying demand conditions

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