Performance evaluation in aggregate production planning using integrated RED-SWARA method under uncertain condition

Document Type : Article

Authors

Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

It is widely felt that the performance evaluation in aggregate production planning provides a theoretical and practical overview. The present study aimed to evaluate the performance in the aggregate production planning. In this regard, the optimal values were determined by the multi-objective grey aggregate production planning model and the weights of the input and output indicators of the performance evaluation were characterized by the step-wise weight assessment ratio analysis (SWARA) technique. Further, the efficiency of the decision-making units was determined by the ratio efficiency dominance (RED) model. Then, the ranking of decision-making units was conducted. In the case study of automobile parts manufacturing industry in Iran, the sensitivity analysis was performed on the model and its effects were evaluated, in addition to evaluating the proposed model. The results indicated that the proposed model had a high degree of accuracy in evaluating performance compared to previous models and helps managers to make better decisions to increase the efficiency and reduce the waste of resources.

Keywords


References
1. Rahmani, D., Youse
i, A., and Ramezanian, R. A
new robust fuzzy approach for aggregate production
planning", Scientia Iranica, Transaction E, Industrial
Engineering, 21(6), pp. 2307{2314 (2014).
2. Ramyar, M., Mehdizadeh, E., and Hadji Molana S.M.
Optimizing reliability and cost of system for aggregate
production planning in supply chain", Scientia
Iranica, 24(6), pp. 3394{3408 (2017).
3. Du, J., Liang, L., Chen, Y., and Bi, G.B. DEAbased
production planning", Omega, 38(1), pp. 105{
112 (2010).
4. Amirteimoori, A. and Kordrostami, S. Production
planning in data envelopment analysis", International
Journal of Production Economics, 140(1), pp. 212{218
(2012).
5. Xiong, Y., Li, Z., and Fang, X. Performance evaluation
of introducing group technology into machining
industry with data envelopment analysis", Journal
of Interdisciplinary Mathematics, 20(1), pp. 295{305
(2017).
6. Kianfar, K., Ahadzadeh Namin, M., Alam Tabriz, A.,
Naja , E., and Hosseinzadeh Lot , F. Hybrid cluster
and data envelopment analysis with interval data",
Scientia Iranica, 25(5), pp. 2904{2911 (2018).
7. Khalili, J. and Alinezhad, A. Performance evaluation
in green supply chain using BSC, DEA and data mining",
International Journal of Supply and Operations
Management, 5(2), pp. 182{191 (2018).
8. Amini, A. and Alinezhad, A. Integrating DEA and
group AHP for eciency evaluation and the identi cation
of the most ecient DMU", International Journal
of Supply and Operations Management, 4(4), pp. 318{
327 (2017).
9. Alinezhad, A., Sarrafha, K., and Amini, A. Sensitivity
analysis of SAWtechnique: The impact of changing
the decision making matrix elements on the nal
ranking of alternatives", Iranian Journal of Operations
Research, 5, pp. 82{94 (2014).
10. Mula, J., Poler. R., Garcia-Sabater, J.P., and Lario,
F.C. Models for production planning under uncertainty:
A review", International Journal of Production
Economics, 103, pp. 271{285 (2006).
11. Tang, J., Wang, D., and Fung, R.Y. Fuzzy formulation
for multi-product aggregate production planning",
Production Planning & Control, 11(7), pp. 670{676
(2000).
12. Wang, R.C. and Liang, T.F. Aggregate production
planning with multiple fuzzy goals", The International
Journal of Advanced Manufacturing Technology, 25(5),
pp. 589{597 (2005).
13. Tang, J., Fung, R.Y., and Yung, K.L. Fuzzy
modelling and simulation for aggregate production
planning", International Journal of Systems Science,
34(12{13), pp. 661{673 (2003).
14. Aliev, R.A., Fazlollahi, B., Guirimov, B.G., and
Aliev, R.R. Fuzzy-genetic approach to aggregate
production-distribution planning in supply chain management",
Information Sciences, 177(20), pp. 4241{
4255 (2007).
15. Jamalnia, A. and Soukhakian, M.A. A hybrid fuzzy
goal programming approach with di erent goal priorities
to aggregate production planning", Computers &
Industrial Engineering, 56(4), pp. 1474{1486 (2009).
16. Yaghin, R.G., Torabi, S.A., and Ghomi, S.F. Integrated
markdown pricing and aggregate production
planning in a two echelon supply chain: A hybrid fuzzy
multiple objective approach", Applied Mathematical
Modelling, 36(12), pp. 6011{6030 (2012).
17. Sadeghi, M., Hajiagha, S.H.R., and Hashemi, S.S. A
fuzzy grey goal programming approach for aggregate
production planning", The International Journal of
Advanced Manufacturing Technology, 64(9{12), pp.
1715{1727 (2013).
18. Gholamian, N., Mahdavi, I., Tavakkoli-Moghaddam,
R., and Mahdavi-Amiri, N. Comprehensive fuzzy
multi-objective multi-product multi-site aggregate
production planning decisions in a supply chain under
uncertainty", Applied soft computing, 37, pp. 585{607
(2015).
19. Mosadegh, H., Khakbazan, E., Salmasnia, A., and
Mokhtari, H. A fuzzy multi-objective goal programming
model for solving an aggregate production planning
problem with uncertainty", International Journal
of Information and Decision Sciences, 9(2), pp. 97{115
(2017).
20. Opricovic, S. and Tzeng, G.H. Comparing DEA and
MCDM method", Multi-Objective Programming and
Goal-Programming: Theory and Applications, pp. 227{
232 (2003).
21. Raju, K.S. and Kumar, D.N. Ranking irrigation planning
alternatives using data envelopment analysis",
Water Resources Management, 20(4), pp. 553{566
(2006).
22. Xiong, G. The entropy DEA model and empirical
analysis for measuring eciency of industrial pollution
control", In 4th International Conference on Wireless
Communications, Networking and Mobile Computing,
November 18, China (2008).
23. Shakouri, H., Nabaee, M., and Aliakbarisani, S. A
quantitative discussion on the assessment of power
supply technologies: DEA (data envelopment analysis)
and SAW (simple additive weighting) as complementary
methods for the Grammar"", Energy, 64, pp.
640{647 (2014).
24. Kuo, R.J. and Lin, Y.J. Supplier selection using analytic
network process and data envelopment analysis",
International Journal of Production Research, 50(11),
pp. 2852{2863 (2012).
926 J. Khalili and A. Alinezhad/Scientia Iranica, Transactions E: Industrial Engineering 28 (2021) 912{926
25. Mansouri, A., Naser, E., and Ramazani, M. Ranking
of companies based on TOPSIS-DEA approach methods
(case study of cement industry in Tehran stock
exchange)", Pak. J. Stat. Oper. Res., 10, pp. 189{204
(2014).
26. Saad, G.H. An overview of production planning models:
structural classi cation and empirical assessment",
The International Journal of Production Research,
20(1), pp. 105{114 (1982).
27. Zimmermann, H.J. Fuzzy programming and linear
programming with several objective functions", Fuzzy
Sets and Systems, 1(1), pp. 45{55 (1978).
28. Leung, S.C. and Chan, S.S. A goal programming
model for aggregate production planning with resource
utilization constraint", Computers & Industrial Engineering,
56(3), pp. 1053{1064 (2009).
29. Kersuliene, V., Zavadskas, E.K., and Turskis, Z.
Selection of rational dispute resolution method by
applying new step-wise weight assessment ratio analysis
(SWARA)", Journal of Business Economics and
Management, 11(2), pp. 243{258 (2010).
30. Farahmand, M. and Desa, M.I. RED: a new method
for performance ranking of large decision making
units", Soft Computing, 21(5), pp. 1271{1290 (2017).