A fuzzy multi-objective multi-product supplier selection and order allocation problem in supply chain under coverage and price considerations: An urban agricultural case study

Document Type : Article

Authors

1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

In this paper, a fuzzy multi-objective model is presented to select and allocate the order to the suppliers in uncertainty conditions, considering multi-period, multi-source, and multi-product cases at two levels of a supply chain with pricing considerations. Objective functions considered in this study as the measures to evaluate the suppliers are the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects. Partial and general coverage of suppliers in respect of distance and finally suppliers' weights make the amounts of products orders more realistic. Deference and coverage parameters in the model are considered as uncertain and random triangular fuzzy number. Since the proposed mathematical model is NP-hard, multi-objective particle swarm optimization (MOPSO) algorithm is presented. To validate the performance of MOPSO, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms. A practical case study in an agricultural industry is shown to demonstrate that the proposed algorithm applies to the real-world problems. The results are analyzed using quantitative criteria, performing parametric, and non-parametric statistical analysis.

Keywords

Main Subjects


References

1. Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E., Designing and Managing the Supply Chain, 2nd Ed., Boston: Irwin McGraw-Hill (2003).

2. Ghiani, G., Laporte, G. and Musmanno, R., Introduction To Logistics Systems Planning and Control, John
Wiley & Sons, Inc., Hoboken, New Jersey (2004).
3. Dulmin, R. and Mininno, V. Supplier selection using
a multi-criteria decision aid method", Journal of
Purchasing and Supply Management, 9, pp. 177-187
(2003).
4. Hugos, M.E., Essentials of Supply chain Management,
2nd Ed. New Jersey: Wiley (2006).
5. Karasakal, O. and Karasakal, E. A maximal covering
location model in the presence of partial coverage",
Computers & Operations Research, 31(9), pp. 1515-
1526 (2004).
6. Liang, T. Fuzzy multi-objective
production/distribution planning decisions with
multi-product", Computers & Industrial Engineering,
55(3), pp. 676-694 (2008).
7. Torabi, S. and Hassini, E. Multi-site production planning
integrating procurement and distribution plans
in multi-echelon supply chains: An interactive fuzzy
goal programming approach", International Journal of
Production Research, 159, pp. 193-214 (2008).
8. Fatih, E., Serkan, G., Mustafa, K. and Diyar, A.
A multi-criteria intuitionistic fuzzy group decision
making for supplier selection with TOPSIS method",
Expert Systems With Applications, 36(8), pp. 11363-
11368 (2009).
9. Onot, S., Selin, S. and Isik, E. Long term supplier
selection using a combined fuzzy MCDM approach:
A case study for a telecommunication company",
Expert Systems With Applications, 36(2), pp. 3887-
3895 (2009).
10. Amid, A., Ghodsypour, S. and O'Brien, C. A
weighted additive fuzzy multi objective model for
the supplier Selection problem under price breaks
in a supply chain", International Journal Production
Economics, 131(1), pp. 323-332 (2009).
11. Kokangol, A. and Susuz, Z. Integrated analytical
hierarch process and mathematical programming to
supplier selection problem with quantity discount",
Applied Mathematical Modeling, 33(3), pp. 1417-1420
(2009).
12. Tsai, W. and Wang, C. Decision making of sourcing
and order allocation with price discounts", Journal of
Manufacturing Systems, 29, pp. 47-54 (2010).
13. Atakhan, Y. and Ali Fuat, G. A weighted additive
fuzzy programming approach for multi-criteria supplier
selection", Expert Systems With Applications,
38(5), pp. 6281-6286 (2011).
14. Haleh, H. and Hamidi, A. A fuzzy MCDM model
for allocating orders to suppliers in a supply chain
under uncertainty over a multi-period time horizon",
448 A. Hajikhani et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 431{449
Expert Systems With Applications, 38(8), pp. 9076-
9083 (2011).
15. Liao, S.H., Lin, H. and Lai, P. An evolutionary
approach for multi-objective optimization of the integrated
location-inventory distribution network problem
in vendor-managed inventory", Expert Systems
With Applications, 38(6), pp. 6768-6776 (2011).
16. Lin, H. An integrated model for supplier selection
under a fuzzy situation", International Journal Production
Economics, 138, pp. 55-61 (2012).
17. Shaw, K., Shankar, R., Yadav, S. and Thakur, L.
Supplier selection using fuzzy AHP and fuzzy multiobjective
linear programming for developing low carbon
supply chain", Expert System With Applications,
39, pp. 8182-8192 (2012).
18. Nazari Shirkouhi, S., Shakouri, H., Javadi, B. and
Keramati, A. Supplier selection and order allocation
problem using a two-phase fuzzy multi-objective linear
programming", Applied Mathematical Modelling, 37,
pp. 9308-9323 (2013).
19. Esfandiari, N. and Seifbarghy, M. Modeling a stochastic
multi-objective supplier quota allocation problem
with price-dependent ordering", Applied Mathematical
Modelling, 37(8), pp. 5790-5800 (2013).
20. Arikan, F. A fuzzy solution approach for multi
objective supplier selection", Expert Systems With
Applications, 40(3), pp. 947-952 (2013).
21. Meena, P. and Sarmah, S. Multiple sourcing under
supplier failure risk and quantity discount: A genetic
algorithm approach", Transportation Research Part E,
50, pp. 84-97 (2013).
22. Hajipour, V., Khodakarami, V. and Tavana, M. The
redundancy queuing-location-allocation problem: A
novel approach", IEEE Transactions on Engineering
Management, 61(3), pp. 534-544 (2014a).
23. Hajipour, V., Rahmati, S.H.A., Pasandideh, S.H.R.
and Niaki, S.T.A. A multi-objective harmony search
algorithm to optimize multi-server location-allocation
problem in congested systems", Computers & Industrial
Engineering, 72, pp. 187-197 (2014b).
24. Patra, K. and Kumar Mondal, S. Multi-item supplier
selection model with fuzzy risk analysis studied by possibility
and necessity constraints", Fuzzy Information
and Engineering, 7(4), pp. 451-474 (2015).
25. Orji, I.J. and Wei, S. An innovative integration
of fuzzy-logic and systems dynamics in sustainable
supplier selection: A case on manufacturing industry",
Computers & Industrial Engineering, 88, pp. 1-12
(2015).
26. Rahiminezhad Galankashi, M., Helmi, S.A. and
Hashemzahi, P. Supplier selection in automobile
industry: A mixed balanced scorecard-fuzzy AHP
approach", Alexandria Engineering Journal, In press
(2016). DOI: 10.1016/j.aej..01.005
27. Amorim, P., Curcio, E., Almada-Lobo, B., Barbosa-
Povoa, A.P., and Grossmann, I.E. Supplier selection
in the processed food industry under uncertainty",
European Journal of Operational Research, 252(3), pp.
801-814 (2016).
28. C ebi, F. and Otay, I. A two-stage fuzzy approach
for supplier evaluation and order allocation problem
with quantity discounts and lead time", Information
Sciences, 339(20), pp. 143-157 (2016).
29. Niaki, S.T.A., Taleizadeh, A. and Barzinpour, F.
Multiple-buyer multiple-vendor multi-product multiconstraint
supply chain problem with stochastic demand
and variable lead-time", Applied Mathematics
and Computation, 217, pp. 9234-9253 (2011).
30. Jiuping, Xu, Qiang, Liu, Rui Wang. A class of
multi-objective supply chain networks optimal model
under random fuzzy environment and its application to
the industry of Chinese liquor", Information Sciences,
178, pp. 2022-2043 (2008).
31. Kamran, S. and Moghaddam, K.S. Fuzzy multiobjective
model for supplier selection and order allocation
in reverse logistics systems under supply and demand
uncertainty", Expert Systems with Applications,
42(15-16), pp. 6237-6254 (2015).
32. Sodenkamp, M.A., Tavana, M. and Di Caprio, D.
Modeling synergies in multi-criteria supplier selection
and order allocation: An application to commodity
trading", European Journal of Operational Research,
254(3), pp. 859-874 (2016)
33. Mei, Y., Salim, F.D. and Li, X. Ecient metaheuristics
for the multi-objective time-dependent orienteering
problem", European Journal of Operational
Research, 254(2), pp. 443-457 (2016).
34. Ozcan-Deniz, G. and Zhu, Y. Multi-objective optimization
of greenhouse gas emissions in highway
construction projects", Sustainable Cities and Society,
28, pp. 162-171 (2017).
35. Hussain, M., Khan, M. and Al-Aomar, R. A framework
for supply chain sustainability in service industry
with con rmatory factor analysis", Renewable and
Sustainable Energy Reviews, 55, pp. 1301-1312 (2016).
36. Sadeghi, J., Mousavi, S.M., Niaki, S.T.A. and Sadeghi,
S. Optimizing a multi-vendor multi-retailer vendor
managed inventory problem: Two tuned metaheuristic
algorithms", Knowledge-Based Systems, 50,
pp. 159-170 (2013).
37. Lu, J., Han, J., Hu, Y. and Zhang, G. Multilevel
decision-making: A survey", Information Sciences,
346, pp. 463-487 (2016).
38. Lemmens, S., Decouttere, C., Vandaele, N. and
Bernuzzi, M. A review of integrated supply chain
network design models: Key issues for vaccine supply
chains", Chemical Engineering Research and Design,
109, pp. 366-384 (2016).
39. Zadeh, L. Fuzzy set as a basis for a theory of possibility",
Fuzzy Sets and Systems, 1, pp. 3-28 (1978).
40. Chen, C. Extensions of TOPSIS for group decision
making under fuzzy environment", Fuzzy Set and
System, 114, pp. 1-9 (2000).
A. Hajikhani et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 431{449 449
41. Ross, T.J., Fuzzy Logic with Engineering Applications,
John Willey & Sons (2005).
42. Deb, K., Pratap, A., Agarwal, S. and Meyarivan,
T.A.M.T. A fast and elitist multiobjective genetic
algorithm: NSGA-II", IEEE Transactions on Evolutionary
Computation, 6(2), pp. 182-197 (2002).
43. Coello Coello, Carlos, Lamont, Gary B., van Veldhuizen,
David A., Evolutionary Algorithms for Solving
Multi-Objective Problems, New York: Kluwer Academic
Publishers (2002).
44. Eberhart, R. and Kennedy, J. A new optimizer using
particle swarm theory", In Proceedings of the Sixth
International Symposium on Micro and Machine and
Human Science, 47, pp. 39-43 (1995).
45. Boyd, R. and Richerson, P.J., Culture and the Evolutionary
Process, University of Chicago Press, Chicago
(1985).
46. Boeringer, D.W. and Werner, D.H. Particle swarm
optimization versus genetic algorithms for phased array
synthesis", IEEE Trans. Antennas Propag., pp.
771-779 (2004).
47. Hajipour, V. and Pasandideh, S.H.R. Proposing an
adaptive particle swarm optimization for a novel biobjective
queuing facility location model", Economic
Computation and Economic Cybernetics Studies and
Research, 47(3), pp. 112-129 (2012).
48. Szidarovszky, F., Gersbon, M.E. and Duckstein, L.,
Techniques for Multiobjective Decision Making in Systems
Management, Elsevier Publishers B.V. (1985).
49. Boloori Arabani, A., Zandieh, M. and Fatemi Ghomi,
S. Multi-objective genetic-based algorithms for a
cross-docking scheduling problem", Applied Soft Computing,
pp. 4954-4970 (2011).