|
[1]
|
F. Altiparmak, M. Gen, L. Lin and T. Paksoy, A genetic algorithm approach for multi-objective optimization of supply chain networks, Computers & Industrial Engineering, 51 (2006), 196-215.
doi: 10.1016/j.cie.2006.07.011.
|
|
[2]
|
S. H. Amin and G. Zhang, A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return, Applied Mathematical Modelling, 37 (2013), 4165-4176.
doi: 10.1016/j.apm.2012.09.039.
|
|
[3]
|
R. K. Apaiah and E. M. T. Hendrix, Design of a supply chain network for pea-based novel protein foods, Journal of Food Engineering, 70 (2005), 383-391.
doi: 10.1016/j.jfoodeng.2004.02.043.
|
|
[4]
|
M. Bachlaus, M. Pandey, C. Mahajan, R. Shankar and M. Tiwari, Designing an integrated multi-echelon agile supply chain network: A hybrid taguchi-particle swarm optimization approach, Journal of Intelligent Manufacturing, 19 (2008), 747-761.
doi: 10.1007/s10845-008-0125-1.
|
|
[5]
|
A. Banasik, A. Kanellopoulos, G. Claassen, J. M. Bloemhof-Ruwaard and J. G. van der Vorst, Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain, International Journal of Production Economics, 183 (2017), 409-420.
doi: 10.1016/j.ijpe.2016.08.012.
|
|
[6]
|
X. Cai, J. Chen, Y. Xiao, X. Xu and G. Yu, Fresh-product supply chain management with logistics outsourcing, Omega, 41 (2013), 752-765.
doi: 10.1016/j.omega.2012.09.004.
|
|
[7]
|
F. T. Chan, A. Jha and M. K. Tiwari, Bi-objective optimization of three echelon supply chain involving truck selection and loading using NSGA-Ⅱ with heuristics algorithm, Applied Soft Computing, 38 (2016), 978-987.
doi: 10.1016/j.asoc.2015.10.067.
|
|
[8]
|
N. Chibeles-Martins, T. Pinto-Varela, A. P. Barbosa-Póvoa and A. Q. Novais, A multi-objective meta-heuristic approach for the design and planning of green supply chains-MBSA, Expert Systems with Applications, 47 (2016), 71-84.
doi: 10.1016/j.eswa.2015.10.036.
|
|
[9]
|
C. A. C. Coello, G. T. Pulido and M. S. Lechuga, Handling multiple objectives with particle swarm optimization, Evolutionary Computation, IEEE Transactions on, 8 (2004), 256-279.
doi: 10.1109/TEVC.2004.826067.
|
|
[10]
|
C. A. Coello and M. S. Lechuga, MOPSO: A proposal for multiple objective particle swarm optimization Evolutionary Computation, 2002. CEC'02. Proceedings of the 2002 Congress on, IEEE, (2002), 1051–1056.
doi: 10.1109/CEC.2002.1004388.
|
|
[11]
|
J. F. Cordeau, F. Pasin and M. Solomon, An integrated model for logistics network design, Annals of Operations Research, 144 (2006), 59-82.
doi: 10.1007/s10479-006-0001-3.
|
|
[12]
|
K. Deb, Multi-objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, 2001.
|
|
[13]
|
K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ, Evolutionary Computation, IEEE Transactions on, 6 (2002), 182-197.
doi: 10.1109/4235.996017.
|
|
[14]
|
J. Dou, X. Wang and L. Wang, Machining fixture layout optimisation under dynamic conditions based on evolutionary techniques, International Journal of Production Research, 50 (2012), 4294-4315.
doi: 10.1080/00207543.2011.618470.
|
|
[15]
|
J. J. Durillo, J. García-Nieto, A. J. Nebro, C. A. C. Coello, F. Luna and E. Alba, Multiobjective particle swarm optimizers: An experimental comparison, Evolutionary MultiCriterion Optimization, Springer, (2009), 495–509.
doi: 10.1007/978-3-642-01020-0_39.
|
|
[16]
|
M. Eskandarpour, P Dejax, J. Miemczyk and O. Péton, Sustainable supply chain network design: An optimization-oriented review, Omega, 54 (2015), 11-32.
|
|
[17]
|
B. Fahimnia, R. Z. Farahani, R. Marian and L. Luong, A review and critique on integrated production-distribution planning models and techniques, Journal of Manufacturing Systems, 32 (2013), 1-19.
doi: 10.1016/j.jmsy.2012.07.005.
|
|
[18]
|
H. Felfel, O. Ayadi and F. Masmoudi, A decision-making approach for a multi-objective multisite supply network planning problem, International Journal of Computer Integrated Manufacturing, 29 (2016), 754-767.
doi: 10.1080/0951192X.2015.1107916.
|
|
[19]
|
K. Govindan, A. Jafarian and V. Nourbakhsh, Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic, Computers & Operations Research, 62 (2015), 112-130.
doi: 10.1016/j.cor.2014.12.014.
|
|
[20]
|
G. Guillén, F. Mele, M. Bagajewicz, A. Espuna and L. Puigjaner, Multiobjective supply chain design under uncertainty, Chemical Engineering Science, 60 (2005), 1535-1553.
|
|
[21]
|
A. Haddadsisakht and S. M. Ryan, Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax, International Journal of Production Economics, 195 (2018), 118-131.
doi: 10.1016/j.ijpe.2017.09.009.
|
|
[22]
|
A. Hafezalkotob, K. Khalili-Damghani and S. S. Ghashami, A Three-Echelon Multi-Objective Multi-Period Multi-Product Supply Chain Network Design Problem: A Goal Programming Approach, Journal of Optimization in Industrial Engineering, 10 (2016), 67-78.
|
|
[23]
|
M. Jin, N. A. Granda-Marulanda and I. Down, The impact of carbon policies on supply chain design and logistics of a major retailer, Journal of Cleaner Production, 85 (2014), 453-461.
doi: 10.1016/j.jclepro.2013.08.042.
|
|
[24]
|
F. Jolai, J. Razmi and N. K. M. Rostami, A fuzzy goal programming and meta heuristic algorithms for solving integrated production: Distribution planning problem, Central European Journal of Operations Research, 19 (2011), 547-569.
doi: 10.1007/s10100-010-0144-9.
|
|
[25]
|
R. S. Kadadevaramath, J. C. Chen, B. Latha Shankar and K. Rameshkumar, Application of particle swarm intelligence algorithms in supply chain network architecture optimization, Expert Systems with Applications, 39 (2012), 10160-10176.
doi: 10.1016/j.eswa.2012.02.116.
|
|
[26]
|
M. Kadziński, T. Tervonen, M. K. Tomczyk and R. Dekker, Evaluation of multi-objective optimization approaches for solving green supply chain design problems, Omega, 68 (2017), 168-184.
|
|
[27]
|
J. Kennedy and R. C. Eberhart, A discrete binary version of the particle swarm algorithm, IEEE Press, 5 (1997), 4104-4108.
doi: 10.1109/ICSMC.1997.637339.
|
|
[28]
|
J. Kennedy and R. Eberhart, Particle swarm optimization, Piscataway, NJ: IEEE Service Center, 4 (1995), 1942-1948.
doi: 10.1109/ICNN.1995.488968.
|
|
[29]
|
K. Khalili-Damghani, M. Tavana and M. Amirkhan, A fuzzy bi-objective mixed-integer programming method for solving supply chain network design problems under ambiguous and vague conditions, The International Journal of Advanced Manufacturing Technology, 73 (2014), 1567-1595.
doi: 10.1007/s00170-014-5891-7.
|
|
[30]
|
S. Lee, S. Soak, S. Oh, W. Pedrycz and M. Jeon, Modified binary particle swarm optimization, Progress in Natural Science, 18 (2008), 1161-1166.
doi: 10.1016/j.pnsc.2008.03.018.
|
|
[31]
|
M. Mohammadzadeh, A. A. Khamseh and M. Mohammadi, A multi-objective integrated model for closed-loop supply chain configuration and supplier selection considering uncertain demand and different performance levels, Journal of Industrial & Management Optimization, 13 (2017), 1041-1064.
doi: 10.3934/jimo.2016061.
|
|
[32]
|
L. A. Moncayo-Martínez and D. Z. Zhang, Multi-objective ant colony optimisation: A meta-heuristic approach to supply chain design, International Journal of Production Economics, 131 (2011), 407-420.
|
|
[33]
|
K. P. Nurjanni, M. S. Carvalho and L. Costa, Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model, International Journal of Production Economics, 183 (2017), 421-432.
doi: 10.1016/j.ijpe.2016.08.028.
|
|
[34]
|
E. Olivares-Benitez, J. L. González-Velarde and R. Z. Ríos-Mercado, A supply chain design problem with facility location and bi-objective transportation choices, Top, 20 (2012), 729-753.
doi: 10.1007/s11750-010-0162-8.
|
|
[35]
|
E. Olivares-Benitez, R. Z. Rios-Mercado and J. L. Gonzalez-Velarde, A metaheuristic algorithm to solve the selection of transportation channels in supply chain design, International Journal of Production Economics, 145 (2013), 161-172.
doi: 10.1016/j.ijpe.2013.01.017.
|
|
[36]
|
K. E. Parsopoulos and M. N. Vrahatis, Particle swarm optimization method in multiobjective problems, in Proceedings of the 2002 ACM symposium on Applied computing, ACM, (2002), 603–607.
doi: 10.1145/508791.508907.
|
|
[37]
|
S. H. R. Pasandideh, S. T. A. Niaki and K. Asadi, Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-Ⅱ and NRGA, Information Sciences, 292 (2015), 57-74.
doi: 10.1016/j.ins.2014.08.068.
|
|
[38]
|
M. M. Paydar and M. Saidi-Mehrabad, Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters, International Journal of Computer Integrated Manufacturing, 28 (2015), 251-265.
doi: 10.1080/0951192X.2013.874596.
|
|
[39]
|
M. S. Pishvaee and J. Razmi, Environmental supply chain network design using multi-objective fuzzy mathematical programming, Applied Mathematical Modelling, 36 (2012), 3433-3446.
doi: 10.1016/j.apm.2011.10.007.
|
|
[40]
|
A. Pourrousta, S. Dehbari, R. Tavakkoli-Moghaddam and M. S. Amalnik, A multi-objective particle swarm optimization for production-distribution planning in supply chain network, Management Science Letters, 2 (2012), 603-614.
doi: 10.5267/j.msl.2011.11.012.
|
|
[41]
|
M. Reyes-sierra and C. A. Coello Coello, Multi-Objective particle swarm optimizers: A survey of the state-of-the-art, International Journal of Computational Intelligence Research, 2 (2006), 287-308.
|
|
[42]
|
H. Sadjady and H. Davoudpour, Two-echelon, multi-commodity supply chain network design with mode selection, lead-times and inventory costs, Computers & Operations Research, 39 (2012), 1345-1354.
doi: 10.1016/j.cor.2011.08.003.
|
|
[43]
|
K. Sarrafha, S. H. A. Rahmati, S. T. A. Niaki and A. Zaretalab, A bi-objective integrated procurement, production, and distribution problem of a multi-echelon supply chain network design: A new tuned MOEA, Computers & Operations Research, 54 (2015), 35-51.
doi: 10.1016/j.cor.2014.08.010.
|
|
[44]
|
B. L. Shankar, S. Basavarajappa, J. C. Chen and R. S. Kadadevaramath, Location and allocation decisions for multi-echelon supply chain network multi-objective evolutionary approach, Expert Systems with Applications, 40 (2013), 551-562.
doi: 10.1016/j.eswa.2012.07.065.
|
|
[45]
|
C. Shankhar and P. S. S. Prasad, Cost optimisation of supply chain networks using particle swarm optimisation, International Journal of Business Performance and Supply Chain Modelling, 2 (2010), 112-124.
doi: 10.1504/IJBPSCM.2010.036164.
|
|
[46]
|
Z. J. M. Shen, Integrated supply chain design models: a survey and future research directions, Journal of Industrial & Management Optimization, 3 (2007), 1-27.
doi: 10.3934/jimo.2007.3.1.
|
|
[47]
|
Y. Shi and R. Eberhart, A modified particle swarm optimizer, in Piscataway, NJ: IEEE Press, (1998), 69–73.
doi: 10.1109/ICEC.1998.699146.
|
|
[48]
|
S. P. Venkatesan and S. Kumanan, A Multi-Objective Discrete Particle Swarm Optimisation Algorithm for supply chain network design, International Journal of Logistics Systems and Management, 11 (2012), 375-406.
doi: 10.1504/IJLSM.2012.045919.
|
|
[49]
|
G. Q. Yang, Y. K. Liu and K. Yang, Multi-objective biogeography-based optimization for supply chain network design under uncertainty, Computers & Industrial Engineering, 85 (2015), 145-156.
doi: 10.1016/j.cie.2015.03.008.
|
|
[50]
|
M. J. Yao and H. W. Hsu, A new spanning tree-based genetic algorithm for the design of multi-stage supply chain networks with nonlinear transportation costs, Optimization and Engineering, 10 (2009), 219-237.
doi: 10.1007/s11081-008-9059-x.
|
|
[51]
|
A. Zhou, B. Y. Qu, H. Li, S. Z. Zhao, P. N. Suganthan and Q. Zhang, Multiobjective evolutionary algorithms: A survey of the state of the art, Swarm and Evolutionary Computation, 1 (2011), 32-49.
doi: 10.1016/j.swevo.2011.03.001.
|
Download:
{{journal.titleEn}} 
