A hybrid metaheuristic algorithm for data driven leagile sustainable closed-loop supply chain modeling under disruption risk

Document Type : Article

Authors

1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Department of Industrial Engineering, Khatam University, Tehran, Iran

Abstract

In the current situation,taking into consideration the environmental and social issues are related with the production and distribution of products in supplychain systems,due tothe global concerns related with emitting lots of greenhouse gaseswithin the manufacturing process and overlooking the major needs of publicThis paper proposes a newmultiobjectivemodel in the area ofclosed loop supplychainproblem integrated with lot sizing by considering lean,agility and sustainability factors simultaneously.In this regard,responsiveness, environmental,social and economic aspects are regarded in the model besides the capacity and service level constraints.Inaddition,strategic and operational backup decisions are developed to increase the resiliency of the system against disruption of the facilities and routs simultaneously.Next,a robust possibilistic programming approach is applied to handle the uncertainty of the model.To increase the responsiveness of the system,a fuzzyc-means clusteringmethod isapplied to select the potential locations based on the proximity to local customers.In the following, a new hybrid metaheuristic algorithm comprised of a PMOPSO algorithm and aMOSEO is developed to deal with large size problems efficiency and to assess the impact of using a single-based initial solution as the income for the second phase of the proposed hybrid algorithm.To ensure about the effectiveness of the proposed hybrid algorithm,the results of this algorithm arecompared with a NSGA-II.

Keywords

References

References:
[1] Widyadana, G. A., and Irohara, T. “Modelling multi-tour inventory routing problem for deteriorating items with time windows”. Scientia Iranica, 26(2), 932-941(2019).
[2] Esfandiyari, Z., Bashiri, M., and Tavakkoli-Moghaddam, R. “Resilient network design in a location-allocation problem with multi-level facility hardening”. Scientia Iranica, 26(2), 996-1008 (2019).
[3] Veysmoradi, D., Vahdani, B., Sartangi, M. F., and Mousavi, S. M. “Multi-objective open location-routing model for relief distribution networks with split delivery and multi-mode transportation under uncertainty”. Scientia Iranica. Transaction E, Industrial Engineering, 25(6), 3635-3653(2018).
[4] Z. Pan, J. Tang, and O. Liu. “Capacitated dynamic lot sizing problems in closed-loop supply chain”. Eur J Oper Res; 198(3) 810-821(2009). 
[5] H. Soleimani, and G. Kannan. “A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks”. Appl Math Model; 39(14) 3990-4012 (2015).
[6] G. Kannan, P. Sasikumar, and K. Devika. “A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling”. Appl Math Model; 34(3) 655-670 (2010).
[7] S. Torkaman, S. F. Ghomi, and B. Karimi. “Multi-stage multi-product multi-period production planning with sequence-dependent setups in closed-loop supply chain”. Comput Ind Eng; 113602-613(2017). 
[8] R. As' ad, M. Hariga, and O. Alkhatib. “Two stage closed loop supply chain models under consignment stock agreement and different procurement strategies”. Appl Math Model; 65 164-186 (2019).
[9] P. Hasanov, M. Y. Jaber, and N. Tahirov. “Four-level closed loop supply chain with remanufacturing”. Appl Math Model; 66 141-155 (2019). 
[10] B. Fahimnia, J. Sarkis, F. Dehghanian, N. Banihashemi, and S. Rahman. “The impact of carbon pricing on a closed-loop supply chain: an Australian case study”. J Clean Prod; 59  210-225 (2013). 
[11] 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”. Comput Oper Res; 62  112-130 (2015).
[12] H. Mokhtari, and A. Hasani. “A multi-objective model for cleaner production-transportation planning in manufacturing plants via fuzzy goal programming”. J Manuf Syst; 44 230-242 (2017).
[13] M. K. Chalmardi, and J. F. Camacho-Vallejo. “A bi-level programming model for sustainable supply chain network design that considers incentives for using cleaner technologies”. J Clean Prod; 213 1035-1050 (2019). 
[14] F. Ciccullo, M. Pero, and M. Caridi. “Exploring the hidden potential of product design to mitigate supply chain risk”. Int J Elect Custo Rela Manag; 11(1) 66-93 (2017).
[15] S. R. Cardoso, A. P. Barbosa-Póvoa, S. Relvas, and A. Q. “Novais. Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty”. Omega; 56 53-73 (2015). 
[16] F. Mohammaddust, S. Rezapour, R. Z. Farahani, M. Mofidfar, and A. Hill. “Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs”. Int J Prod Econ; 183 632-653 (2017). 
[17] M. Rohaninejad, R. Sahraeian, and R. Tavakkoli-Moghaddam. “Multi-echelon supply chain design considering unreliable facilities with facility hardening possibility”. Appl Math Model; 62 321-337(2018). 
[18] Ghobadi, M., Arkat, J., and Tavakkoli-Moghaddam, R. “Hypercube queuing models in emergency service systems: A state-of-the-art review”. Scientia Iranica. Transaction E, Industrial Engineering, 26(2), 909-931(2019).
[19] B. Vahdani, R. Tavakkoli-Moghaddam, M. Modarres, and A. Baboli. “Reliable design of a forward/reverse logistics network under uncertainty: a robust-M/M/c queuing model”. Transport Res E-Log; 48(6) 1152-1168 (2012). 
[20] S. Saeedi, M. Mohammadi, and S. Torabi. “A De Novo programming approach for a robust closed-loop supply chain network design under uncertainty: An M/M/1 queueing model”. Int J Ind Eng Comput; 6(2) 211-228 (2015). 
[21] B. Vahdani, and M. Mohammadi. “A bi-objective interval-stochastic robust optimization model for designing closed loop supply chain network with multi-priority queuing system”. Int J Prod Econ; 170 67-87 (2015).
[22] M. Mousazadeh, S. Torabi, and B. Zahiri. “A robust possibilistic programming approach for pharmaceutical supply chain network design”. Comput Chem Eng; 82 115-128 (2015). 
[23] N. S. Sadghiani, S. Torabi, and N. Sahebjamnia. “Retail supply chain network design under operational and disruption risks”. Transport Res E-Log; 75 95-114 (2015). 
[24] J. Kim, B. Do Chung, Y. Kang, and B. Jeong. Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. J Clean Prod; 196 1314-1328 (2018). 
[25] V. Hajipour, M. Tavana, D. Di Caprio, M. Akhgar, and Y. Jabbari. “An Optimization Model for Traceable Closed-Loop Supply Chain Networks”. Appl Math Model; 71 673-699 (2019). 
[26] J. W. Escobar, R. Linfati, and P. Toth. “A two-phase hybrid heuristic algorithm for the capacitated location-routing problem”. Comput Oper Res; 40(1) 70-79 (2013). 
[27] B. Karimi, S. F. Ghomi, and J. M. Wilson. “The capacitated lot sizing problem: a review of models and algorithms”. Omega; 31(5) 365-378 (2003). 
[28] A. M. Fathollahi-Fard, M. Hajiaghaei-Keshteli, and R. Tavakkoli-Moghaddam. “The social engineering optimizer (SEO)”. Eng Appl Artif Intel; 72 267-293 (2018). 
[29] F. Dehghanian and S. Mansour. “Designing sustainable recovery network of end-of-life products using genetic algorithm”. Res, Conse Recy, 53(10), 559-570(2009).
[30] N. Sahebjamnia, A. M. Fathollahi-Fard and M. Hajiaghaei-Keshteli. “Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks”. Jour clea prod, 196, 273-296 (2018).
[31] M. Hajiaghaei-Keshteli and A. M. Fathollahi-Fard. “Sustainable closed-loop supply chain network design with discount supposition”. Neur Comp Appli, 31(9), 5343-5377(2019).
[32] A. A. Taleizadeh, F. Haghighi and S. T. A. Niaki. “Modeling and solving a sustainable closed loop supply chain problem with pricing decisions and discounts on returned products”. Jour clea prod, 207, 163-181(2019).
[33] M. Safaeian, A. M. Fathollahi-Fard, G. Tian, Z. Li, and H. Ke. “A multi-objective supplier selection and order allocation through incremental discount in a fuzzy environment”. Jour Int Fuzz Sys, (Preprint), 1-21(2019).
[34] A. M. Fathollahi-Fard, K. Govindan, M. Hajiaghaei-Keshteli and A. Ahmadi. “A green home health care supply chain: New modified simulated annealing algorithms”. Jour Clea Prod, 240, 118200 (2019).
[35] Y. Feng Zhang, G. Tian, A. M. Fathollahi-Fard, N. Hao, Z. Li, ... and J. Tan. “A novel hybrid fuzzy grey TOPSIS method: Supplier evaluation of a collaborative manufacturing enterprise”. App Sci, 9(18), 3770 (2019).
[36] X. Liu, G. Tian, A. M. Fathollahi-Fard, and M.  Mojtahedi. “Evaluation of ship’s green degree using a novel hybrid approach combining group fuzzy entropy and cloud technique for the order of preference by similarity to the ideal solution theory”. Clea Tech Envi Pol, 22(2), 493-512 (2020).
[37] A. M. Fathollahi-Fard, M. Hajiaghaei-Keshteli, and S. Mirjalili. “Multi-objective stochastic closed-loop supply chain network design with social considerations”. App So Compu, 71, 505-525 (2018).
[38] E. Araee, N.Manavizadeh, , S. Aghamohammadi Bosjin. “Designing a multi-objective model for a hazardous waste routing problem considering flexibility of routes and social effects”. Jour Ind Prod Eng, 37(1), 33-45 (2020).
[39] S. Aghamohammadi-Bosjin, M. Rabbani and R. Tavakkoli-Moghaddam,. “Agile two-stage lot-sizing and scheduling problem with reliability, customer satisfaction and behaviour under uncertainty: a hybrid metaheuristic algorithm”. Engine Opti, 1-21(2019).
[40] A. M. Fathollahi-Fard, M. Hajiaghaei-Keshteli, G.Tian and Z. Li. “An adaptive Lagrangian relaxation-based algorithm for a coordinated water supply and wastewater collection network design problem”. Info Scien, 512, 1335-1359 (2020).
[41] M. Ramezani, M. Bashiri and R. Tavakkoli-Moghaddam. “A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level”. Appl Math Mod, 37(1-2), 328-344(2013).
[42] X. Zhang, K. J. Du, Z. H. Zhan, S. Kwong, T. L. Gu and J.Zhang. “Cooperative Coevolutionary Bare-Bones Particle Swarm Optimization With Function Independent Decomposition for Large-Scale Supply Chain Network Design With Uncertainties.” IEEE trans cyber (2019).
[43] S. K. Baliarsingh, W. Ding, S. Vipsita, and S. Bakshi. “A memetic algorithm using emperor penguin and social engineering optimization for medical data classification.” Appl Sof Comp, 85, 105773(2019).
[44] A. M. Fathollahi-Fard, M. Ranjbar-Bourani, N. Cheikhrouhou and M. Hajiaghaei-Keshteli. “Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system.” Comp  Indus Eng, 137, 106103 (2019).
Scientia Iranica
Volume 29, Issue 3
Transactions on Industrial Engineering (E)
May and June 2022
Pages 1685-1704

Files

Share

How to cite

Statistics