Network design for resilience in supply chains using novel crazy elitist TLBO


A resilient plant location model is proposed in this research and has been evaluated for a case problem. The model considers three major indicators of network resilience, viz. node density, node complexity and node criticality. A resilient design could ensure for cost efficiency, apart from that the likelihood of potential disruptions due to bottlenecks could be minimized. The results were optimized using a novel crazy elitist TLBO algorithm. The algorithm has been presented to solve the case problem and has been pretested for a constrained and unconstrained test function. A multi-objective decision-making model has been constructed with the flow of products as variables and was effectively solved using the meta-heuristic. The solution to the case brings insights into the design of supply network for resilience, and the managers are recommended to incorporate the concepts of resilience from the design phase itself.

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  1. 1.

    Abd-Elazim SM, Ali ES (2018) Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm. Neural Comput Appl 30(2):607–616

    Article  Google Scholar 

  2. 2.

    Altiparmak F, Gen M, Lin L, Paksoy T (2006) A genetic algorithm approach for multi-objective optimization of supply chain networks. Comput Ind Eng 51(1):196–215

    Article  Google Scholar 

  3. 3.

    Ambulkar S, Blackhurst J, Grawe S (2015) Firm’s resilience to supply chain disruptions: Scale development and empirical examination. J Oper Manag 33:111–122

    Article  Google Scholar 

  4. 4.

    Aydogdu I, Akin A (2014) Teaching and learning-based optimization algorithm for optimum design of steel buildings. Comput Civ Build Eng 2(7):2167–2175

    Google Scholar 

  5. 5.

    Biswas S, Kundu S, Bose D, Das S (2012) Cooperative co-evolutionary teaching-learning based algorithm with a modified exploration strategy for large scale global optimization. In: Swarm, evolutionary, and memetic computing, Lecture Notes in Computer Science, vol 7677, pp 467–475

    Google Scholar 

  6. 6.

    Cafaro DC, Grossmann IE (2014) Strategic planning, design, and development of the shale gas supply chain network. AIChE J 60(6):2122–2142

    Article  Google Scholar 

  7. 7.

    Cardoso SR, Barbosa-Póvoa AP, Relvas S, Novais AQ (2016) Evaluating supply chain resilience under different types of disruption. In: Computational management science. Springer, pp 123–129

  8. 8.

    Cheng MY, Prayogo D (2018) Fuzzy adaptive teaching–learning-based optimization for global numerical optimization. Neural Comput Appl 29(2):309–327

    Article  Google Scholar 

  9. 9.

    Cheng Y-H (2013) A novel optimization method for picking PCR oligonucleotide primers. Int J Comput Sci Electron Eng 1(4):518–523

    Google Scholar 

  10. 10.

    Craighead CW, Blackhurst J, Rungtusanatham MJ, Handfield RB (2007) The severity of supply chain disruptions: design characteristics and mitigation capabilities. Decis Sci 38(1):131–156

    Article  Google Scholar 

  11. 11.

    Daljit K, Ranjit K (2013) A design of IIR based digital hearing aids using teaching-learning-based optimization. Int J Comput Eng Appl 3(2–3):180–190

    Google Scholar 

  12. 12.

    Das K, Lashkari RS, Mehta M (2014) Designing a resilient supply management system for a supply chain. In: IIE annual conference. Proceedings. Institute of Industrial Engineers-Publisher, p 301

  13. 13.

    Dixit V, Seshadrinath N, Tiwari MK (2016) Performance measures based optimization of supply chain network resilience: a NSGA-II + Co-Kriging approach. Comput Ind Eng 1:1.

    Article  Google Scholar 

  14. 14.

    Dwived S, Mishra V, Kosta Y (2015) Application of teaching learning based optimization in antenna designing. Adv Electromagn 4(1):68–73

    Article  Google Scholar 

  15. 15.

    Fallah H, Eskandari H, Pishvaee MS (2015) Competitive closed-loop supply chain network design under uncertainty. J Manuf Syst 37:649–661

    Article  Google Scholar 

  16. 16.

    Farrokh M, Azar A, Jandaghi G, Ahmadi E (2018) A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty. Fuzzy Sets Syst 341:69–91

    MathSciNet  MATH  Article  Google Scholar 

  17. 17.

    Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Mirjalili S (2018) Hybrid optimizers to solve a tri-level programming model for a tire closed-loop supply chain network design problem. Appl Soft Comput 70:701–722

    Article  Google Scholar 

  18. 18.

    French S (2015) Cynefin: uncertainty, small worlds and scenarios. J Oper Res Soc 66(10):1635–1645

    Article  Google Scholar 

  19. 19.

    Gong J, Mitchell JE, Krishnamurthy A, Wallace WA (2014) An interdependent layered network model for a resilient supply chain. Omega 46:104–116

    Article  Google Scholar 

  20. 20.

    Gonzalez-Álvarez DL, Vega-Rodriguez MA, Gomez-Pulido JA, Sanchez-Pérez JM (2012) Predicting DNA motifs by using evolutionary multiobjective optimization. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):913–925

    Article  Google Scholar 

  21. 21.

    Govindan K, Fattahi M, Keyvanshokooh E (2017) Supply chain network design under uncertainty: a comprehensive review and future research directions. Eur J Oper Res 263(1):108–141

    MathSciNet  MATH  Article  Google Scholar 

  22. 22.

    Haddadsisakht A, Ryan SM (2018) Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax. Int J Prod Econ 195:118–131

    Article  Google Scholar 

  23. 23.

    Hajiaghaei-Keshteli M, Fard AMF (2018) Sustainable closed-loop supply chain network design with discount supposition. Neural Comput Appl.

    Article  Google Scholar 

  24. 24.

    Hohenstein NO, Feisel E, Hartmann E, Giunipero LC (2015) Research on the phenomenon of supply chain resilience: a systematic review and paths for further investigation. Int J Phys Distrib Logist Manag 45(1/2):90–117

    Article  Google Scholar 

  25. 25.

    Huang J, Gao L, Li X (2015) A teaching–learning-based cuckoo search for constrained engineering design problems. Adv Glob Optim 95:375–386

    MATH  Google Scholar 

  26. 26.

    Jabbarzadeh A, Fahimnia B, Sheu JB, Moghadam HS (2016) Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transp Res Part B Methodol 94:121–149

    Article  Google Scholar 

  27. 27.

    Jabbarzadeh A, Haughton M, Khosrojerdi A (2018) Closed-loop supply chain network design under disruption risks: a robust approach with real world application. Comput Ind Eng 116:178–191

    Article  Google Scholar 

  28. 28.

    Jiang X, Zhou J (2013) Hybrid DE-TLBO algorithm for solving short term hydro-thermal optimal scheduling with incommensurable objectives. In: Proceedings of IEEE 32nd Chinese control conference, 26–28 July, Xi’an, pp 2474–2479

  29. 29.

    Jordehi AR (2015) Optimal setting of TCSCs in power systems using teaching–learning-based optimisation algorithm. Neural Comput Appl 26(5):1249–1256

    Article  Google Scholar 

  30. 30.

    Kankal M, Uzlu E (2017) Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey. Neural Comput Appl 28(1):737–747

    Article  Google Scholar 

  31. 31.

    Keyvanshokooh E, Ryan SM, Kabir E (2016) Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition. Eur J Oper Res 249(1):76–92

    MathSciNet  MATH  Article  Google Scholar 

  32. 32.

    Kim Y, Chen YS, Linderman K (2015) Supply network disruption and resilience: a network structural perspective. J Oper Manag 33:43–59

    Article  Google Scholar 

  33. 33.

    Klibi W, Martel A, Guitouni A (2010) The design of robust value-creating supply chain networks: a critical review. Eur J Oper Res 203(2):283–293

    MATH  Article  Google Scholar 

  34. 34.

    Li G, Niu PSW, Liu Y (2013) Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching–learning-based optimization. Chemometr Intell Lab Syst 126:11–20

    Article  Google Scholar 

  35. 35.

    Lim WH, Isa NAM (2014) Bidirectional teaching and peer- learning particle swarm optimization. Inf Sci 280:111–134

    Article  Google Scholar 

  36. 36.

    Mandal B, Roy PK (2014) Multi- objective optimal power flow using quasi-oppositional teaching learning based optimization. Appl Soft Comput 21:590–606

    Article  Google Scholar 

  37. 37.

    Medina MA, Coello CAC, Ramirez JM (2013) Reactive power handling by a multi- objective teaching learning optimizer based on decomposition. IEEE Trans Power Syst 28(4):3629–3637

    Article  Google Scholar 

  38. 38.

    Mohapatra A, Panigrahi BK, Singh B, Bansal R (2012) Optimal placement of capacitors in distribution networks using a modified teaching-learning based algorithm. In: Swarm, evolutionary, and memetic computing. Springer, Berlin, pp. 398–405

    Google Scholar 

  39. 39.

    Nayak J, Naik B, Behera HS, Abraham A (2018) Elitist teaching–learning-based optimization (ETLBO) with higher-order Jordan Pi-sigma neural network: a comparative performance analysis. Neural Comput Appl.

    Article  Google Scholar 

  40. 40.

    Nenavath H, Jatoth RK (2018) Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking. Neural Comput Appl.

    Article  Google Scholar 

  41. 41.

    Niknam T, Azizipanah-Abarghooee R, Narimani MR (2012) A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems. Eng Appl Artif Intell 25(8):1577–1588

    Article  Google Scholar 

  42. 42.

    Niu Q, Zhang H, Li K (2014) An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models. Int J Hydrog Energy 39(8):3837–3854

    Article  Google Scholar 

  43. 43.

    Oshaba AS, Ali ES, Elazim SA (2017) PI controller design for MPPT of photovoltaic system supplying SRM via BAT search algorithm. Neural Comput Appl 28(4):651–667

    Article  Google Scholar 

  44. 44.

    Oshaba AS, Ali ES, Elazim SA (2017) PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load. Neural Comput Appl 28(2):353–364

    Article  Google Scholar 

  45. 45.

    Park YB, Kim HS (2016) Simulation-based evolutionary algorithm approach for deriving the operational planning of global supply chains from the systematic risk management. Comput Ind 83:68–77

    Article  Google Scholar 

  46. 46.

    Patel V, Savsani V (2016) Multi-objective optimization of a Stirling heat engine using TS-TLBO (tutorial training and self-learning inspired teaching-learning based optimization) algorithm. Energy 95:528–541

    Article  Google Scholar 

  47. 47.

    Pawar PJ, Rao RV (2013) Parameter optimization of machining processes using teaching–learning-based optimization algorithm. Int J Adv Manuf Technol 67(5–8):995–1006

    Article  Google Scholar 

  48. 48.

    Pawar PJ, Rao RV (2013) Erratum to: parameter optimization of machining processes using teaching-learning-based optimization algorithm. Int J Adv Manuf Technol 67(5–8):1955

    Article  Google Scholar 

  49. 49.

    Ponis ST, Koronis E (2012) Supply chain resilience: definition of concept and its formative elements. J Appl Bus Res 28(5):921

    Article  Google Scholar 

  50. 50.

    Rad RS, Nahavandi N (2018) A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. J Clean Prod 196:1549–1565

    Article  Google Scholar 

  51. 51.

    Rajasekhar A, Rani R, Ramya K, Abraham A (2012) Elitist teaching learning opposition based algorithm for global optimization. In: Proceedings of IEEE international conference on systems, man, and cybernetics, Seoul.

  52. 52.

    Rajesh R (2017) Study of select issues of resilient supply chains. Thesis (Ph.D.),

  53. 53.

    Rajesh R (2018) Pseudo resilient supply chains: concept, traits, and practices. J Risk Res 21(10):1264–1286

    Article  Google Scholar 

  54. 54.

    Rajesh R (2018) Measuring the barriers to resilience in manufacturing supply chains using Grey Clustering and VIKOR approaches. Measurement 126:259–273

    Article  Google Scholar 

  55. 55.

    Rajesh R (2018) On sustainability, resilience, and the sustainable–resilient supply networks. Sustain Prod Consum 15:74–88

    Article  Google Scholar 

  56. 56.

    Rajesh R (2018) Group decision-making and grey programming approaches to optimal product mix in manufacturing supply chains. Neural Comput Appl.

    Article  Google Scholar 

  57. 57.

    Rajesh R (2019) Social and environmental risk management in resilient supply chains: a periodical study by the Grey-Verhulst model. Int J Prod Res.

    Article  Google Scholar 

  58. 58.

    Rajesh R (2019) A fuzzy approach to analyzing the level of resilience in manufacturing supply chains. Sustain Prod Consum 18:224–236

    Article  Google Scholar 

  59. 59.

    Rao RV (2016) Design optimization of a plate fin heat sink using TLBO and ETLBO algorithms. In: Teaching learning based optimization algorithm. Springer, Cham, pp 103–113

    Google Scholar 

  60. 60.

    Rao RV, Patel V (2012) An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 3(4):535–560

    Google Scholar 

  61. 61.

    Rao RV, Patel V (2013) Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Appl Math Model 37(3):1147–1162

    MathSciNet  MATH  Article  Google Scholar 

  62. 62.

    Rao RV, Waghmare G (2015) Design optimization of robot grippers using teaching-learning-based optimization algorithm. Adv Robot 29(6):431–447

    Article  Google Scholar 

  63. 63.

    Rao RV, Kalyankar VD, Waghmare G (2014) Parameters optimization of selected casting processes using teaching–learning-based optimization algorithm. Appl Math Model 38(23):5592–5608

    Article  Google Scholar 

  64. 64.

    Rao RV, Savsani VJ, Balic J (2012) Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Eng Optim 44(12):1447–1462

    Article  Google Scholar 

  65. 65.

    Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

  66. 66.

    Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15

    MathSciNet  Article  Google Scholar 

  67. 67.

    Rezaee A, Dehghanian F, Fahimnia B, Beamon B (2017) Green supply chain network design with stochastic demand and carbon price. Ann Oper Res 250(2):463–485

    MathSciNet  MATH  Article  Google Scholar 

  68. 68.

    Rezapour S, Farahani RZ, Fahimnia B, Govindan K, Mansouri Y (2015) Competitive closed-loop supply chain network design with price-dependent demands. J Clean Prod 93:251–272

    Article  Google Scholar 

  69. 69.

    Sadghiani NS, Torabi SA, Sahebjamnia N (2015) Retail supply chain network design under operational and disruption risks. Transp Res Part E Logist Transp Rev 75:95–114

    Article  Google Scholar 

  70. 70.

    Scholten K, Schilder S (2015) The role of collaboration in supply chain resilience. Supply Chain Manag Int J 20(4):471–484

    Article  Google Scholar 

  71. 71.

    Scholten K, Sharkey Scott P, Fynes B (2014) Mitigation processes–antecedents for building supply chain resilience. Supply Chain Manag Int J 19(2):211–228

    Article  Google Scholar 

  72. 72.

    Soleimani H, Govindan K, Saghafi H, Jafari H (2017) Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Comput Ind Eng 109:191–203

    Article  Google Scholar 

  73. 73.

    Subulan K, Baykasoğlu A, Özsoydan FB, Taşan AS, Selim H (2014) A case-oriented approach to a lead/acid battery closed-loop supply chain network design under risk and uncertainty. J Manuf Syst 37(1):340–361

    Google Scholar 

  74. 74.

    Sultana S, Roy PK (2014) Optimal capacitor placement in radial distribution systems using teaching learning based optimization. Int J Electr Power Energy Syst 54:387–398

    Article  Google Scholar 

  75. 75.

    Takami MA, Sheikh R, Sana SS (2015) Product portfolio optimisation using teaching–learning-based optimisation algorithm: a new approach in supply chain management. Int J Syst Sci Oper Logist 1:1–11.

    Article  Google Scholar 

  76. 76.

    Talaei M, Moghaddam BF, Pishvaee MS, Bozorgi-Amiri A, Gholamnejad S (2016) A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. J Clean Prod 113:662–673

    Article  Google Scholar 

  77. 77.

    Thanh PN, Bostel N, Péton O (2008) A dynamic model for facility location in the design of complex supply chains. Int J Prod Econ 113(2):678–693

    Article  Google Scholar 

  78. 78.

    Toğan V (2012) Design of planar steel frames using teaching–learning based optimization. Eng Struct 34:225–232

    Article  Google Scholar 

  79. 79.

    Tuncel G, Alpan G (2010) Risk assessment and management for supply chain networks: a case study. Comput Ind 61(3):250–259

    Article  Google Scholar 

  80. 80.

    Wang L, Zou F, Hei X, Yang D, Chen D, Jiang Q, Cao Z (2014) A hybridization of teaching–learning-based optimization and differential evolution for chaotic time series prediction. Neural Comput Appl 25(6):1407–1422

    Article  Google Scholar 

  81. 81.

    Wu T, Blackhurst J, Chidambaram V (2006) A model for inbound supply risk analysis. Comput Ind 57(4):350–365

    Article  Google Scholar 

  82. 82.

    Xu Y, Wang L, Wang SY, Liu M (2015) An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time. Neurocomputing 148:260–268

    Article  Google Scholar 

  83. 83.

    Yildiz AR (2013) Optimization of multi-pass turning operations using hybrid teaching learning-based approach. Int J Adv Manuf Technol 66:1319–1326

    Article  Google Scholar 

  84. 84.

    Zhile YANG, Kang LI, Qun NIU, Yusheng XUE, Foley A (2014) A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads. J Modern Power Syst Clean Energy 2(4):298–307

    Article  Google Scholar 

  85. 85.

    Zhong Y, Shu J, Xie W, Zhou YW (2018) Optimal trade credit and replenishment policies for supply chain network design. Omega 81:26–37

    Article  Google Scholar 

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Annexure 1: Recent literature on network design and supply chains

Sl. no. Author(s) Nature of work Remarks/findings
1. [67] Green supply network design for stochastic demand was proposed Supply network configuration was found to be highly sensitive to the probability distribution of the carbon credit price
2. [21] A review on network design problems under uncertainty conditions is completed Existing optimization techniques for dealing with uncertainty were explored in terms of mathematical modeling and solution approaches
3. [31] A hybrid robust stochastic programming approach was used for network design An accelerated stochastic Benders decomposition algorithm was proposed
4. [72] Considered the design problem of a closed-loop supply chain considering various echelons of a supply chain Sustainability and green approaches were considered in the modeling and design of the network
5. [76] A fuzzy optimization model for carbon-efficient closed-loop supply chain network design was proposed Model was observed to be capable of controlling the network uncertainties
6. [22] Closed-loop supply chain network design problem that encompasses flows in both forward and reverse directions was considered Concluded that adjusting product flows to the tax rate can provide negligible benefits
7. [23] A mixed integer nonlinear programming model for multi-objective sustainable closed-loop supply chain network design problem was developed Efficiency and effectiveness of these algorithms were compared using Pareto optimal analyses
8. [50] An integrated mathematical programming model for multi-period, multi-product and capacitated closed-loop green supply chain was developed The model objective functions considered the minimization of economic cost and environmental emissions and maximization of customer satisfaction
9. [17] A tri-level programming model for the tire closed-loop supply chain was designed Four hybrid optimizers to improving the recent and old used meta-heuristics were developed
10. [27] A stochastic robust optimization to designing a closed-loop supply chain network was proposed Facility locations and transshipments in different disruption scenarios were optimized
11. [85] An integrated supply chain network design model was proposed that incorporates payment time Deferred payment time and its impacts on the associated decisions were examined analytically and numerically
12. [16] Closed-loop supply chain network design problem under hybrid uncertainty was proposed and solved Possibility theory and robust fuzzy stochastic programming approaches were employed

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Rajesh, R. Network design for resilience in supply chains using novel crazy elitist TLBO. Neural Comput & Applic 32, 7421–7437 (2020).

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  • Supply network design
  • Resilience
  • Decision-making
  • TLBO