Abstract
Supply chain network design (SCND) is one of the important, primary and strategic decisions affecting competitive advantages and all other decisions in supply chain management. Although most of papers in SCND focus only on the economic performance, this study considers simultaneously economic, social and environmental aspects. In this study, a new mixed integer nonlinear programming model is developed to formulate a multi-objective sustainable closed-loop supply chain network design problem by considering discount supposition in the transportation costs for the first time. In order to address the problem, not only traditional and recent metaheuristics are utilized, but also the algorithms are hybridized according to their strengths especially in intensification and diversification. To evaluate the efficiency and effectiveness of these algorithms, they are compared with each other by four assessment metrics for Pareto optimal analyses. Although the results indicate the performance of three proposed new hybridization algorithms, KAGA achieves better solutions compared with the others, but it needs more time. At the end, we introduced a real industrial example in glass industry to verify the proposed model and the algorithms.
Similar content being viewed by others
References
Alemdag S, Gurocak Z, Cevik A, Cabalar AF, Gokceoglu C (2016) Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming. Eng Geol 203:70–82
Ali ES (2017) ICA-based speed control of induction motor fed by wind turbine. Neural Comput Appl 28(5):1069–1077
Amin GR, Toloo M (2007) Finding the most efficient DMUs in DEA: an improved integrated model. Comput Ind Eng 52(1):71–77
Aras N, Aksen D (2008) Locating collection centers for distance-and incentive-dependent returns. Int J Prod Econ 111(2):316–333
Ardalan Z, Karimi S, Naderi B, Arshadi Khamseh A (2016) Supply chain networks design with multi-mode demand satisfaction policy. Comput Ind Eng 96:108–117
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation, Singapore, pp 4661–4667
Babazadeh R, Razmi J, Pishvaee MS, Rabbani M (2017) A sustainable second-generation biodiesel supply chain network design problem under risk. Omega 66:258–277
Bagher M, Zandieh M, Farsijani H (2011) Balancing of stochastic U-type assembly lines: an imperialist competitive algorithm. The International Journal of Advanced Manufacturing Technology 54(1):271–285
Behnamian J, Fatemi Ghomi S (2011) Hybrid flowshop scheduling with machine and resource-dependent processing times. Appl Math Model 35(3):1107–1123
Bhattacharya CB, Sen S (2004) When, why, and how consumers respond to social initiatives. Calif Manag Rev 47(1):9–24
Brandenburg M, Govindan K, Sarkis J, Seuring S (2014) Quantitative models for sustainable supply chain management: developments and directions. Eur J Oper Res 233(1):299–312
Chaabane A, Ramudhin A, Paquet M (2012) Design of sustainable supply chains under the emission trading scheme. Int J Prod Econ 135(1):37–49
Chen G, Govindan K, Golias MM (2013) A queueing network based multiobjective model to reduce truck emissions at container terminals. Transportation Part E 55:3–22
Cruz-Rivera R, Ertel J (2009) Reverse logistics network design for the collection of end-of-life vehicles in Mexico. Eur J Oper Res 196(3):930–939
Dasci A, Verter V (2001) A continuous model for production–distribution system design. Eur J Oper Res 129(2):287–298
Dehghanian F, Mansour S (2009) Designing sustainable recovery network of end-of-life products using genetic algorithm. Resour Conserv Recycl 53(10):559–570
Demirel NÖ, Gökçen H (2008) A mixed integer programming model for remanufacturing in reverse logistics environment. International J Adv Manuf Technol 39(11–12):1197–1206
Devika K, Jafarian A, Nourbakhsh V (2014) Designing a sustainable closed-loop supply chain network based on triple bottom line approach: a comparison of metaheuristics hybridization techniques. European Journal of Operation Research 235(3):594–615
Eckert C, Gottlieb J (2002) Direct representation and variation operators for the fixed charge transportation problem. In: Guervós J, Adamidis P, Beyer H-G, Schwefel HP, Fernández-Villacañas J-L (eds) Parallel problem solving from nature—PPSN VII. Springer, Berlin, pp 77–87
El-Fallahi A, Martí R, Lasdon L (2005) Path relinking and GRG for artificial neural networks. Eur J Oper Res 169(2):508–519
Elhedhli S, Merrick R (2012) Green supply chain network design to reduce carbon emissions. Transportation Research Part D: Transport and Environment 17(5):370–379
Farahani RZ, Rezapour S, Drezner T, Fallah S (2014) Competitive supply chain network design: an overview of classification, models, solution technique and application. Omega 45:92–118
Fathollahi Fard AM, Hajiaghaei-Keshteli M (2016) Red Deer Algorithm (RDA); a new optimization algorithm inspired by Red Deers’ mating. In: 12th International conference on industrial engineering (ICIE 2016), IEEE, Tehran, Iran, pp 34–35
Fathollahi Fard AM, Gholian-Jouybari F, Paydar MM, Hajiaghaei-Keshteli M (2017) A bi-objective stochastic closed-loop supply chain network design problem considering downside risk. Industrial Engineering & Management Systems 16(3):342–362
Fathollahi Fard AM, Hajiaghaei-Keshteli M (2018) A tri-level location-allocation model for forward/reverse supply chain. Appl Soft Comput 62:328–346
Fleischmann M, Beullens P, Bloemhof-Ruwaard JM, Van Wassenhove LN (2001) The impact of product recovery on logistics network design. Production and Operations Management 10(2):156–173
Fombrun CJ (2005) The leadership challenge: building resilient corporate reputations. In: Doh JP, Stumpf SA (eds) Handbook on responsible leadership and governance in global business, vol 54. Edward Elgar Publishing, Cheltenham, p 68
Fonseca MC, García-Sánchez Á, Ortega-Mier M, Saldanha-da-Gama F (2010) A stochastic bi-objective location model for strategic reverse logistics. Top 18(1):158–184
Garcia-Najera A, Bullinaria JA (2011) An improved multi-objective evolutionary algorithm for thevehicle routing problem with time windows. Comput Oper Res 38(1):287–300
Gendreau M (2003) An introduction to Tabu search. In: Glover F, Kochenberger GA (eds) Handbook of metaheuristics. Kluwer Academic Publishers, Boston, pp 37–54
Georgiadis MC, Tsiakis P, Longinidis P, Sofioglou MK (2011) Optimal design of supply chain networks under uncertain transient demand variations. Omega 39(3):254–272
Glover F (1977) Heuristics for integer programming using surrogate constraints. Decis Sci 8(1):156–166
Glover F, Laguna M, Martí R (2007) Principles of Tabu search. In: Gonzalez T (ed) Handbook on approximation algorithms and metaheuristics. Chapman and Hall/CRC, Boca Raton
Govindan K, Jafarian A, Khodaverdi R, Devika K (2014) Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food. Int J Prod Econ 152:9–28
Govindan K, Jafarian A, Nourbakhsh V (2015) Biobjective 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
Ha AY, Li L, Ng SM (2003) Price and delivery logistics competition in a supply chain. Manag Sci 49(9):1139–1153
Hajiaghaei-Keshteli M, Aminnayeri M (2013) Keshtel Algorithm (KA); a new optimization algorithm inspired by Keshtels’ feeding. In: Proceeding in IEEE conference on industrial engineering and management systems, pp 2249–2253
Holland JH (1975) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Michigan, Ann Arbor
Hsu H-W, Wang H-F (2009) “Modeling of green supply logistics. In: Wang H-F (ed) Web-based green products life cycle management systems: Reverse supply chain utilization”. IGI Global Publication, USA, pp 268–282
Koç Ç (2017) An evolutionary algorithm for supply chain network design with assembly line balancing. Neural Comput Appl 28(11):3183–3195
Krikke HR, van Harten A, Schuur PC (1999) Business case Oce: reverse logistic network re-design for copiers. OR-Spektrum 21(3):381–409
Jabbarzadeh A, Pishvaee MS, Papi A (2016) A multi-period fuzzy mathematical programming model for crude oil supply chain network design considering budget and equipment limitations. Journal of Industrial and Systems Engineering 9:88–107
Jayaraman V, Pirkul H (2001) Planning and coordination of production and distribution facilities for multiple commodities. Eur J Oper Res 133(2):394–408
Jayaraman V, Ross A (2003) A simulated annealing methodology to distribution network design and management. Eur J Oper Res 144(3):629–645
Jo J-B, Li Y, Gen M (2007) Nonlinear fixed charge transportation problem by spanning tree-based genetic algorithm. Comput Ind Eng 53(2):290–298
Kannan D, Diabat A, Alrefaei M, Govindan K, Yong G (2012) A carbon footprint based reverse logistics network design model. Resour Conserv Recycl 67:75–79
Kirkpatrick S, Gelatto CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680
Ko HJ, Evans GW (2007) A genetic algorithm-based heuristic for the dynamic integratedforward/reverse logistics network for 3PLs. Comput Oper Res 34(2):346–366
Lee DH, Dong M (2008) A heuristic approach to logistics network design for end-of-lease computer products recovery. Transp Res Part E: Logist Transp Rev 44(3):455–474
Lemmens S, Decouttere C, Vandaele N, Bernuzzi M (2016) A review of integrated supply chain network design models: key issues for vaccine supply chains. Chem Eng Res Des 109:366–384
Listeş O, Dekker R (2005) A stochastic approach to a case study for product recovery network design. Eur J Oper Res 160(1):268–287
Lotfi MM, Tavakkoli-Moghaddam R (2013) A genetic algorithm using priority-based encoding with new operators for fixed charge transportation problems. Appl Soft Comput 13:2711–2726
Lu Z, Bostel N (2007) A facility location model for logistics systems including reverse flows: the case of remanufacturing activities. Comput Oper Res 34(2):299–323
Marin A, Pelegrín B (1998) The return plant location problem: modelling and resolution. Eur J Oper Res 104(2):375–392
Min H, Ko CS, Ko HJ (2006) The spatial and temporal consolidation of returned products in aclosed-loop supply chain network. Comput Ind Eng 51(2):309–320
Min H, Ko HJ (2008) The dynamic design of a reverse logistics network from the perspective of thirdparty logistics service providers. Int J Prod Econ 113(1):176–192
Miranda PA, Garrido RA (2004) Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand. Transportation Research Part E: Logistics and Transportation Review 40(3):183–207
Myers RH, Montgomery DC, Anderson-Cook CM (2009) Response surface methodology: process and product optimization using designed experiments. Wiley, Hoboken
Pishvaee MS, Torabi SA, Razmi J (2012) Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Comput Ind Eng 62(2):624–632
Pishvaee M, Kianfar K, Karimi B (2010) Reverse logistics network design using simulated annealing. The International Journal of Advanced Manufacturing Technology 47(1):269–281
Pishvaee MS, Rabbani M, Torabi SA (2011) A robust optimization approach to closed-loop supply chain network design under uncertainty. Appl Mathemat Model 35(2):637–649
Pishvaee M, Razmi J, Torabi SA (2012) Robust possibilistic programming for socially responsible supply chain network design: a new approach. Fuzzy Sets Syst 206:1–20
Pokharel S, Mutha A (2009) Perspectives in reverse logistics: a review. Resour Conserv Recycl 53(4):175–182
Price KV, Storn R (1997) Differential evolution: a simple evolution strategy for fast optimization. Dr. Dobb’s Journal 22(4):18–24
Sadeghi-Moghaddam S, Hajiaghaei-Keshteli M, Mahmoodjanloo M (2017) New approaches in metaheuristics to solve the fixed charge transportation problem in a fuzzy environment. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3027-3
Sasikumar P, Kannan G (2008) Issues in reverse supply chains, part I: end-of-life product recovery and inventory management—an overview. International Journal of Sustainable Engineering 1(3):154–172
Sasikumar P, Kannan G (2008) Issues in reverse supply chain, part II: reverse distribution issues—an overview. International Journal of Sustainable Engineering 1(4):234–249
Sasikumar P, Kannan G (2009) Issues in reverse supply chain, part III: classification and simple analysis. International Journal of Sustainable Engineering 2(1):2–27
Spar DL, La Mure LT (2003) The power of activism: assessing the impact of NGOs on global business. Calif Manag Rev 45(3):78–101
Seuring S, Müller M (2008) From a literature review to a conceptual framework for sustainable supply chain management. J Clean Prod 16(15):1699–1710
Syarif A, Yun Y, Gen M (2002) Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach. Comput Ind Eng 43(1–2):299–314
Tang CS, Zhou S (2012) Research advances in environmentally and socially sustainable operations. Eur J Oper Res 223(3):585–594
Tang XS, Wei H (2017) A segment-wise prediction based on genetic algorithm for object recognition. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3189-z
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
Tsiakis P, Papageorgiou LG (2008) Optimal production allocation and distribution supply chainnetworks. Int J Prod Econ 111(2):468–483
Wang F, Lai X, Shi N (2011) A multi-objective optimization for green supply chain network design. Decis Support Syst 51(2):262–269
Salema MIG, Póvoa APB, Novais AQ (2009) A strategic and tactical model for closed-loopsupply chains. OR spectr 31(3):573–599
Srivastava SK (2008) Network design for reverse logistics. Omega 36(4):535–548
Soleimani H, Esfahani MS, Govindan K (2014) Incorporating risk measures in closed-loop supply chain network design. Int J Prod Res 52(6):1843–1867
Simchi-Levi D, Kaminsky P, Simchi-Levi E (2000) Designing and managing the supply chain. Irwin McGraw-Hill, New York
Schultmann F, Zumkeller M, Rentz O (2006) Modeling reverse logistic tasks within closed-loop supply chains: an example from the automotive industry. Eur J Oper Res 171(3):1033–1050
Soleimani H, Kannan G (2015) A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modeling 39(14):3990–4012
Su CT, Lin HC (2011) Applying electromagnetism-like mechanism for feature selection. Inf Sci 181(5):972–986
Tsao YC, Lu JC (2012) A supply chain network design considering transportation cost. Transp Res Part E 48:401–414
Vahdani B, Zandieh M (2010) Scheduling trucks in cross-docking systems: robust meta-heuristics. Comput Ind Eng 58(1):12–24
Van Der Laan E, Salomon M, Dekker R, Van Wassenhove L (1999) Inventory control in hybrid systems with remanufacturing. Manage Sci 45(5):733–747
Wang HF, Hsu HW (2010) A closed-loop logistic model with a spanning-tree based genetic algorithm. Comput Oper Res 37(2):376–389
Xu S, Wang Y, Lu P (2017) Improved imperialist competitive algorithm with mutation operator for continuous optimization problems. Neural Comput Appl 28(7):1667–1682
Yao MJ, Hsu HW (2009) A new spanning tree-based genetic algorithm for the design of multi-stage supply chain networks with nonlinear transportation costs. Optimization and Engineering 10(2):219–237
Yeh P (2005) Optical waves in layered media, vol 6. Wiley-Interscience
Yi P, Huang M, Guo L, Shi T (2016) A retailer oriented closed-loop supply chain network design for end of life construction machinery remanufacturing. J Clean Prod 124:191–203
Zohal M, Soleimani H (2016) Developing an Ant colony approach for green closed-loop supply chain network design: a case study in gold industry. J Clean Prod 133:314–337
Ziane I, Benhamida F, Graa A (2017) Simulated annealing algorithm for combined economic and emission power dispatch using max/max price penalty factor. Neural Comput Appl 28(1):197–205
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hajiaghaei-Keshteli, M., Fathollahi Fard, A.M. Sustainable closed-loop supply chain network design with discount supposition. Neural Comput & Applic 31, 5343–5377 (2019). https://doi.org/10.1007/s00521-018-3369-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-018-3369-5