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A fuzzy-budgeted robust optimization model for joint network design-pricing problem in a forward−reverse supply chain: the viewpoint of third-party logistics

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Abstract

The increasing opportunities for cost savings and customer satisfaction have propelled third-party logistics providers (3PLs) to get involved in the forward and reverse logistics operations. The forward−reverse supply chain network design (FRSCND) for 3PLs have been somehow studied under various conditions, so far. However, some very common business configurations and real-world concerns such as pricing and uncertainty are less investigated in the literature. Accordingly, this paper proposes a novel robust model for the design of a 3PL’s logistics network and pricing decisions. Since the value of uncertainty budget parameter in the robust model is an epistemic uncertain one, the fuzzy-robust model regarding the uncertainty budget parameter as a fuzzy number is also developed. The results of numerical examples show that the proposed models outperform the deterministic model regarding solution robustness and computational time. Considering the robust sensitivity analysis, the effects of uncertain parameters on total cost, based on their conservatism levels are analyzed. In addition, the conducted sensitivity analysis over penalty values for constraints violation reveal that for the medium- and large-size problems, the proposed models are more cost-effective for high penalty values, while the models’ performance is related to the problem size, for their low amounts.

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References

  • Aguezzoul A (2014) Third-party logistics selection problem: a literature review on criteria and methods. Omega 49:69–78

    Article  Google Scholar 

  • Arabzad SM, Ghorbani M, Tavakkoli-Moghaddam R (2015) An evolutionary algorithm for a new multi-objective location-inventory model in a distribution network with transportation modes and third-party logistics providers. Int J Prod Res 53(4):1038–1050

    Article  Google Scholar 

  • Aras N, Aksen D, Tanuğur AG (2008) Locating collection centers for incentive-dependent returns under a pick-up policy with capacitated vehicles. Eur J Oper Res 191(3):1223–1240

    Article  MathSciNet  MATH  Google Scholar 

  • Azadi M, Saen RF (2011) A new chance-constrained data envelopment analysis for selecting third-party reverse logistics providers in the existence of dual-role factors. Expert Syst Appl 38(10):12231–12236

    Article  Google Scholar 

  • Ben-Tal A, Nemirovski A (1999) Robust solutions of uncertain linear programs. Oper Res Lett 25(1):1–13

    Article  MathSciNet  MATH  Google Scholar 

  • Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35–53

    Article  MathSciNet  MATH  Google Scholar 

  • Bottani E, Rizzi A (2006) A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Manag Int J 11(4):294–308

    Article  Google Scholar 

  • Chang C-T, Chang C-C (2000) A linearization method for mixed 0–1 polynomial programs. Comput Oper Res 27(10):1005–1016

    Article  MathSciNet  MATH  Google Scholar 

  • Chinneck JW (2006) Practical optimization: a gentle introduction. Systems and Computer Engineering. Carleton University, Ottawa. http://www.sce.carleton.ca/faculty/chinneck/po.html

  • Choi T-M, Li D, Yan H (2004) Optimal returns policy for supply chain with e-marketplace. Int J Prod Econ 88(2):205–227

    Article  Google Scholar 

  • Daghigh R, Jabalameli M, Amiri A, Pishvaee M (2016) A multi-objective location-inventory model for 3PL providers with sustainable considerations under uncertainty. Int J Ind Eng Comput 7(4):615–634

    Google Scholar 

  • Du F, Evans GW (2008) A bi-objective reverse logistics network analysis for post-sale service. Comput Oper Res 35(8):2617–2634

    Article  MATH  Google Scholar 

  • Ecer F (2018) Third-party logistics (3PLs) provider selection via Fuzzy AHP and EDAS integrated model. Technol Econ Dev Econ 24(2):615–634

    Article  Google Scholar 

  • Fattahi M, Govindan K (2017) Integrated forward/reverse logistics network design under uncertainty with pricing for collection of used products. Ann Oper Res 253(1):193–225

    Article  MathSciNet  MATH  Google Scholar 

  • Fattahi M, Govindan K, Keyvanshokooh E (2018) A multi-stage stochastic program for supply chain network redesign problem with price-dependent uncertain demands. Comput Oper Res 100:314–332

    Article  MathSciNet  MATH  Google Scholar 

  • Fleischmann M, Krikke HR, Dekker R, Flapper SDP (2000) A characterisation of logistics networks for product recovery. Omega 28(6):653–666

    Article  Google Scholar 

  • Ghafarimoghadam A, Karimi A, Mousazadeh M, Pishvaee MS (2016) A robust optimisation model for remanufacturing network design problem with one-way substitution. Int J Serv Oper Manag 24(4):484–503

    Google Scholar 

  • Ghaffari-Nasab N, Ghazanfari M, Teimoury E (2016) Hub-and-spoke logistics network design for third party logistics service providers. Int J Manag Sci Eng Manag 11(1):49–61

    Google Scholar 

  • Ghayebloo S, Tarokh MJ, Venkatadri U, Diallo C (2015) Developing a bi-objective model of the closed-loop supply chain network with green supplier selection and disassembly of products: the impact of parts reliability and product greenness on the recovery network. J Manuf Syst 36:76–86

    Article  Google Scholar 

  • Giri B, Sarker BR (2017) Improving performance by coordinating a supply chain with third party logistics outsourcing under production disruption. Comput Ind Eng 103:168–177

    Article  Google Scholar 

  • Glover F (1975) Improved linear integer programming formulations of nonlinear integer problems. Manag Sci 22(4):455–460

    Article  MathSciNet  MATH  Google Scholar 

  • Hasani A, Zegordi SH, Nikbakhsh E (2012) Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. Int J Prod Res 50(16):4649–4669

    Article  Google Scholar 

  • Kayvanfar V, Husseini SM, Sajadieh MS, Karimi B (2018) A multi-echelon multi-product stochastic model to supply chain of small-and-medium enterprises in industrial clusters. Comput Ind Eng 115:69–79

    Article  Google Scholar 

  • Keyvanshokooh E, Fattahi M, Seyed-Hosseini S, Tavakkoli-Moghaddam R (2013) A dynamic pricing approach for returned products in integrated forward/reverse logistics network design. Appl Math Model 37(24):10182–10202

    Article  MathSciNet  MATH  Google Scholar 

  • Ko HJ, Evans GW (2007) A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Comput Oper Res 34(2):346–366

    Article  MATH  Google Scholar 

  • Ko HJ, Park BI (2005) The impacts of an integrated solution on the logistics networks of third-party logistics providers. J Eastern Asia Soc Transp Stud 6:2762–2777

    Google Scholar 

  • Kumar S, Muddada RR, Pandey M, Mahanty B, Tiwari M (2013) Logistics planning and inventory optimization using swarm intelligence: a third party perspective. Int J Adv Manuf Technol 65(9–12):1535–1551

    Article  Google Scholar 

  • Lee D-H, 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

    Article  Google Scholar 

  • Lee H, Zhang T, Boile M, Theofanis S, Choo S (2013) Designing an integrated logistics network in a supply chain system. KSCE J Civ Eng 17(4):806–814

    Article  Google Scholar 

  • Li H-L (1994) Global optimization for mixed 0–1 programs with convex or separable continuous functions. J Oper Res Soc 45(9):1068–1076

    Article  MATH  Google Scholar 

  • Li Z, Floudas CA (2012) Robust counterpart optimization: Uncertainty sets, formulations and probabilistic guarantees. In: Proceedings of the 6th conference on foundations of computer-aided process operations, Savannah (Georgia)

  • Li X, Liu B (2006) A sufficient and necessary condition for credibility measures. Int J Uncertain Fuzziness Knowl Based Syst 14(05):527–535

    Article  MathSciNet  MATH  Google Scholar 

  • Li Z, Ding R, Floudas CA (2011) A comparative theoretical and computational study on robust counterpart optimization: I. Robust linear optimization and robust mixed integer linear optimization. Ind Eng Chem Res 50(18):10567–10603

    Article  Google Scholar 

  • Li Y, Guo H, Zhang Y (2018) An integrated location-inventory problem in a closed-loop supply chain with third-party logistics. Int J Prod Res 56(10):3462–3481

    Article  Google Scholar 

  • Liang Y, Pokharel S, Lim GH (2009) Pricing used products for remanufacturing. Eur J Oper Res 193(2):390–395

    Article  MathSciNet  MATH  Google Scholar 

  • Liu B (2004) Uncertainty theory: an introduction to its axiomatic foundations. Springer, Berlin

    Book  MATH  Google Scholar 

  • Liu B, Liu Y-K (2002) Expected value of fuzzy variable and fuzzy expected value models. IEEE Trans Fuzzy Syst 10(4):445–450

    Article  Google Scholar 

  • Mallidis I, Dekker R, Vlachos D (2012) The impact of greening on supply chain design and cost: a case for a developing region. J Transp Geogr 22:118–128

    Article  Google Scholar 

  • Min H, Ko H-J (2008) The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. Int J Prod Econ 113(1):176–192

    Article  Google Scholar 

  • Mulvey JM, Vanderbei RJ, Zenios SA (1995) Robust optimization of large-scale systems. Oper Res 43(2):264–281

    Article  MathSciNet  MATH  Google Scholar 

  • Nie XH, Huang GH, Li YP, Liu L (2007) IFRP: A hybrid interval-parameter fuzzy robust programming approach for waste management planning under uncertainty. J Environ Manag 84(1):1–11

    Article  Google Scholar 

  • Nukala S, Gupta SM (2007) A fuzzy mathematical programming approach for supplier selection in a closed-loop supply chain network. In: Proceedings of the 2007 POMS-Dallas meeting

  • Oral M, Kettani O (1992) A linearization procedure for quadratic and cubic mixed-integer problems. Oper Res 40(1):S109–S116

    Article  MathSciNet  MATH  Google Scholar 

  • Pishvaee MS, Rabbani M, Torabi SA (2011) A robust optimization approach to closed-loop supply chain network design under uncertainty. Appl Math Model 35(2):637–649

    Article  MathSciNet  MATH  Google Scholar 

  • Pokharel S, Mutha A (2009) Perspectives in reverse logistics: a review. Resour Conserv Recycl 53(4):175–182

    Article  Google Scholar 

  • Prakash S, Kumar S, Soni G, Jain V, Rathore APS (2018) Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach. Ann Oper Res 1–28

  • Ramezani M, Kimiagari AM, Karimi B, Hejazi TH (2014) Closed-loop supply chain network design under a fuzzy environment. Knowl Based Syst 59:108–120

    Article  MATH  Google Scholar 

  • Sen DK, Datta S, Mahapatra SS (2017) Decision support framework for selection of 3PL service providers: dominance-based approach in combination with grey set theory. Int J Inf Technol Decis Mak 16(01):25–57

    Article  Google Scholar 

  • Soyster AL (1973) Convex programming with set-inclusive constraints and applications to inexact linear programming. Oper Res 21(5):1154–1157

    Article  MathSciNet  MATH  Google Scholar 

  • Suyabatmaz AÇ, Altekin FT, Şahin G (2014) Hybrid simulation-analytical modeling approaches for the reverse logistics network design of a third-party logistics provider. Comput Ind Eng 70:74–89

    Article  Google Scholar 

  • 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 

  • Taleizadeh AA, Haghighi F, Niaki STA (2019) Modeling and solving a sustainable closed loop supply chain problem with pricing decisions and discounts on returned products. J Clean Prod 207:163–181

    Article  Google Scholar 

  • Xanthopoulos A, Iakovou E (2009) On the optimal design of the disassembly and recovery processes. Waste Manag 29(5):1702–1711

    Article  Google Scholar 

  • Yalabik B, Petruzzi NC, Chhajed D (2005) An integrated product returns model with logistics and marketing coordination. Eur J Oper Res 161(1):162–182

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang Y, Xie L, Hang W, Cui X (2007) A robust model for 3PLS to design a remanufacturing logistics network under the uncertain environment. Automation and Logistics, 2007 IEEE International Conference on IEEE

  • Zhu H, Zhang J (2009) A credibility-based fuzzy programming model for APP problem. Artificial Intelligence and Computational Intelligence, 2009. AICI’09. International Conference on IEEE

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Correspondence to Mir Saman Pishvaee.

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Communicated by Rosana Sueli da Motta Jafelice.

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Ghafarimoghadam, A., Ghayebloo, S. & Pishvaee, M.S. A fuzzy-budgeted robust optimization model for joint network design-pricing problem in a forward−reverse supply chain: the viewpoint of third-party logistics. Comp. Appl. Math. 38, 194 (2019). https://doi.org/10.1007/s40314-019-0966-6

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  • DOI: https://doi.org/10.1007/s40314-019-0966-6

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