Taguchi Analysis for Improving Optimization of Integrated Forward/Reverse Logistics

doi:10.1007/s40305-021-00380-7

Abstract

Abstract: The distribution–allocation problem is known as one of the most comprehensive strategic decisions. In real-world cases, it is impossible to solve a distribution–allocation problem completely in acceptable time. This forces the researchers to develop efficient heuristic techniques for the large-term operation of the whole supply chain. These techniques provide near optimal solution and are comparably fast particularly for large-scale test problems. This paper presents an integrated supply chain model which is flexible in the delivery path. As solution methodology, we apply a memetic algorithm with a novelty in population presentation. To identify the optimum operating condition of the proposed memetic algorithm, Taguchi method is adopted. In this study, four factors, namely population size, crossover rate, local search iteration and number of iteration, are considered. Determining the best level of the considered parameters is the outlook of this research.

Key words: Integrated logistics network, Flexible path, Memetic algorithm, Taguchi analysis

CLC Number:

Elham Behmanesh, Jürgen Pannek. Taguchi Analysis for Improving Optimization of Integrated Forward/Reverse Logistics[J]. Journal of the Operations Research Society of China, 2023, 11(3): 529-552.

TrendMD

References

[1] Chan, F., Chung, S.: Multi-criteria genetic optimisation for distribution network problems. Int. J. Adv. Manuf. Technol. 24, 517–532(2004)
[2] Selim, H., Ozkarahan, I.: A supply chain distribution network design model: an interactive fuzzy goal progrprogra-based solution approach. Int. J. Adv. Manuf. Technol. 35, 401–418(2008)
[3] Gen, M., Cheng, R.: Genetic Algorithms and Engineering Design. Wiley, New york (1997)
[4] Dasci, A., Verter, V.: A continuous model for proproduct-distribution system des. Eur. J. Oper. Res. 129, 278–298(2001)
[5] Amiri, A.: Designing a distribution network in a supply chain system: formulated and efficient solution procedure. Eur. J. Oper. Res. 171, 567–576(2006)
[6] Listes, O., Dekker, R.: A stochastic approach to a case study for product recovery network design. Eur. J. Oper. Res. 160, 268–287(2005)
[7] Thomas, D., Griffin, P.: Coordinated supply chain management. Eur. J. Oper. Res. 94, 1–15(1996)
[8] Pishvaee, M.S., Farahani, R.Z., Dullaert, W.: A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Comput. Oper. Res. 37, 1100–1112(2010)
[9] Altiparmak, F., Gen, M., Lin, L., Paksoy, T.: A genetic algorithm approach for multi-objective potimization of supply chain networks. Comput. Ind. Eng. 51, 197–216(2006)
[10] Zhou, G., Min, H., Gen, M.: A genetic algorithm approach to the bi-criteria allocation of customers to warehouse. Int. J. Prod. Econ. 86, 35–45(2003)
[11] Du, F., Evans, G.: A bi-objective reverse logistics network analysis for post-sale service. Comput. Oper. Res. 35, 2617–2634(2008)
[12] Neto, J.Q.F., Walther, G., Bloemhof, J., Nunen, A.E.E.V., Spengler, T.: From closed-loop to sustainable supply chain: the weee case. Int. J. Prod. Res. 48(15), 4463–4481(2010)
[13] Alumur, S.A., Nickel, S., da Gamad, F.S., Verter, V.: Multi-period reverse logistics network design. Eur. J. Oper. Res. 220(1), 67–78(2012)
[14] Fleischmann, M., Beullens, P., Bloemhof-ruwaard, J.M., Wassenhove, L.: The impact of product recovery on logistics network design. Prod. Oper. Manag. 10, 156–173(2001)
[15] Yeh, W.: A hybrid heuristic algorithm for the multistage supply chain network problem. Int. J. Adv. Manuf. Technol. 26, 675–685(2005)
[16] Jayaraman, V., Patterson, R., Rolland, E.: The design of reverse distribution networks: mmodel and solution procedure. Eur. J. Oper. Res. 150, 128–149(2003)
[17] Pishvaee, M.S., Kianfar, K., Karimi, B.: Reverse logistics network design simulated annealing. Int. J. Adv. Manuf. Technol. 47(1), 269–281(2010)
[18] Chouhan, V.K., Khan, S.H., Hajiaghaei-Keshteli, M., Subramanian, S.: Multi-facility-based improved closed-loop supply chain network for handling uncertain demands. Soft. Comput. 24, 7125–7147(2020)
[19] Gumus, D.B., Ozcan, E., Atkin, J.: An analysis of the taguchi method for tuning a memetic algorithm with reduced computational time budget, In: International Symposium on Computer and Information Sciences, (2016) https://doi.org/10.1007/978-3-319-47217-1_2
[20] Alavidoost, M.H., Jafarnejad, A., Babazadeh, H.: A novel fuzzy mathematical model for an integrated supply chain planning using multi-objective evolutionary algorithm. Soft. Comput. 25, 1777–1801(2021)
[21] Akbari, M., Molla-Alizadeh-Zavardehi, S., Niroomand, S.: Meta-heuristic approaches for fixed-charge solid transportation problem in two-stage supply chain network. Oper. Res. Int. J. 20, 447–471(2020)
[22] Zahraee, S.M., Rohani, J.M., Firouzi, A., Shahpanah, A.: Efficiency improvement of blood supply chain system using taguchi method and dynamic simulation. Proc. Manuf. 2, 1–5(2015)
[23] Vinary, V.P., Sridharan, R.: Taguchi method for parameter design in aco algorithm for distribution-allocation in a two-stage supply chain, Int. J. Adv. Manuf. Technol. (2013)
[24] Gottlieb, J., Paulmann, L.: Genetic algorithms for the fixed charge transportation problem, In: IEEE World Congress on Evolutionary Computation INN, I.C. (ed.), pp. 330–335(1998)
[25] Jo, J.B., Yinzhen, L., Gen, M.: Nonlinear fixed charge transportation problem by spanning tree-based genetic algorithm. Comput. Ind. Eng. 53, 290–298(2007)
[26] Behmanesh, E., Pannek, J.: Modeling and random path-based direct encoding for a closed loop supply chain model with flexible delivery paths, In: The Seventh IFAC Conference on Management and Control of Production and Logistics, p. 49(2), 78–83(2016)
[27] Wang, H., Hsu, H.: A closed-loop logistic model with a spanning-tree based genetic algorithm. Comput. Oper. Res. 37, 376–389(2010)
[28] Behmanesh, E., Pannek, J.: A memetic algorithm with extended random path encoding for a closed-loop supply chain model with flexible delivery. J. Logist. Res. 9(22), 1–12(2016)
[29] Michalewicz, Z.: Genetic algorithms + data Structures = evolution Programs. Springer-Verlag, Berlin (1996)
[30] Yun, Y., Moon, C., Kim, D.: Hybrid genetic algorithm with adaptive local search scheme for solving multistage-based supply chain problems. Comput. Ind. Eng. 56, 821–838(2009)
[31] Hart, W.: Adaptive Global Optimization with Local Search. Ph.D Thesis, University of California, San Diego, CA, (1994)
[32] Liu, C., Li, B.: Memetic algorithm with adaprive local search depth for large scale global optimization, In: IEEE Congress on Evolutionary Computation, (2014)
[33] Lipowski, A., Lipowska, D.: Roulette-wheel selection via stochastic acceptance. Physica A. 391(6), 2193–2196(2012)
[34] Athreya, S., Venkatesh, Y.D.: Application of taguchi method for optimization of process parameters in imroving the surface roughness of lathe facing operation. Int. Refer. J. Eng. Sci. 1(3), 13–19(2012)
[35] Daniel, C.: One at a time plans. J. America Atatistical Assoc. 68, 353–360(1973)
[36] Zandieh, M., Amiri, M., Vahdani, B., Soltani, R.: A robust parameter design for multi-response problems. J. Comput. Appl. Math. 230, 463–476(2009)
[37] Taguchi, G., Konishi, S.: Taguchi Methods, Orthogonal Arrays and Linear Graphs, Tools for Quality American Supplier Institute, American Supplier Institute, https://www.researchgate.net/publication/310478918(1987)
[38] Subulan, K., Tasan, A.S.: Taguchi method for analyzing the tactical planning model in a closed-loop supply chain considering remanufacturing option. Int. J. Adv. Manuf. Technol. 66, 251–269(2013)
[39] Shula, S., Tiwari, M., Wan, H., Shankar, R.: Optimization of the supply chain network:simulation, taguchi, and psychoclonal algorithm embedded approach. Comput. Ind. Eng. 58, 29–39(2010)
[40] Piotrowski, A.P.: Review of differential evolution population size. Swarm Evol. Comput. 32, 1–24(2017)
[41] Lee, D., Dong, M.: A heuristic approach to logistics network design for end-of lease computer pproduct recovery. Transp. Res. Part E 44, 455–474(2007)
[42] Behmanesh, E., Pannek, J.: The effect of various parameters of solution methomethod on a flexible integrated supply chain model, Mathematical Problems in Engineering. https://doi.org/10.1155/2018/5935268(2018)
[43] Hedayat, A.S., Sloane, N.J.A.: Orthogonal Arrays: Theory and Applications. Springer, New York (1999)