Supply chain network design is one of the most important strategic decisions that need to be optimized for long-term efficiency. Critical decisions include facility location, inventory, and transportation issues. This study proposes that with a dual-channel supply chain network design model, the traditional location-inventory problem should be extended to consider the vast amount of online customers at the strategic level, since the problem usually involves multiple and conflicting objectives. Therefore, a multi-objective dual-channel supply chain network model involving three conflicting objectives is initially proposed to allow a comprehensive trade-off evaluation. In addition to the typical costs associated with facility operation and transportation, we explicitly consider the pivotal online customer service rate between the distribution centers (DCs) and their assigned customers. This study proposes a heuristic solution scheme to resolve this multi-objective programming problem, by integrating genetic algorithms, a clustering analysis, a Non-dominated Sorting Genetic Algorithm II (NSGA-II), and a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Several experiments are simulated to demonstrate the possibility and efficacy of the proposed approach. A scenario analysis is conducted to understand the model’s performance.
Accepté le :
DOI : 10.1051/ro/2016010
Mots-clés : Supply chain network design, location inventory problem, dual channel, multi-objective programming, evolutionary computation
@article{RO_2017__51_1_135_0,
author = {Liao, Shu-Hsien and Hsieh, Chia-Lin and Ho, Wei-Chung},
title = {Multi-objective evolutionary approach for supply chain network design problem within online customer consideration},
journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
pages = {135--155},
publisher = {EDP-Sciences},
volume = {51},
number = {1},
year = {2017},
doi = {10.1051/ro/2016010},
mrnumber = {3597663},
zbl = {1358.90059},
language = {en},
url = {http://archive.numdam.org/articles/10.1051/ro/2016010/}
}
TY - JOUR AU - Liao, Shu-Hsien AU - Hsieh, Chia-Lin AU - Ho, Wei-Chung TI - Multi-objective evolutionary approach for supply chain network design problem within online customer consideration JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2017 SP - 135 EP - 155 VL - 51 IS - 1 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/ro/2016010/ DO - 10.1051/ro/2016010 LA - en ID - RO_2017__51_1_135_0 ER -
%0 Journal Article %A Liao, Shu-Hsien %A Hsieh, Chia-Lin %A Ho, Wei-Chung %T Multi-objective evolutionary approach for supply chain network design problem within online customer consideration %J RAIRO - Operations Research - Recherche Opérationnelle %D 2017 %P 135-155 %V 51 %N 1 %I EDP-Sciences %U http://archive.numdam.org/articles/10.1051/ro/2016010/ %R 10.1051/ro/2016010 %G en %F RO_2017__51_1_135_0
Liao, Shu-Hsien; Hsieh, Chia-Lin; Ho, Wei-Chung. Multi-objective evolutionary approach for supply chain network design problem within online customer consideration. RAIRO - Operations Research - Recherche Opérationnelle, Tome 51 (2017) no. 1, pp. 135-155. doi : 10.1051/ro/2016010. http://archive.numdam.org/articles/10.1051/ro/2016010/
, , and , An application to support the temporal and spatial distributed decision-making process in supply chain collaborative planning. Comput. Ind. 62 (2011) 519–40. | DOI
and , Solving multi-objective parallel machine scheduling problem by a modified NSGA-II. Appl. Math. Model. 37 (2013) 6718–6729. | DOI | MR | Zbl
, , and , Using clustering analysis in a capacitated location-routing problem. Eur. J. Oper. Res. 179 (2007) 968–77. | DOI | Zbl
, and , Inventory and distribution strategies for retail/e-tail organizations. Comput. Indust. Eng. 58 (2010) 119–32. | DOI
S. Chakraborty and C.h. Yeh, A Simulation Comparison of Normalization Procedures for TOPSIS, In Proc. of the International Conference on Computers and Industrial Engineering (2009) 1815–1820.
, and , A hybrid genetic algorithm for production and distribution. Omega 33 (2005) 345–55. | DOI
C.C. Coello, G.B. Lamont and D.A.V. Veldhuizen, Evolutionary algorithms for solving multi-objective problems. Springer (2007). | MR | Zbl
, and , An Inventory-Location Model: Formulation, Solution Algorithm and Computational Results. Ann. Oper. Res. 110 (2002) 83–106. | DOI | MR | Zbl
, , and , A fast and elitist multiobjective genetic algorithm: NSGA-II, Evolutionary Computation. IEEE Trans. 6 (2002) 182–97.
and , Improvements and extensions to the Miller-Tucker- Zemlin subtour elimination constraints. Oper. Res. Lett. 10 (1991) 27–36. | DOI | MR | Zbl
, Joint pricing and ordering policy for a deteriorating inventory with partial backlogging. Omega 35 (2007) 184–189. | DOI
, and , Multiple criteria facility location problems: A survey. Appl. Math. Model. 34 (2010) 1689–1709. | DOI | MR | Zbl
, and , Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS. Int. J. Prod. Res. 50 (2011) 4175–91. | DOI
, and , Linear location-inventory models for service parts logistics network design. Comput. Ind. Eng. 69 (2014) 53–63. | DOI
C.L. Hwang and K. Yoon, Multiple attribute decision making: methods and applications: a state-of-the-art survey. Springer-Verlag, New York (1981). | MR | Zbl
A.K. Jain and R.C. Dubes, Algorithms for clustering data.: Prentice-Hall (1988). | MR | Zbl
, A study on the budget constrained facility location model considering inventory management cost. RAIRO: OR 46 (2012) 107–123. | DOI | Numdam | MR | Zbl
, and , A multi-objective evolutionary optimization approach for an integrated location-inventory distribution network problem under vendor-managed inventory systems. Ann. Oper. Res. 186 (2011) 213-29. | DOI | MR | Zbl
and , Multi-objective optimization for stochastic computer networks using NSGA-II and TOPSIS. Eur. J. Oper. Res. 218 (2012) 735–746. | DOI | MR | Zbl
and , Multiperiod supply chain network equilibrium model with electronic commerce and multicriteria decision-making. RAIRO: OR 46 (2012) 253–287. | DOI | Numdam | MR | Zbl
and , The value of postponing online fulfillment decisions in multi-channel retail/e-tail organizations. Comput. Oper. Res. 36 (2009) 3061–3072. | DOI | Zbl
, and , A multi-objective supply chain configuration model for new products. Int. J. Prod. Res. 49 (2011) 7107–34. | DOI
, and , Capacitated warehouse location model with risk pooling. Nav. Res. Log. 55 (2008) 295–312. | DOI | MR | Zbl
, , and , The closed-loop supply chain network with competition, distribution channel investment, and uncertainties. Omega 41 (2013) 186–194. | DOI
and , A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega 28 (2000) 581–98. | DOI
, and , A Joint Location-Inventory Model. Transport. Sci. 37 (2003) 40–55. | DOI
and , Incorporating inventory and routing costs in strategic location models. Eur. J. Oper. Res. 179 (2007) 372–389. | DOI | Zbl
and An extension of TOPSIS for group decision making. Math. Comput. Model. 45 (2007) 801–813. | DOI | Zbl
, and , A single-product network design model with lead time and safety stock considerations. IIE Trans. 39 (2007) 411–424. | DOI
, and , A hybrid method of Pareto, TOPSIS and genetic algorithm to optimize multi-product multi-constraint Heuristic Solution. Transport. Sci. 41 (2007) 392–408.
, , and , Integrated Production – Inventory – Distribution System Design with Risk Pooling: Model Formulation and inventory control systems with random fuzzy replenishments. Math. Comput. Model. 49 (2009) 1044–57.
, , , and , Managing sales return in dual sales channel: its product substitution and return channel analysis. Int. J. Indust. Syst. Eng. 9 (2011) 121–49.
, , and , The evaluation of tourism destination competitiveness by TOPSIS & information entropy – A case in the Yangtze River Delta of China. Tour. Manag. 32 (2011) 443–51. | DOI
M. Zeleny, Multiple criteria decision making. Graw-Hill, New York (1982). | Zbl
Cité par Sources :
