These days, clothing companies are becoming more and more developed around the world. Due to the rapid development of these companies, designing an efficient clothing supply chain network can be highly beneficial, especially with the remarkable increase in demand and uncertainties in both supply and demand. In this study, a bi-objective stochastic mixed-integer linear programming model is proposed for designing the supply chain of the clothing industry. The first objective function maximizes total profit and the second one minimizes downside risk. In the presented network, the initial demand and price are uncertain and are incorporated into the model through a set of scenarios. To solve the bi-objective model, weighted normalized goal programming is applied. Besides, a real case study for the clothing industry in Iran is proposed to validate the presented model and developed method. The obtained results showed the validity and efficiency of the current study. Also, sensitivity analyses are conducted to evaluate the effect of several important parameters, such as discount and advertisement, on the supply chain. The results indicate that considering the optimal amount for discount parameter can conceivably enhance total profit by about 20% compared to the time without this discount scheme. When the optimized parameter is taken into account for advertisement, 12% is obtained as total profit.
Accepté le :
Première publication :
Publié le :
DOI : 10.1051/ro/2020118
Keywords: Clothing supply chain, downside risk, discount-sensitive demand, advertising-sensitive demand, pricing
@article{RO_2021__55_S1_S2509_0,
author = {Paydar, Mohammad Mahdi and Olfati, Marjan and Triki, Chefi},
title = {Designing a clothing supply chain network considering pricing and demand sensitivity to discounts and advertisement},
journal = {RAIRO. Operations Research},
pages = {S2509--S2541},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
doi = {10.1051/ro/2020118},
mrnumber = {4223157},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2020118/}
}
TY - JOUR AU - Paydar, Mohammad Mahdi AU - Olfati, Marjan AU - Triki, Chefi TI - Designing a clothing supply chain network considering pricing and demand sensitivity to discounts and advertisement JO - RAIRO. Operations Research PY - 2021 SP - S2509 EP - S2541 VL - 55 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2020118/ DO - 10.1051/ro/2020118 LA - en ID - RO_2021__55_S1_S2509_0 ER -
%0 Journal Article %A Paydar, Mohammad Mahdi %A Olfati, Marjan %A Triki, Chefi %T Designing a clothing supply chain network considering pricing and demand sensitivity to discounts and advertisement %J RAIRO. Operations Research %D 2021 %P S2509-S2541 %V 55 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2020118/ %R 10.1051/ro/2020118 %G en %F RO_2021__55_S1_S2509_0
Paydar, Mohammad Mahdi; Olfati, Marjan; Triki, Chefi. Designing a clothing supply chain network considering pricing and demand sensitivity to discounts and advertisement. RAIRO. Operations Research, Tome 55 (2021), pp. S2509-S2541. doi: 10.1051/ro/2020118
[1] and , Integrated pricing and lot-sizing decisions in a serial supply chain. Appl. Math. Model. 54 (2018) 429–445. | MR | DOI
[2] , , and , A fuzzy stochastic multi-objective optimization model to configure a supply chain considering new product development. Appl. Math. Model. 40 (2016) 7545–7570. | MR | DOI
[3] , , , and , Robust production planning in fashion apparel industry under demand uncertainty via conditional value at risk. Math. Prob. Eng. 2014 (2014) 1–10. | DOI
[4] , and , Forward and reverse supply chain network design for consumer medical supplies considering biological risk. Comput. Ind. Eng. 140 (2020) 106229. | DOI
[5] and , Textile supplier selection in sustainable supply chain using a modular fuzzy inference system model. J. Textile Inst. 108 (2017) 1250–1258.
[6] , and , An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier’s risk. J. Cleaner Prod. 190 (2018) 577–591. | DOI
[7] , , and , Integrated shipment dispatching and packing problems: a case study. J. Math. Model. Alg. 6 (2007) 77–85. | Zbl | DOI
[8] , , and , An integrated Fuzzy ANP-MOP approach for partner selection problem and order allocation optimization: the case of virtual enterprise configuration. RAIRO:OR 53 (2019) 223-241. | MR | Zbl | Numdam | DOI
[9] , and , Multi-product production system with the reduced failure rate and the optimum energy consumption under variable demand. Mathematics 7 (2019) 465. | DOI
[10] , and , Solving stochastic linear programs with restricted recourse using interior point methods. Comput. Optim. App. 15 (2000) 215–234. | MR | Zbl | DOI
[11] , and , Towards a sustainable fashion retail supply chain in Europe: organisation and performance. Int. J. Prod. Econ. 114 (2008) 534–553. | DOI
[12] , , and , An integrated inventory model involving discrete setup cost reduction, variable safety factor, selling price dependent demand, and investment. RAIRO:OR 53 (2019) 39–57. | MR | DOI
[13] , , and , Autonomation policy to control work-in-process inventory in a smart production system. Int. J. Prod. Res. (2020) 1–23.
[14] , , and , A bi-objective stochastic closed-loop supply chain network design problem considering downside risk. Ind. Eng. Manage. Syst. 16 (2017) 342–362.
[15] , and , Multi-objective stochastic closed-loop supply chain network design with social considerations. Appl. Soft Comput. 71 (2018) 505–525. | DOI
[16] , and , A decision-making approach for a multi-objective multisite supply network planning problem. Int. J. Comput. Integr. Manuf. 29 (2016) 754–767. | DOI
[17] , , and , Stochastic multi-site supply chain planning in textile and apparel industry under demand and price uncertainties with risk aversion. Ann. Oper. Res. 271 (2018) 551–574. | MR | DOI
[18] and , Manufacturer’s pricing strategy in a two-level supply chain with competing retailers and advertising cost dependent demand. Econ. Model. 38 (2014) 102–111. | DOI
[19] and , Deterministic global optimization of mixed integer bilevel programming problems. In: Foundations of Computer Aided Process Design (FOCAPD) Conference Proceedings (2004) 529–532. | MR | Zbl
[20] , Supply chain optimization of blood products. Ph.D. dissertation, University of South Florida, Tampa, FL (2013).
[21] , , , and , Mathematical model and genetic optimization for the job shop scheduling problem in a mixed-and multi-product assembly environment: a case study based on the apparel industry. Comput. Ind. Eng. 50 (2006) 202–219. | DOI
[22] , and , Designing a bi-objective multi-echelon robust blood supply chain in a disaster. Appl. Math. Model. 55 (2018) 583–599. | MR | DOI
[23] and , Introduction to Operations Research, 7th Edition, Chapter 12. McGraw-Hill Companies (2000). | Zbl
[24] , and , Who is next? E-commerce strategy for the development of an apparel manufacturer. Int. Conf. Inf. Manage. Innov. Manage. Ind. Eng. 3 (2011) 545–548.
[25] , and , Joint pricing and inventory problem with price dependent stochastic demand and price discounts. Comput. Ind. Eng. 114 (2017) 45–53. | DOI
[26] , Designing the green supply chain performance optimisation model. Global J. Flexible Syst. Manage. 15 (2014) 235–259. | DOI
[27] , Performance evaluation and a flow allocation decision model for a sustainable supply chain of an apparel industry. J. Cleaner Prod. 87 (2015) 391–413. | DOI
[28] , and , Supplier selection and evaluation in the garment supply chain: an integrated DEA–PCA–VIKOR approach. J. Text. Inst. (2020) 1–18.
[29] , and , Integrated optimization model for distribution network design: a case study of the clothing industry. Int. Trans. Oper. Res. 26 (2019) 1269–1292. | MR | DOI
[30] and , Two-period pricing and decision strategies in a two-echelon supply chain under price-dependent demand. Appl. Math. Model. 42 (2017) 655–674 | MR | DOI
[31] , , and , Optimisation of the replenishment problem in the fashion retail industry using Tabu-Bees algorithm. IFAC-PapersOnLine 49 (2016) 1685–1690. | DOI
[32] , , and , A multistage formulation for generation companies in a multi-auction electricity market. IMA J. Manage. Math. 21 (2010) 165–181. | MR | Zbl
[33] , and , Joint replenishment and pricing decisions with different freight modes considerations for a supply chain under a composite incentive contract. J. Oper. Res. Soc. 69 (2017) 876–894. | DOI
[34] , and , Sustainable supplier selection and order allocation through quantity discounts. Int. J. Manage. Sci. Eng. Manage. 13 (2018) 20–32.
[35] , , , and , A meta-heuristic approach for financial risks management in heat exchanger networks. Comput. Aided Chem. Eng. Elsevier 40 (2017) 955–960. | DOI
[36] and , Designing and solving a reverse logistics network for polyethylene terephthalate bottles. J. Cleaner Prod. 195 (2018) 605–617. | DOI
[37] and , Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters. Int. J. Comput. Integr. Manuf. 28 (2015) 251–265. | DOI
[38] , and , An engine oil closed-loop supply chain design considering collection risk. Comput. Chem. Eng. 104 (2017) 38–55. | DOI
[39] , , , and , A robust optimization model for multi-objective multi-period supply chain planning under uncertainty considering quantity discounts. J. Ind. Prod. Eng. 35 (2018) 214–228.
[40] , and , A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Appl. Math. Model. 37 (2013) 328–344. | MR | DOI
[41] , and , Customer relationship management and new product development in designing a robust supply chain. RAIRO:OR 54 (2020) 369–391. | MR | Zbl | Numdam | DOI
[42] , Vers une approche intégrée de gestion de planification de la production et de la distribution : cas de l’industrie textile. Ph.D. thesis, Châtenay-Malabry, Ecole centrale de Paris (2013).
[43] , and , Production planning and worker training in dynamic manufacturing systems. J. Manuf. Syst. 32 (2013) 308–314. | DOI
[44] , , and , Pricing and market segmentation in an uncertain supply chain. SÂdhan 45 (2020) 118. | MR
[45] , and , A multi-objective optimization of energy, economic, and carbon emission in a production model under sustainable supply chain management. App. Sci. 8 (2018) 1744. | DOI
[46] , , and , Optimal production delivery policies for supplier and manufacturer in a constrained closed-loop supply chain for returnable transport packaging through metaheuristic approach. Comput. Ind. Eng. 135 (2019) 987–1003. | DOI
[47] , and , A cooperative advertising collaboration policy in supply chain management under uncertain conditions. Appl. Soft Comput. 88 (2020) 105948. | DOI
[48] , and , A game theoretic approach to coordinate pricing and vertical co-op advertising in manufacturer–retailer supply chains. Eur. J. Oper. Res. 211 (2011) 263–273. | MR | Zbl | DOI
[49] , and , Jointly pricing and ordering for a multi-product multi-constraint newsvendor problem with supplier quantity discounts. Appl. Math. Model. 35 (2011) 3001–3011. | MR | Zbl | DOI
[50] and , Pricing, manufacturing and inventory policies for raw material in a three-level supply chain. Int. J. Syst. Sci. 47 (2016) 919–931. | MR | Zbl | DOI
[51] , and , Pricing and ordering decisions of two competing supply chains with different composite policies: a Stackelberg game-theoretic approach. Int. J. Prod. Res. 54 (2016) 2807–2836. | DOI
[52] , and , A possibilistic closed-loop supply chain: pricing, advertising and remanufacturing optimization. Neural Comput. App. 32 (2020) 1195–1215. | DOI
[53] , and , Goal programming for decision making: An overview of the current state-of-the-art. Eur. J. Oper. Res. 111 (1998) 569–581. | Zbl | DOI
[54] , , and , A sustainable development framework for a cleaner multi-item multi-stage textile production system with a process improvement initiative. J. Cleaner Prod. 246 (2020) 119–155. | DOI
[55] and , Recovery-channel selection in a hybrid manufacturing-remanufacturing production model with RFID and product quality. Int. J. Prod. Econ. 219 (2020) 360–374. | DOI
[56] , , and , Risk management in the automotive supply chain: an exploratory study in Brazil. Int. J. Prod. Res. 58 (2020) 783–799. | DOI
[57] , and , Fashion retail supply chain management: A review of operational models. Int. J. Prod. Econ. 207 (2019) 34–55. | DOI
[58] and , Coordinating advertising and pricing in a manufacturer–retailer channel. Eur. J. Oper. Res. 197 (2009) 785–791. | MR | Zbl | DOI
[59] , and , Tire forward and reverse supply chain design considering customer relationship management. Res. Conserv. Recycl. 138 (2018) 215–228. | DOI
[60] , and , Risk management for a global supply chain planning under uncertainty: models and algorithms. AIChE J. 55 (2009) 931–946. | DOI
[61] and , Pricing and determining the optimal discount time of perishable goods with time and price dependent demand. RAIRO:OR 51 (2017) 509–518. | MR | Zbl | Numdam | DOI
[62] , and , Joint optimization on pricing, promotion and inventory control with stochastic demand. Int. J. Prod. Econ. 116 (2008) 190–198. | DOI
[63] , , , and , Novel model and kernel search heuristic for multi-period closed-loop food supply chain planning with returnable transport items. Int. J. Prod. Res. 57 (2019) 7439–7456. | DOI
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