# American Institute of Mathematical Sciences

• Previous Article
Asymptotic convergence of stationary points of stochastic multiobjective programs with parametric variational inequality constraint via SAA approach
• JIMO Home
• This Issue
• Next Article
Online ordering strategy for the discrete newsvendor problem with order value-based free-shipping
October  2019, 15(4): 1631-1651. doi: 10.3934/jimo.2018115

## Coordinating a multi-echelon supply chain under production disruption and price-sensitive stochastic demand

 1 Department of Mathematics, Jadavpur University, Kolkata, India 2 Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge LA 70803, USA

Received  February 2017 Revised  March 2018 Published  October 2019 Early access  August 2018

This paper considers a three-echelon supply chain system with one raw-material supplier, one manufacturer and one retailer in which both the manufacturer and the raw-material supplier are exposed to the risk of production disruptions. The market demand is assumed to be uncertain but sensitive to the retail price. The objective is to determine the optimal lot sizes of the supplier and the manufacturer, and the selling price of the retailer when the wholesale prices of the upstream entities are prescribed and the retailer's order quantity is chosen before the actual demand is realized. As the benchmark case, the expected total profit of the centralized channel is maximized. The decentralized supply chain is coordinated under pairwise and spanning revenue sharing mechanisms. Numerical study shows that disruptions have remarkable impact on supply chain decisions.

Citation: Bibhas C. Giri, Bhaba R. Sarker. Coordinating a multi-echelon supply chain under production disruption and price-sensitive stochastic demand. Journal of Industrial & Management Optimization, 2019, 15 (4) : 1631-1651. doi: 10.3934/jimo.2018115
##### References:

show all references

##### References:
Impact of $\alpha$ on the manufacturer's decisions
Impact of $\beta$ on the supplier's profit
Impact of $\beta$ on the supply chain's total profit
Effects of $\alpha$ and $\beta$ on the manufacturer's and the supplier's decentralized decisions
 when $p^d$ (= 24.60) is known when $Q^d$ (= 19.42) is known $\alpha$ $Q^d$ $\Pi_m$ $\beta$ $R^d$ $\Pi_s$ 0.0 16.79 50.36 0.0 19.42 38.84 0.2 19.42 38.33 0.2 22.62 29.94 0.4 21.28 29.82 0.4 25.20 25.45 0.6 22.03 21.98 0.6 26.27 21.91 0.8 22.43 14.33 0.8 26.87 18.65 1.0 22.69 6.76 1.0 27.24 15.51
 when $p^d$ (= 24.60) is known when $Q^d$ (= 19.42) is known $\alpha$ $Q^d$ $\Pi_m$ $\beta$ $R^d$ $\Pi_s$ 0.0 16.79 50.36 0.0 19.42 38.84 0.2 19.42 38.33 0.2 22.62 29.94 0.4 21.28 29.82 0.4 25.20 25.45 0.6 22.03 21.98 0.6 26.27 21.91 0.8 22.43 14.33 0.8 26.87 18.65 1.0 22.69 6.76 1.0 27.24 15.51
Optimal results for different values of $\xi$ and $\eta$ in the decentralized system
 $\xi$ $\eta$ $\tilde{p}^d\tilde{\Pi}_r$ $\tilde{Q}_d\tilde{\Pi}_m$ $\tilde{R}^d\tilde{\Pi}_s$ Total profit 0.95 0.90 23.58 89.98 24.12 42.50 30.11 31.18 163.66 0.92 23.58 89.98 24.60 46.76 30.71 27.24 163.98 0.94 23.58 89.98 25.12 51.07 30.71 23.06 164.11 0.97 0.90 23.03 96.38 25.89 39.40 32.32 32.78 168.56 0.92 23.03 96.38 26.41 43.83 32.97 28.80 169.01 0.94 23.03 96.38 26.97 48.32 33.67 24.74 169.44 0.99 0.90 22.49 103.11 27.80 35.89 34.71 34.48 173.48 0.92 22.49 103.11 28.35 40.51 35.39 30.35 173.97 0.94 22.49 103.11 28.96 45.19 36.16 26.14 174.44
 $\xi$ $\eta$ $\tilde{p}^d\tilde{\Pi}_r$ $\tilde{Q}_d\tilde{\Pi}_m$ $\tilde{R}^d\tilde{\Pi}_s$ Total profit 0.95 0.90 23.58 89.98 24.12 42.50 30.11 31.18 163.66 0.92 23.58 89.98 24.60 46.76 30.71 27.24 163.98 0.94 23.58 89.98 25.12 51.07 30.71 23.06 164.11 0.97 0.90 23.03 96.38 25.89 39.40 32.32 32.78 168.56 0.92 23.03 96.38 26.41 43.83 32.97 28.80 169.01 0.94 23.03 96.38 26.97 48.32 33.67 24.74 169.44 0.99 0.90 22.49 103.11 27.80 35.89 34.71 34.48 173.48 0.92 22.49 103.11 28.35 40.51 35.39 30.35 173.97 0.94 22.49 103.11 28.96 45.19 36.16 26.14 174.44
A comparison of results in different scenarios of pairwise RS contract
 Decentralized model Retailer's profit Manufacturer's profit Supplier's profit Total profit without RS contract 87.03 38.33 27.48 152.84 Pairwise RS contract $\xi =0.95, \eta = 0.90$ 89.98 42.50 31.18 163.66 $\xi =0.95, \eta = 1.0$ 89.98 64.38 11.21 165.57 $\xi =1.0, \eta = 0.90$ 106.60 33.96 35.35 175.91
 Decentralized model Retailer's profit Manufacturer's profit Supplier's profit Total profit without RS contract 87.03 38.33 27.48 152.84 Pairwise RS contract $\xi =0.95, \eta = 0.90$ 89.98 42.50 31.18 163.66 $\xi =0.95, \eta = 1.0$ 89.98 64.38 11.21 165.57 $\xi =1.0, \eta = 0.90$ 106.60 33.96 35.35 175.91
Optimal results in Example 2
 Model scenario Retailer's profit Manufacturer's profit Supplier's profit Total profit Centralized - - - 463.60 Decentralized without RS contract 167.97 73.05 52.35 293.37 Decentralized with pairwise RS $\xi = 0.95, \eta = 0.90$ 172.28 81.26 58.84 312.38 Decentralized with spanning RS $\xi_1 = 0.05, \xi_2 = 0.02$ 178.30 81.92 62.27 322.49
 Model scenario Retailer's profit Manufacturer's profit Supplier's profit Total profit Centralized - - - 463.60 Decentralized without RS contract 167.97 73.05 52.35 293.37 Decentralized with pairwise RS $\xi = 0.95, \eta = 0.90$ 172.28 81.26 58.84 312.38 Decentralized with spanning RS $\xi_1 = 0.05, \xi_2 = 0.02$ 178.30 81.92 62.27 322.49
Optimal results for different values of $e$ in the decentralized system
 $e$ Retailer($p^d, \Pi_r^d$) Manufacturer($Q^d, \Pi_m^d$) Supplier($R^d, \Pi_s^d$) Total profit 3.0 (31.5,167.97) (37.01, 73.05) (46.21, 52.35) 293.37 3.1 (31.0,119.06) (27.54, 54.37) (34.38, 38.96) 212.39 3.2 (30.55, 84.52) (20.47, 40.41) (25.31, 28.68) 153.61 3.3 (30.13, 60.08) (15.22, 30.05) (19.0, 21.53) 111.66 3.4 (29.75, 42.77) (11.31, 22.32) (14.12, 16.0) 81.09 3.5 (29.40, 30.48) (8.39, 16.58) (10.47, 11.87) 58.92
 $e$ Retailer($p^d, \Pi_r^d$) Manufacturer($Q^d, \Pi_m^d$) Supplier($R^d, \Pi_s^d$) Total profit 3.0 (31.5,167.97) (37.01, 73.05) (46.21, 52.35) 293.37 3.1 (31.0,119.06) (27.54, 54.37) (34.38, 38.96) 212.39 3.2 (30.55, 84.52) (20.47, 40.41) (25.31, 28.68) 153.61 3.3 (30.13, 60.08) (15.22, 30.05) (19.0, 21.53) 111.66 3.4 (29.75, 42.77) (11.31, 22.32) (14.12, 16.0) 81.09 3.5 (29.40, 30.48) (8.39, 16.58) (10.47, 11.87) 58.92
Optimal results for different values of $a$ in the decentralized system
 $a$ Retailer($p^d, \Pi_r^d$) Manufacturer($Q^d, \Pi_m^d$) Supplier($R^d, \Pi_s^d$) Total profit 5000 (31.5,167.97) (37.01, 73.05) (46.21, 52.35) 293.37 6000 (31.5,201.56) (44.41, 87.66) (55.45, 62.83) 352.05 7000 (31.5,235.16) (51.81,102.28) (64.68, 73.30) 410.74 8000 (31.5,268.75) (59.21,116.89) (73.92, 83.77) 469.41 9000 (31.5,302.34) (66.61,131.50) (83.16, 94.24) 528.08 10000 (31.5,335.94) (74.01,146.11) (92.40,104.71) 586.76
 $a$ Retailer($p^d, \Pi_r^d$) Manufacturer($Q^d, \Pi_m^d$) Supplier($R^d, \Pi_s^d$) Total profit 5000 (31.5,167.97) (37.01, 73.05) (46.21, 52.35) 293.37 6000 (31.5,201.56) (44.41, 87.66) (55.45, 62.83) 352.05 7000 (31.5,235.16) (51.81,102.28) (64.68, 73.30) 410.74 8000 (31.5,268.75) (59.21,116.89) (73.92, 83.77) 469.41 9000 (31.5,302.34) (66.61,131.50) (83.16, 94.24) 528.08 10000 (31.5,335.94) (74.01,146.11) (92.40,104.71) 586.76
Optimal results for different values of $\sigma$ in the decentralized system
 $\sigma$ Retailer's profit Manufacturer's profit Supplier's profit Total profit 51 167.97 73.05 52.35 293.37 53 160.95 69.96 50.14 281.05 55 153.97 66.81 47.88 268.66 57 147.04 63.75 45.69 256.48 59 140.19 60.74 43.53 244.46
 $\sigma$ Retailer's profit Manufacturer's profit Supplier's profit Total profit 51 167.97 73.05 52.35 293.37 53 160.95 69.96 50.14 281.05 55 153.97 66.81 47.88 268.66 57 147.04 63.75 45.69 256.48 59 140.19 60.74 43.53 244.46
 [1] Min Li, Jiahua Zhang, Yifan Xu, Wei Wang. Effect of disruption risk on a supply chain with price-dependent demand. Journal of Industrial & Management Optimization, 2020, 16 (6) : 3083-3103. doi: 10.3934/jimo.2019095 [2] Chandan Pathak, Saswati Mukherjee, Santanu Kumar Ghosh, Sudhansu Khanra. A three echelon supply chain model with stochastic demand dependent on price, quality and energy reduction. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021098 [3] Qiang Lin, Yang Xiao, Jingju Zheng. Selecting the supply chain financing mode under price-sensitive demand: Confirmed warehouse financing vs. trade credit. Journal of Industrial & Management Optimization, 2021, 17 (4) : 2031-2049. doi: 10.3934/jimo.2020057 [4] Min Li, Jiahua Zhang, Yifan Xu, Wei Wang. Effects of disruption risk on a supply chain with a risk-averse retailer. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021024 [5] Sanjoy Kumar Paul, Ruhul Sarker, Daryl Essam. Managing risk and disruption in production-inventory and supply chain systems: A review. Journal of Industrial & Management Optimization, 2016, 12 (3) : 1009-1029. doi: 10.3934/jimo.2016.12.1009 [6] Honglin Yang, Jiawu Peng. Coordinating a supply chain with demand information updating. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020181 [7] Junling Han, Nengmin Wang, Zhengwen He, Bin Jiang. Optimal return and rebate mechanism based on demand sensitivity to reference price. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021087 [8] Chih-Te Yang, Liang-Yuh Ouyang, Hsiu-Feng Yen, Kuo-Liang Lee. Joint pricing and ordering policies for deteriorating item with retail price-dependent demand in response to announced supply price increase. Journal of Industrial & Management Optimization, 2013, 9 (2) : 437-454. doi: 10.3934/jimo.2013.9.437 [9] Mitali Sarkar, Young Hae Lee. Optimum pricing strategy for complementary products with reservation price in a supply chain model. Journal of Industrial & Management Optimization, 2017, 13 (3) : 1553-1586. doi: 10.3934/jimo.2017007 [10] Jing Feng, Yanfei Lan, Ruiqing Zhao. Impact of price cap regulation on supply chain contracting between two monopolists. Journal of Industrial & Management Optimization, 2017, 13 (1) : 349-373. doi: 10.3934/jimo.2016021 [11] Katherinne Salas Navarro, Jaime Acevedo Chedid, Whady F. Florez, Holman Ospina Mateus, Leopoldo Eduardo Cárdenas-Barrón, Shib Sankar Sana. A collaborative EPQ inventory model for a three-echelon supply chain with multiple products considering the effect of marketing effort on demand. Journal of Industrial & Management Optimization, 2020, 16 (4) : 1613-1633. doi: 10.3934/jimo.2019020 [12] Arman Hamedirostami, Alireza Goli, Yousef Gholipour-Kanani. Green cross-dock based supply chain network design under demand uncertainty using new metaheuristic algorithms. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021105 [13] Weihua Liu, Xinran Shen, Di Wang, Jingkun Wang. Order allocation model in logistics service supply chain with demand updating and inequity aversion: A perspective of two option contracts comparison. Journal of Industrial & Management Optimization, 2021, 17 (6) : 3269-3295. doi: 10.3934/jimo.2020118 [14] Dingzhong Feng, Xiaofeng Zhang, Ye Zhang. Collection decisions and coordination in a closed-loop supply chain under recovery price and service competition. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021117 [15] Zhidan Wu, Xiaohu Qian, Min Huang, Wai-Ki Ching, Hanbin Kuang, Xingwei Wang. Channel leadership and recycling channel in closed-loop supply chain: The case of recycling price by the recycling party. Journal of Industrial & Management Optimization, 2021, 17 (6) : 3247-3268. doi: 10.3934/jimo.2020116 [16] Huaqing Cao, Xiaofen Ji. Optimal recycling price strategy of clothing enterprises based on closed-loop supply chain. Journal of Industrial & Management Optimization, 2022  doi: 10.3934/jimo.2021232 [17] M. M. Ali, L. Masinga. A nonlinear optimization model for optimal order quantities with stochastic demand rate and price change. Journal of Industrial & Management Optimization, 2007, 3 (1) : 139-154. doi: 10.3934/jimo.2007.3.139 [18] Xiaoming Yan, Minghui Zhang, Ke Liu, Yong Wang. Optimal ordering policies and sourcing strategies with supply disruption. Journal of Industrial & Management Optimization, 2014, 10 (4) : 1147-1168. doi: 10.3934/jimo.2014.10.1147 [19] Masoud Mohammadzadeh, Alireza Arshadi Khamseh, Mohammad Mohammadi. A multi-objective integrated model for closed-loop supply chain configuration and supplier selection considering uncertain demand and different performance levels. Journal of Industrial & Management Optimization, 2017, 13 (2) : 1041-1064. doi: 10.3934/jimo.2016061 [20] Mingzheng Wang, M. Montaz Ali, Guihua Lin. Sample average approximation method for stochastic complementarity problems with applications to supply chain supernetworks. Journal of Industrial & Management Optimization, 2011, 7 (2) : 317-345. doi: 10.3934/jimo.2011.7.317

2020 Impact Factor: 1.801