doi: 10.3934/jimo.2020181

Coordinating a supply chain with demand information updating

School of Business Administration, Hunan University, Changsha, Hunan Province 410082, China

Received  July 2020 Revised  October 2020 Published  December 2020

Fund Project: The authors would like to thank the editor and the anonymous referees for their helpful comments and suggestions that greatly improved the quality of this paper. The research is supported by the National Natural Science Foundation of China under Grant Nos. 72071072 and the Postgraduate Scientific Research Innovation Project of Hunan Province under Grant Nos.CX20200456

We investigate how to coordinate a two-echelon supply chain in which a supplier builds production capacity in advance and a manufacturer makes the ordering decision based on updated demand information. By combining European call option and buyback mechanisms, we propose a new hybrid option-buyback contract to coordinate such a supply chain with demand information updating. We construct a two-stage optimization model in that the supplier offers option price and the manufacturer decides initial ordering quantity in the first stage, then the supplier offers exercise price and buyback price and the manufacturer decides final ordering quantity in the second stage after demand information is updated. In both the centralized and decentralized settings, we analytically derive the optimal equilibrium solutions of two-stage ordering quantity. Particularly, we obtain closed-form formulae to describe the members' optimal behavior with a bivariate uniformly distribution. We prove that the proposed contracts can realize the perfect coordination of the supply chain and analyze how the proposed contracts affect the members' decisions. The theoretical results show that, by tuning the option price or buyback price, the supply chain profit can be arbitrarily split between the members, which is a desired property for supply chain coordination. Compared with the standard option and buyback contract, the proposed contract results in a greater supply chain profit and achieves Pareto improvement for the supply chain members. Furthermore, extending the baseline model focusing on price-independent demand to the case of price-dependent demand, we show that the proposed contract still can achieve supply chain coordination. Numerical examples are also conducted to complement the theoretical results.

Citation: Honglin Yang, Jiawu Peng. Coordinating a supply chain with demand information updating. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2020181
References:
[1]

H. V. AraniM. Rabbani and H. Rafiei, A revenue-sharing option contract toward coordination of supply chains, International Journal of Production Economics, 178 (2016), 42-56.   Google Scholar

[2]

I. Baltas and A. N. Yannacopoulos, Uncertainty and inside information, Journal of Dynamics and Games, 3 (2016), 1-24.  doi: 10.3934/jdg.2016001.  Google Scholar

[3]

I. BaltasA. Xepapadeas and A. N. Yannacopoulos, Robust portfolio decisions for financial institutions, Journal of Dynamics and Games, 5 (2018), 61-94.  doi: 10.3934/jdg.2018006.  Google Scholar

[4]

D. Barnes-SchusterY. Bassok and R. Anupind, Coordination and flexibility in supply contracts with option, Manufacturing & Service Operations Management, 4 (2002), 171-207.  doi: 10.1287/msom.4.3.171.7754.  Google Scholar

[5]

S. B$\ddot{o}$ckem and U. Schiller, Option contracts in supply chains, Journal of Economics & Management Strategy, 17 (2008), 219-245.   Google Scholar

[6]

J. BuzacottH. M. Yan and H. Q. Zhang, Risk analysis of commitment–option contracts with forecast updates, IIE transactions, 43 (2011), 415-431.  doi: 10.1080/0740817X.2010.532851.  Google Scholar

[7]

M. A. BegenH. Pun and X. H. Yan, Supply and demand uncertain reduction efforts and cost comparison, International Journal of Production Economics, 180 (2010), 125-134.   Google Scholar

[8]

G. P. Cachon, Supply chain coordination with contracts, Handbooks in Operations Research and Management Science: Supply Chain Management, North-Holland, 11 (2003), 227-339.  doi: 10.1016/S0927-0507(03)11006-7.  Google Scholar

[9]

G. Cachon and R. Swinney, Purchasing, pricing and quick response in the presence of strategic consumers, Management Science, 55 (2009), 497-511.  doi: 10.1287/mnsc.1080.0948.  Google Scholar

[10]

G. Cachon and R. Swinney, The value of fast fashion: Quick response, enhanced design, and strategic consumer behavior, Management Science, 57 (2011), 778-795.   Google Scholar

[11]

T. M. ChoiD. Li and H. Yan, Optimal two-stage ordering policy with Bayesian information updating, Journal of the Operational Research Society, 54 (2003), 846-859.  doi: 10.1057/palgrave.jors.2601584.  Google Scholar

[12]

T. M. ChoiD. Li and H. Yan, Quick response policy with Bayesian information updates, European Journal of Operational Research, 170 (2006), 788-808.  doi: 10.1016/j.ejor.2004.07.049.  Google Scholar

[13]

H. ChenJ. Chen and Y. F. Chen, A coordination mechanism for a supply chain with demand information updating, International Journal of Production Economics, 103 (2006), 347-361.  doi: 10.1016/j.ijpe.2005.09.002.  Google Scholar

[14]

H. ChenY. F. ChenC. H. ChiuT. M. Choi and S. Sethi, Coordination mechanism for the supply chain with lead-time consideration and price-dependent demand, European Journal of Operational Research, 203 (2010), 70-80.   Google Scholar

[15]

A. Cheaitou and R. Cheaytou, A two-stage capacity reservation supply contract with risky supplier and forecast updating, International Journal of Production Economics, 209 (2019), 42-60.  doi: 10.1016/j.ijpe.2018.01.019.  Google Scholar

[16]

K. L. Donohue, Efficient supply contracts for fashion goods with forecast updating and two production modes, Management Science, 46 (2000), 1397-1411.  doi: 10.1287/mnsc.46.11.1397.12088.  Google Scholar

[17]

G. D. Eppen and A. V. Iyer, Backup-agreements in fashion buying-the value of upstream flexibility, Management Science, 43 (1997), 1469-1484.  doi: 10.1287/mnsc.43.11.1469.  Google Scholar

[18]

M. L. Fisher and A. Raman, Reducing the cost of demand uncertainty through accurate response to early sales, Operations Research, 44 (1996), 87-99.  doi: 10.1287/opre.44.1.87.  Google Scholar

[19]

H Gurnani and C. S. Tang, Note: optimal ordering decisions with uncertain cost and demand forecast updating, Management Science, 45 (1999), 1456-1462.  doi: 10.1287/mnsc.45.10.1456.  Google Scholar

[20]

H. HuangS. P. Sethi and H. Yan, Purchase contract management with demand forecast updating, IIE Transactions, 37 (2005), 775-785.   Google Scholar

[21]

K. J$\ddot{o}$rnstenS. L. NonasL. Sandal and J. Ubøe, Mixed contracts for the newsvendor problem with real options and discrete demand, Omega, 41 (2013), 809-881.   Google Scholar

[22]

P. Kouvelis and W. Zhao, Supply chain contract design under financial constraints and bankruptcy costs, Management Science, 62 (2015), 2341-2357.  doi: 10.1287/mnsc.2015.2248.  Google Scholar

[23]

H. LiP. Ritchken and Y. Wang., Option and forward contracting with asymmetric information: Valuation issues in supply chains, European Journal of Operational Research, 197 (2009), 134-148.  doi: 10.1016/j.ejor.2008.06.021.  Google Scholar

[24]

T. Y. Li, G. W. Fang and M. Baykal-Gürsoy, Two-stage inventory management with financing under demand updates, International Journal of Production Economics, (2020), 107915. doi: 10.1016/j.ijpe.2020.107915.  Google Scholar

[25]

L. J. MaY. X. ZhaoW. L. XueT. C. E. Cheng and H. M. Yan, Loss-averse newsvendor model with two ordering opportunities and market information updating, International Journal of Production Economics, 140 (2012), 912-921.  doi: 10.1016/j.ijpe.2012.07.012.  Google Scholar

[26]

N. C. Petruzzi and M. Dada, Pricing and the newsvendor problem: A review with extensions, Operations Research, 47 (1999), 183-194.  doi: 10.1287/opre.47.2.183.  Google Scholar

[27]

T. Pfeiffer, A Comparison of simple two-part supply chain contracts, International Journal of Production Economics, 180 (2016), 114-124.  doi: 10.1016/j.ijpe.2016.06.023.  Google Scholar

[28]

M. PervinS. K. Roy and G. W. Weber, Analysis of inventory control model with shortage under time-dependent demand and time-varying holding cost including stochastic deterioration, Annals of Operations Research, 260 (2018), 437-460.  doi: 10.1007/s10479-016-2355-5.  Google Scholar

[29]

M. PervinS. K. Roy and G. W. Weber, Multi-item deteriorating two-echelon inventory model with price- and stock-dependent demand: A trade-credit policy, Journal of Industrial and Management Optimization, 15 (2019), 1345-1373.  doi: 10.3934/jimo.2018098.  Google Scholar

[30]

M. PervinS. K. Roy and G. W. Weber, Deteriorating inventory with preservation technology under price- and stock-sensitive demand, Journal of Industrial and Management Optimization, 16 (2020), 1585-1612.  doi: 10.3934/jimo.2019019.  Google Scholar

[31]

E. Savku and G. W. Weber, A stochastic maximum principle for a Markov regime-switching jump-diffusion model with delay and an application to Finance, Journal of Optimization Theory and Applications, 179 (2018), 696-721.  doi: 10.1007/s10957-017-1159-3.  Google Scholar

[32]

E. Savku and G. W. Weber, Stochastic differential games for optimal investment problems in a Markov regime-switching jump-diffusion market, Annals of Operations Research, (2020). doi: 10.1007/s10479-020-03768-5.  Google Scholar

[33]

J. Spengler, Vertical integration and antitrust policy, Journal of Political Economy, 58 (1950), 347-352.  doi: 10.1086/256964.  Google Scholar

[34]

B. ShenT. M. Choi and S. Minner, A review on supply chain contracting with information considerations: Information updating and information asymmetry, International Journal of Production Research, 57 (2019), 4898-4936.   Google Scholar

[35]

A. A. Tsay, The quantity flexibility contract and supplier-customer incentives, Management Science, 45 (1999), 1339-1358.  doi: 10.1287/mnsc.45.10.1339.  Google Scholar

[36]

J. Wu, Quantity flexibility contracts under Bayesian updating, Computers & Operations Research, 32 (2005), 1267-1288.  doi: 10.1016/j.cor.2003.11.004.  Google Scholar

[37]

Q. Z. Wang and D. B. Tsao, Supply contract with bidirectional options: The buyer's perspective, International Journal of Production Economics, 101 (2006), 30-52.  doi: 10.1016/j.ijpe.2005.05.005.  Google Scholar

[38]

X. T. WangP. Ma and Y. L. Zhang, Pricing and inventory strategies under quick response with strategic and myopic customer, International Transactions in Operational Research, 27 (2020), 1729-1750.  doi: 10.1111/itor.12453.  Google Scholar

[39]

X. M. Yan and Y. Wang, A newsvendor model with capital constraint and demand forecast updating, International Journal of Production Research, 52 (2014), 5012-5040.   Google Scholar

[40]

D. Q. YangT. J. XiaoT. M. Choi and T. C. E. Cheng, Optimal reservation pricing strategy for a fashion supply chain with forecast update and asymmetric cost information, International Journal of Production Research, 56 (2018), 1960-1981.  doi: 10.1080/00207543.2014.998789.  Google Scholar

[41]

Y. W. Zhou and S. D. Wang, Supply chain coordination for newsvendor-type products with two ordering opportunities and demand information update, Journal of the Operational Research Society, 63 (2012), 1655-1678.  doi: 10.1057/jors.2011.152.  Google Scholar

[42]

M. M. ZhengK. Wu and Y. Shu, Newsvendor problems with demand forecast updating and supply constraints, Computers & Operations Research, 67 (2016), 193-206.  doi: 10.1016/j.cor.2015.10.007.  Google Scholar

[43]

Y. X. ZhaoT. M. ChoiT. C. E. Cheng and S. Y. Wang, Supply option contracts with spot market and demand information updating, European Journal of Operational Research, 266 (2018), 1062-1071.  doi: 10.1016/j.ejor.2017.11.001.  Google Scholar

[44]

Y. Z. ZhaiW. L. Xue and L. J. Ma., Commitment decisions with demand information updating and a capital-constrained supplier, International Transactions in Operational Research, 27 (2020), 2294-2316.  doi: 10.1111/itor.12722.  Google Scholar

show all references

References:
[1]

H. V. AraniM. Rabbani and H. Rafiei, A revenue-sharing option contract toward coordination of supply chains, International Journal of Production Economics, 178 (2016), 42-56.   Google Scholar

[2]

I. Baltas and A. N. Yannacopoulos, Uncertainty and inside information, Journal of Dynamics and Games, 3 (2016), 1-24.  doi: 10.3934/jdg.2016001.  Google Scholar

[3]

I. BaltasA. Xepapadeas and A. N. Yannacopoulos, Robust portfolio decisions for financial institutions, Journal of Dynamics and Games, 5 (2018), 61-94.  doi: 10.3934/jdg.2018006.  Google Scholar

[4]

D. Barnes-SchusterY. Bassok and R. Anupind, Coordination and flexibility in supply contracts with option, Manufacturing & Service Operations Management, 4 (2002), 171-207.  doi: 10.1287/msom.4.3.171.7754.  Google Scholar

[5]

S. B$\ddot{o}$ckem and U. Schiller, Option contracts in supply chains, Journal of Economics & Management Strategy, 17 (2008), 219-245.   Google Scholar

[6]

J. BuzacottH. M. Yan and H. Q. Zhang, Risk analysis of commitment–option contracts with forecast updates, IIE transactions, 43 (2011), 415-431.  doi: 10.1080/0740817X.2010.532851.  Google Scholar

[7]

M. A. BegenH. Pun and X. H. Yan, Supply and demand uncertain reduction efforts and cost comparison, International Journal of Production Economics, 180 (2010), 125-134.   Google Scholar

[8]

G. P. Cachon, Supply chain coordination with contracts, Handbooks in Operations Research and Management Science: Supply Chain Management, North-Holland, 11 (2003), 227-339.  doi: 10.1016/S0927-0507(03)11006-7.  Google Scholar

[9]

G. Cachon and R. Swinney, Purchasing, pricing and quick response in the presence of strategic consumers, Management Science, 55 (2009), 497-511.  doi: 10.1287/mnsc.1080.0948.  Google Scholar

[10]

G. Cachon and R. Swinney, The value of fast fashion: Quick response, enhanced design, and strategic consumer behavior, Management Science, 57 (2011), 778-795.   Google Scholar

[11]

T. M. ChoiD. Li and H. Yan, Optimal two-stage ordering policy with Bayesian information updating, Journal of the Operational Research Society, 54 (2003), 846-859.  doi: 10.1057/palgrave.jors.2601584.  Google Scholar

[12]

T. M. ChoiD. Li and H. Yan, Quick response policy with Bayesian information updates, European Journal of Operational Research, 170 (2006), 788-808.  doi: 10.1016/j.ejor.2004.07.049.  Google Scholar

[13]

H. ChenJ. Chen and Y. F. Chen, A coordination mechanism for a supply chain with demand information updating, International Journal of Production Economics, 103 (2006), 347-361.  doi: 10.1016/j.ijpe.2005.09.002.  Google Scholar

[14]

H. ChenY. F. ChenC. H. ChiuT. M. Choi and S. Sethi, Coordination mechanism for the supply chain with lead-time consideration and price-dependent demand, European Journal of Operational Research, 203 (2010), 70-80.   Google Scholar

[15]

A. Cheaitou and R. Cheaytou, A two-stage capacity reservation supply contract with risky supplier and forecast updating, International Journal of Production Economics, 209 (2019), 42-60.  doi: 10.1016/j.ijpe.2018.01.019.  Google Scholar

[16]

K. L. Donohue, Efficient supply contracts for fashion goods with forecast updating and two production modes, Management Science, 46 (2000), 1397-1411.  doi: 10.1287/mnsc.46.11.1397.12088.  Google Scholar

[17]

G. D. Eppen and A. V. Iyer, Backup-agreements in fashion buying-the value of upstream flexibility, Management Science, 43 (1997), 1469-1484.  doi: 10.1287/mnsc.43.11.1469.  Google Scholar

[18]

M. L. Fisher and A. Raman, Reducing the cost of demand uncertainty through accurate response to early sales, Operations Research, 44 (1996), 87-99.  doi: 10.1287/opre.44.1.87.  Google Scholar

[19]

H Gurnani and C. S. Tang, Note: optimal ordering decisions with uncertain cost and demand forecast updating, Management Science, 45 (1999), 1456-1462.  doi: 10.1287/mnsc.45.10.1456.  Google Scholar

[20]

H. HuangS. P. Sethi and H. Yan, Purchase contract management with demand forecast updating, IIE Transactions, 37 (2005), 775-785.   Google Scholar

[21]

K. J$\ddot{o}$rnstenS. L. NonasL. Sandal and J. Ubøe, Mixed contracts for the newsvendor problem with real options and discrete demand, Omega, 41 (2013), 809-881.   Google Scholar

[22]

P. Kouvelis and W. Zhao, Supply chain contract design under financial constraints and bankruptcy costs, Management Science, 62 (2015), 2341-2357.  doi: 10.1287/mnsc.2015.2248.  Google Scholar

[23]

H. LiP. Ritchken and Y. Wang., Option and forward contracting with asymmetric information: Valuation issues in supply chains, European Journal of Operational Research, 197 (2009), 134-148.  doi: 10.1016/j.ejor.2008.06.021.  Google Scholar

[24]

T. Y. Li, G. W. Fang and M. Baykal-Gürsoy, Two-stage inventory management with financing under demand updates, International Journal of Production Economics, (2020), 107915. doi: 10.1016/j.ijpe.2020.107915.  Google Scholar

[25]

L. J. MaY. X. ZhaoW. L. XueT. C. E. Cheng and H. M. Yan, Loss-averse newsvendor model with two ordering opportunities and market information updating, International Journal of Production Economics, 140 (2012), 912-921.  doi: 10.1016/j.ijpe.2012.07.012.  Google Scholar

[26]

N. C. Petruzzi and M. Dada, Pricing and the newsvendor problem: A review with extensions, Operations Research, 47 (1999), 183-194.  doi: 10.1287/opre.47.2.183.  Google Scholar

[27]

T. Pfeiffer, A Comparison of simple two-part supply chain contracts, International Journal of Production Economics, 180 (2016), 114-124.  doi: 10.1016/j.ijpe.2016.06.023.  Google Scholar

[28]

M. PervinS. K. Roy and G. W. Weber, Analysis of inventory control model with shortage under time-dependent demand and time-varying holding cost including stochastic deterioration, Annals of Operations Research, 260 (2018), 437-460.  doi: 10.1007/s10479-016-2355-5.  Google Scholar

[29]

M. PervinS. K. Roy and G. W. Weber, Multi-item deteriorating two-echelon inventory model with price- and stock-dependent demand: A trade-credit policy, Journal of Industrial and Management Optimization, 15 (2019), 1345-1373.  doi: 10.3934/jimo.2018098.  Google Scholar

[30]

M. PervinS. K. Roy and G. W. Weber, Deteriorating inventory with preservation technology under price- and stock-sensitive demand, Journal of Industrial and Management Optimization, 16 (2020), 1585-1612.  doi: 10.3934/jimo.2019019.  Google Scholar

[31]

E. Savku and G. W. Weber, A stochastic maximum principle for a Markov regime-switching jump-diffusion model with delay and an application to Finance, Journal of Optimization Theory and Applications, 179 (2018), 696-721.  doi: 10.1007/s10957-017-1159-3.  Google Scholar

[32]

E. Savku and G. W. Weber, Stochastic differential games for optimal investment problems in a Markov regime-switching jump-diffusion market, Annals of Operations Research, (2020). doi: 10.1007/s10479-020-03768-5.  Google Scholar

[33]

J. Spengler, Vertical integration and antitrust policy, Journal of Political Economy, 58 (1950), 347-352.  doi: 10.1086/256964.  Google Scholar

[34]

B. ShenT. M. Choi and S. Minner, A review on supply chain contracting with information considerations: Information updating and information asymmetry, International Journal of Production Research, 57 (2019), 4898-4936.   Google Scholar

[35]

A. A. Tsay, The quantity flexibility contract and supplier-customer incentives, Management Science, 45 (1999), 1339-1358.  doi: 10.1287/mnsc.45.10.1339.  Google Scholar

[36]

J. Wu, Quantity flexibility contracts under Bayesian updating, Computers & Operations Research, 32 (2005), 1267-1288.  doi: 10.1016/j.cor.2003.11.004.  Google Scholar

[37]

Q. Z. Wang and D. B. Tsao, Supply contract with bidirectional options: The buyer's perspective, International Journal of Production Economics, 101 (2006), 30-52.  doi: 10.1016/j.ijpe.2005.05.005.  Google Scholar

[38]

X. T. WangP. Ma and Y. L. Zhang, Pricing and inventory strategies under quick response with strategic and myopic customer, International Transactions in Operational Research, 27 (2020), 1729-1750.  doi: 10.1111/itor.12453.  Google Scholar

[39]

X. M. Yan and Y. Wang, A newsvendor model with capital constraint and demand forecast updating, International Journal of Production Research, 52 (2014), 5012-5040.   Google Scholar

[40]

D. Q. YangT. J. XiaoT. M. Choi and T. C. E. Cheng, Optimal reservation pricing strategy for a fashion supply chain with forecast update and asymmetric cost information, International Journal of Production Research, 56 (2018), 1960-1981.  doi: 10.1080/00207543.2014.998789.  Google Scholar

[41]

Y. W. Zhou and S. D. Wang, Supply chain coordination for newsvendor-type products with two ordering opportunities and demand information update, Journal of the Operational Research Society, 63 (2012), 1655-1678.  doi: 10.1057/jors.2011.152.  Google Scholar

[42]

M. M. ZhengK. Wu and Y. Shu, Newsvendor problems with demand forecast updating and supply constraints, Computers & Operations Research, 67 (2016), 193-206.  doi: 10.1016/j.cor.2015.10.007.  Google Scholar

[43]

Y. X. ZhaoT. M. ChoiT. C. E. Cheng and S. Y. Wang, Supply option contracts with spot market and demand information updating, European Journal of Operational Research, 266 (2018), 1062-1071.  doi: 10.1016/j.ejor.2017.11.001.  Google Scholar

[44]

Y. Z. ZhaiW. L. Xue and L. J. Ma., Commitment decisions with demand information updating and a capital-constrained supplier, International Transactions in Operational Research, 27 (2020), 2294-2316.  doi: 10.1111/itor.12722.  Google Scholar

Figure 1.  The sequence of events
Figure 2.  Pareto improvement area
Figure 3.  The impacts of $ c_e $ and $ w_e $
Figure 4.  The impacts of $ b $ and $ p $
Figure 5.  The impacts of $ c_1 $ and $ c_2 $
Figure 6.  The impacts of $ v_1 $ and $ v_2 $
Table 1.  Notations and explanations
Notation Explanation
$ x $ Random demand
$ p $ Retail price
$ c_e $ Option price (decision variable)
$ w_e $ Exercise price (decision variable)
$ \lambda $ Revenue sharing ratio.
$ b $ Buyback price (decision variable)
$ Q_k^j $ Capacity reservation quantity of the key component for the manufacturer,
where $ j=c,d $ and $ k=sc,m,s $(decision variable)
$ Q_e^j $ Final order quantity of the manufacturer, where $ j=c,d $ (decision variable)
$ I $ Demand information
$ c_1 $ Capacity investment cost of the supplier
$ c_2 $ Production cost of the supplier
$ v_1 $ Salvage value of excess capacity
$ v_2 $ Salvage value of leftover inventory
$ f(\cdot),F(\cdot) $ Prior probability density and distribution functions of $ x $
$ g(\cdot),G(\cdot) $ Probability density and distribution functions of $ I $
$ h(x|i) $ Probability density function of $ x $ given $ I=i $
$ H(x|i) $ Cumulative distribution functions for $ x $ given $ I=i $
$ I $ Total profit
Notation Explanation
$ x $ Random demand
$ p $ Retail price
$ c_e $ Option price (decision variable)
$ w_e $ Exercise price (decision variable)
$ \lambda $ Revenue sharing ratio.
$ b $ Buyback price (decision variable)
$ Q_k^j $ Capacity reservation quantity of the key component for the manufacturer,
where $ j=c,d $ and $ k=sc,m,s $(decision variable)
$ Q_e^j $ Final order quantity of the manufacturer, where $ j=c,d $ (decision variable)
$ I $ Demand information
$ c_1 $ Capacity investment cost of the supplier
$ c_2 $ Production cost of the supplier
$ v_1 $ Salvage value of excess capacity
$ v_2 $ Salvage value of leftover inventory
$ f(\cdot),F(\cdot) $ Prior probability density and distribution functions of $ x $
$ g(\cdot),G(\cdot) $ Probability density and distribution functions of $ I $
$ h(x|i) $ Probability density function of $ x $ given $ I=i $
$ H(x|i) $ Cumulative distribution functions for $ x $ given $ I=i $
$ I $ Total profit
Table 2.  The optimal decisions of the supply chain with demand information updating
Supply chain Final order quantity Capacity reservation quantity
Centralized $ Q_e^{c*}=Q_e^c|i, \ if \ i\leq i_{Q_{sc}^c} $, $ \int_{i_{Q_{sc}^{c*}}}^{+\infty}[H(Q_e^c|i)-H(Q_{sc}^{c*}|i)]dG(i)=\frac{c_1-v_1}{p-v_2} $.
$ Q_e^{c*}=Q_{sc}^c, \ if \ i \ i_{Q_{sc}^c} $,
where $ H(Q_e^c|i)=\frac{p-c_2-v_1}{p-v_2} $}.
Decentralized $ Q_e^{d*}=Q_e^d|i, \ if \ i\leq i_{Q_{m}^d} $, $ \int_{i_{Q_m^{d*}}}^{+\infty}[H(Q_e^d|i)-H(Q_m^{d*}|i)]dG(i)=\frac{c_e}{p-b}. $
$ Q_e^{d*}=Q_{m}^d, \ if \ i \ i_{Q_{m}^d} $,
where $ H(Q_e^d|i)=\frac{p-w_e}{p-b} $.
Supply chain Final order quantity Capacity reservation quantity
Centralized $ Q_e^{c*}=Q_e^c|i, \ if \ i\leq i_{Q_{sc}^c} $, $ \int_{i_{Q_{sc}^{c*}}}^{+\infty}[H(Q_e^c|i)-H(Q_{sc}^{c*}|i)]dG(i)=\frac{c_1-v_1}{p-v_2} $.
$ Q_e^{c*}=Q_{sc}^c, \ if \ i \ i_{Q_{sc}^c} $,
where $ H(Q_e^c|i)=\frac{p-c_2-v_1}{p-v_2} $}.
Decentralized $ Q_e^{d*}=Q_e^d|i, \ if \ i\leq i_{Q_{m}^d} $, $ \int_{i_{Q_m^{d*}}}^{+\infty}[H(Q_e^d|i)-H(Q_m^{d*}|i)]dG(i)=\frac{c_e}{p-b}. $
$ Q_e^{d*}=Q_{m}^d, \ if \ i \ i_{Q_{m}^d} $,
where $ H(Q_e^d|i)=\frac{p-w_e}{p-b} $.
Table 3.  Optimal quantity decisions with uniformly distribution
Optimal decisions Centralized supply chain Decentralized supply chain
$ \gamma-\frac{\alpha}{2}\leq i \leq i_{Q_{sc}^c} $ $ {i_{Q_{sc}^c}} \le \gamma + \frac{\alpha }{2}$ $ \gamma-\frac{\alpha}{2}\leq i \leq i_{Q_{m}^d} $ $ {i_{Q_m^d}} \le \gamma + \frac{\alpha }{2}$
Exercise quantity $ i-\frac{\beta}{2}+\frac{p-c_2-v_1}{p-v_2}\beta $ $ Q_{sc}^c $ $ i-\frac{\beta}{2}+\frac{p-w_e}{p-b}\beta $ $ Q_{m}^d $
Option quantity $ Q_{sc}^c=\gamma+\frac{\alpha-\beta}{2}+\frac{p-c_2-v_1}{p-v_2}\beta-\sqrt{\frac{2(c_1-v_1)\alpha\beta}{p-v_2}} $ $ Q_{m}^d=\gamma+\frac{\alpha-\beta}{2}+\frac{p-w_e}{p-b}\beta-\sqrt{\frac{2c_e\alpha\beta}{p-b}} $
Optimal decisions Centralized supply chain Decentralized supply chain
$ \gamma-\frac{\alpha}{2}\leq i \leq i_{Q_{sc}^c} $ $ {i_{Q_{sc}^c}} \le \gamma + \frac{\alpha }{2}$ $ \gamma-\frac{\alpha}{2}\leq i \leq i_{Q_{m}^d} $ $ {i_{Q_m^d}} \le \gamma + \frac{\alpha }{2}$
Exercise quantity $ i-\frac{\beta}{2}+\frac{p-c_2-v_1}{p-v_2}\beta $ $ Q_{sc}^c $ $ i-\frac{\beta}{2}+\frac{p-w_e}{p-b}\beta $ $ Q_{m}^d $
Option quantity $ Q_{sc}^c=\gamma+\frac{\alpha-\beta}{2}+\frac{p-c_2-v_1}{p-v_2}\beta-\sqrt{\frac{2(c_1-v_1)\alpha\beta}{p-v_2}} $ $ Q_{m}^d=\gamma+\frac{\alpha-\beta}{2}+\frac{p-w_e}{p-b}\beta-\sqrt{\frac{2c_e\alpha\beta}{p-b}} $
Table 4.  Optimal quantity and expected profits with $ \{c_e,w_e,b\}\in M $
$ \lambda $ $ c_e $ $ w_e $ $ b $ $ Q_m^{d*} $ $ Q_e^{d*}|i $ $ \Pi^d $ $ \Pi_s^d $ $ \Pi_m^d $ $ \Pi_s^d/\Pi^d $ $ \Pi_m^d/\Pi^d $
0.1 1.5 96.5 93 1414 $ i $ 2.1355 1.92919 0.2135 0.9 0.1
0.2 3.0 93.0 86 1414 $ i $ 2.1355 1.7084 0.4271 0.8 0.2
0.3 4.5 89.5 79 1414 $ i $ 2.1355 1.4948 0.6406 0.7 0.3
0.4 6.0 86.0 72 1414 $ i $ 2.1355 1.2813 0.8542 0.6 0.4
0.5 7.5 82.5 65 1414 $ i $ 2.1355 1.0677 1.0677 0.5 0.5
0.6 9.0 79.0 58 1414 $ i $ 2.1355 0.8542 1.2813 0.4 0.6
0.7 10.5 75.5 51 1414 $ i $ 2.1355 0.6406 1.4948 0.3 0.7
0.8 12.0 72.0 44 1414 $ i $ 2.1355 0.4271 1.7084 0.2 0.8
0.9 13.5 68.5 37 1414 $ i $ 2.1355 0.2135 1.9219 0.1 0.9
$ \lambda $ $ c_e $ $ w_e $ $ b $ $ Q_m^{d*} $ $ Q_e^{d*}|i $ $ \Pi^d $ $ \Pi_s^d $ $ \Pi_m^d $ $ \Pi_s^d/\Pi^d $ $ \Pi_m^d/\Pi^d $
0.1 1.5 96.5 93 1414 $ i $ 2.1355 1.92919 0.2135 0.9 0.1
0.2 3.0 93.0 86 1414 $ i $ 2.1355 1.7084 0.4271 0.8 0.2
0.3 4.5 89.5 79 1414 $ i $ 2.1355 1.4948 0.6406 0.7 0.3
0.4 6.0 86.0 72 1414 $ i $ 2.1355 1.2813 0.8542 0.6 0.4
0.5 7.5 82.5 65 1414 $ i $ 2.1355 1.0677 1.0677 0.5 0.5
0.6 9.0 79.0 58 1414 $ i $ 2.1355 0.8542 1.2813 0.4 0.6
0.7 10.5 75.5 51 1414 $ i $ 2.1355 0.6406 1.4948 0.3 0.7
0.8 12.0 72.0 44 1414 $ i $ 2.1355 0.4271 1.7084 0.2 0.8
0.9 13.5 68.5 37 1414 $ i $ 2.1355 0.2135 1.9219 0.1 0.9
Table 5.  Optimal quantity and expected profits with $ \{c_e,w_e\} $
$ \lambda $ $ c_e $ $ w_e $ $ Q_m^{A*} $ $ Q_e^{A*}|i $ $ \Pi^A $ $ \Pi_s^A $ $ Gap_s^A $ $ \Pi_m^A $ $ Gap_m^A $
0.1 1.5 96.5 1454 i-360 1.7374 1.5729 0.181 0.1645 0.025
0.2 3.0 93.0 1418 i-320 1.8526 1.5062 0.105 0.3463 0.042
0.3 4.5 89.5 1399 i-280 1.9339 1.3937 0.052 0.5402 0.052
0.4 6.0 86.0 1389 i-240 1.9954 1.2512 0.015 0.7441 0.057
0.5 7.5 82.5 1386 i-200 2.0425 1.0855 -0.009 0.9570 0.058
0.6 9.0 79.0 1386 i-160 2.0782 0.9001 -0.023 1.1781 0.054
0.7 10.5 75.5 1390 i-120 2.1043 0.6974 -0.029 1.4069 0.046
0.8 12.0 72.0 1396 i-80 2.1220 0.4791 -0.027 1.6429 0.034
0.9 13.5 68.5 1404 i-40 2.1322 0.2463 -0.017 1.8859 0.019
$ \mathbf{1} $ $ \mathbf{5} $ $ \mathbf{55} $ $ \mathbf{1414} $ $ \mathbf{i} $ $ \mathbf{2.1355} $ $ \mathbf{0} $ $ \mathbf{0} $ $ \mathbf{2.1355} $ $ \mathbf{0} $
$ \lambda $ $ c_e $ $ w_e $ $ Q_m^{A*} $ $ Q_e^{A*}|i $ $ \Pi^A $ $ \Pi_s^A $ $ Gap_s^A $ $ \Pi_m^A $ $ Gap_m^A $
0.1 1.5 96.5 1454 i-360 1.7374 1.5729 0.181 0.1645 0.025
0.2 3.0 93.0 1418 i-320 1.8526 1.5062 0.105 0.3463 0.042
0.3 4.5 89.5 1399 i-280 1.9339 1.3937 0.052 0.5402 0.052
0.4 6.0 86.0 1389 i-240 1.9954 1.2512 0.015 0.7441 0.057
0.5 7.5 82.5 1386 i-200 2.0425 1.0855 -0.009 0.9570 0.058
0.6 9.0 79.0 1386 i-160 2.0782 0.9001 -0.023 1.1781 0.054
0.7 10.5 75.5 1390 i-120 2.1043 0.6974 -0.029 1.4069 0.046
0.8 12.0 72.0 1396 i-80 2.1220 0.4791 -0.027 1.6429 0.034
0.9 13.5 68.5 1404 i-40 2.1322 0.2463 -0.017 1.8859 0.019
$ \mathbf{1} $ $ \mathbf{5} $ $ \mathbf{55} $ $ \mathbf{1414} $ $ \mathbf{i} $ $ \mathbf{2.1355} $ $ \mathbf{0} $ $ \mathbf{0} $ $ \mathbf{2.1355} $ $ \mathbf{0} $
Table 6.  Optimal quantity and expected profits with $ \{c_e,w_e\} $
$ \lambda $ $ w $ $ b $ $ Q_s^{B*} $ $ Q_m^{B*}|i $ $ \Pi^B $ $ \Pi_s^B $ $ Gap_s^B $ $ \Pi_m^B $ $ Gap_m^B $
0.1 95 93 1335 i+171 1.8209 1.4827 0.228 0.3382 -0.065
0.2 95 86 1358 i-114 2.0587 1.5237 0.096 0.5350 -0.056
0.3 95 79 1328 i-210 1.9339 1.4712 0.012 0.5270 0.059
0.4 95 72 1302 i-257 1.9386 1.4283 -0.076 0.5102 0.179
0.5 95 65 1279 i-285 1.8924 1.3988 -0.172 0.4936 0.298
0.6 95 58 1258 i-305 1.8560 1.3791 -0.273 0.4769 0.418
0.7 95 51 1236 i-318 1.8259 1.3668 -0.378 0.4591 0.538
0.8 95 44 1213 i-329 1.7990 1.3601 -0.485 0.4388 0.661
0.9 95 37 1187 i-337 1.7724 1.3581 -0.595 0.4142 0.784
$ \lambda $ $ w $ $ b $ $ Q_s^{B*} $ $ Q_m^{B*}|i $ $ \Pi^B $ $ \Pi_s^B $ $ Gap_s^B $ $ \Pi_m^B $ $ Gap_m^B $
0.1 95 93 1335 i+171 1.8209 1.4827 0.228 0.3382 -0.065
0.2 95 86 1358 i-114 2.0587 1.5237 0.096 0.5350 -0.056
0.3 95 79 1328 i-210 1.9339 1.4712 0.012 0.5270 0.059
0.4 95 72 1302 i-257 1.9386 1.4283 -0.076 0.5102 0.179
0.5 95 65 1279 i-285 1.8924 1.3988 -0.172 0.4936 0.298
0.6 95 58 1258 i-305 1.8560 1.3791 -0.273 0.4769 0.418
0.7 95 51 1236 i-318 1.8259 1.3668 -0.378 0.4591 0.538
0.8 95 44 1213 i-329 1.7990 1.3601 -0.485 0.4388 0.661
0.9 95 37 1187 i-337 1.7724 1.3581 -0.595 0.4142 0.784
[1]

Sushil Kumar Dey, Bibhas C. Giri. Coordination of a sustainable reverse supply chain with revenue sharing contract. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020165

[2]

Wei Chen, Yongkai Ma, Weihao Hu. Electricity supply chain coordination with carbon abatement technology investment under the benchmarking mechanism. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020175

[3]

Xi Zhao, Teng Niu. Impacts of horizontal mergers on dual-channel supply chain. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020173

[4]

Wenyan Zhuo, Honglin Yang, Leopoldo Eduardo Cárdenas-Barrón, Hong Wan. Loss-averse supply chain decisions with a capital constrained retailer. Journal of Industrial & Management Optimization, 2021, 17 (2) : 711-732. doi: 10.3934/jimo.2019131

[5]

Zonghong Cao, Jie Min. Selection and impact of decision mode of encroachment and retail service in a dual-channel supply chain. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020167

[6]

Feimin Zhong, Jinxing Xie, Yuwei Shen. Bargaining in a multi-echelon supply chain with power structure: KS solution vs. Nash solution. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020172

[7]

Hongxia Sun, Yao Wan, Yu Li, Linlin Zhang, Zhen Zhou. Competition in a dual-channel supply chain considering duopolistic retailers with different behaviours. Journal of Industrial & Management Optimization, 2021, 17 (2) : 601-631. doi: 10.3934/jimo.2019125

[8]

Xiao-Xu Chen, Peng Xu, Jiao-Jiao Li, Thomas Walker, Guo-Qiang Yang. Decision-making in a retailer-led closed-loop supply chain involving a third-party logistics provider. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2021014

[9]

Yanjun He, Wei Zeng, Minghui Yu, Hongtao Zhou, Delie Ming. Incentives for production capacity improvement in construction supplier development. Journal of Industrial & Management Optimization, 2021, 17 (1) : 409-426. doi: 10.3934/jimo.2019118

[10]

Yunfeng Geng, Xiaoying Wang, Frithjof Lutscher. Coexistence of competing consumers on a single resource in a hybrid model. Discrete & Continuous Dynamical Systems - B, 2021, 26 (1) : 269-297. doi: 10.3934/dcdsb.2020140

[11]

Wai-Ki Ching, Jia-Wen Gu, Harry Zheng. On correlated defaults and incomplete information. Journal of Industrial & Management Optimization, 2021, 17 (2) : 889-908. doi: 10.3934/jimo.2020003

[12]

Nicolas Rougerie. On two properties of the Fisher information. Kinetic & Related Models, 2021, 14 (1) : 77-88. doi: 10.3934/krm.2020049

[13]

Hong Fu, Mingwu Liu, Bo Chen. Supplier's investment in manufacturer's quality improvement with equity holding. Journal of Industrial & Management Optimization, 2021, 17 (2) : 649-668. doi: 10.3934/jimo.2019127

[14]

Shengxin Zhu, Tongxiang Gu, Xingping Liu. AIMS: Average information matrix splitting. Mathematical Foundations of Computing, 2020, 3 (4) : 301-308. doi: 10.3934/mfc.2020012

[15]

Editorial Office. Retraction: Xiao-Qian Jiang and Lun-Chuan Zhang, A pricing option approach based on backward stochastic differential equation theory. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 969-969. doi: 10.3934/dcdss.2019065

[16]

Hui Lv, Xing'an Wang. Dissipative control for uncertain singular markovian jump systems via hybrid impulsive control. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 127-142. doi: 10.3934/naco.2020020

[17]

Guo Zhou, Yongquan Zhou, Ruxin Zhao. Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem. Journal of Industrial & Management Optimization, 2021, 17 (2) : 533-548. doi: 10.3934/jimo.2019122

[18]

Mahir Demir, Suzanne Lenhart. A spatial food chain model for the Black Sea Anchovy, and its optimal fishery. Discrete & Continuous Dynamical Systems - B, 2021, 26 (1) : 155-171. doi: 10.3934/dcdsb.2020373

[19]

Yueyang Zheng, Jingtao Shi. A stackelberg game of backward stochastic differential equations with partial information. Mathematical Control & Related Fields, 2020  doi: 10.3934/mcrf.2020047

[20]

Chuan Ding, Da-Hai Li. Angel capitalists exit decisions under information asymmetry: IPO or acquisitions. Journal of Industrial & Management Optimization, 2021, 17 (1) : 369-392. doi: 10.3934/jimo.2019116

2019 Impact Factor: 1.366

Article outline

Figures and Tables

[Back to Top]