Abstract
It is a challenging task to develop appropriate realistic production inventory models due to the presence of imprecision in the data available in the current market situation. In addition, increasing environmental concerns around the world have led to a shift toward green products. There are subsidy programs implemented by governments in several countries for green products. In a real-life production system, the various inventory cost and carbon emission parameters are imprecise in nature. Interval number theory is an efficient tool for handling such impreciseness. This study investigates the effects of a simultaneous investment in greening innovation (GI) and emission reduction technologies (ERTs) in a green production inventory model where the various inventory cost components and the carbon emission parameters are interval-valued. Using the cap-and-trade carbon regulation policy, the optimal inventory decision is investigated based on a price and greenness level sensitive demand. Two models are developed depending on whether the manufacturer wishes or not to invest in ERTs. Both models consider the chances of imperfect production and their reworking process. The resulting interval optimization problems are solved using the quantum-behaved particle swarm optimization technique to derive the interval-valued optimum profit. Numerical illustrations for both models are presented. Several managerial insights are identified through a sensitivity analysis over the optimal solution regarding the main inventory parameters. The result shows that the greenness level of the product increases with the intensity of the subsidy offered by the government. Again, it is found that the manufacturing company of the green product as well as our environment benefit from joint investment in GI and ERT.
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References
Bakker S, Trip JJ (2013) Policy options to support the adoption of electric vehicles in the urban environment. Transp Res Part D 25:18–23
Bhunia AK, Shaikh AA, Cárdenas-Barrón LE (2017) A partially integrated production-inventory model with interval valued inventory costs, variable demand and flexible reliability. Appl Soft Comput 55:491–502
Bi G, Jin M, Ling L, Yang F (2017) Environmental subsidy and the choice of green technology in the presence of green consumers. Ann Oper Res 255(1):1–22
Cao K, Xu X, Wu Q, Zhang Q (2017) Optimal production and carbon emission reduction level under cap-and-trade and low carbon subsidy policies. J Clean Prod 167:505–513
Chen X, Benjaafar S, Elomri A (2013) The carbon-constrained EOQ. Oper Res Lett 41(2):172–179
Datta TK (2017) Effect of green technology investment on a production-inventory system with carbon Tax. Adv Oper Res 2017:4834839. https://doi.org/10.1155/2017/4834839
Datta TK, Nath P, Choudhury KD (2020) A hybrid carbon policy inventory model with emission source-based green investments. Opsearch 57:202–220
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micro machine and human science, Nagoya, Japan, pp 39–43
Ghosh SK, Seikh MR, Chakrabortty M (2020) Analyzing a stochastic dual-channel supply chain under consumers’ low carbon preferences and cap-and-trade regulation. Comput Ind Eng 149:106765. https://doi.org/10.1016/j.cie.2020.106765
Ghosh D, Shah J (2012) A comparative analysis of greening policies across supply chain structures. Int J Prod Econ 135(2):568–583
Guo D, He Yi, Wu Y, Xu Q (2016) Analysis of supply chain under different subsidy policies of the government. Sustainability 8(12):1290. https://doi.org/10.3390/su8121290
Hasan MR, Roy TC, Daryanto Y, Wee HM (2021) Optimizing inventory level and technology investment under a carbon tax, cap-and-trade and strict carbon limit regulations. Sustain Prod Consum 25:604–621
Hintermann B (2010) Allowance price drivers in the first phase of the EU ETS. J Environ Econ Manag 59(1):43–56
Huang YS, Fang CC, Lin YA (2020) Inventory management in supply chains with consideration of logistics, green investment and different carbon emissions policies. Comput Ind Eng 139:106207. https://doi.org/10.1016/j.cie.2019.106207
India Today (2021) Govt. increases FAME II subsidy on electric two-wheelers, manufacturers welcome the move. https://www.indiatoday.in/auto/bikes/story/fame-ii-subsidy-increased-electric-two-wheelers-tvs-revolt-ather-1814074-2021-06-12
Jamali BM, Barzoki MR (2018) A game theoretic approach for green and non-green product pricing in chain-to-chain competitive sustainable and regular dual-channel supply chains. J Clean Prod 170:1029–1043
Jung SH, Feng T (2020) Government subsidies for green technology development under uncertainty. Eur J Oper Res 286(2):726–739
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural network, Perth, WA, Australia, vol 4, pp. 1942-1948. https://doi.org/10.1109/ICNN.1995.488968
Lin HJ (2018) Investing in transportation emission cost reduction on environmentally sustainable EOQ models with partial backordering. J Appl Sci Eng 21(3):291–303
Liu J, Yang Q, Zhang Y, Sun W, Xu Y (2019) Analysis of \(CO_2\) emissions in China’s manufacturing industry based on extended logarithmic mean division index decomposition. Sustainibility 11:226. https://doi.org/10.3390/su11010226
Lou GX, Xia HY, Zhang JQ, Fan TJ (2015) Investment strategy of emission-reduction technology in a supply chain. Sustainability 7(8):10684–10708
Luo C, Leng M, Huang J, Liang L (2014) Supply chain analysis under a price-discount incentive scheme for electric vehicles. Eur J Oper Res 235(1):329–333
Madani SR, Rasti-Barzoki M (2017) Sustainable supply chain management with pricing, greening and governmental tariffs determining strategies: a game-theoretic approach. Comput Ind Eng 105:287–298
Meng Q, Li M, Li Z, Zhu J (2020) How different government subsidy objects impact on green supply chain decision considering consumer group complexity. Math Probl Eng 2020:5387867. https://doi.org/10.1155/2020/5387867
Min SH, Lim SY, Yoo SH (2017) Consumers’ willingness to pay a premium for eco-labeled led TVs in Korea: a contingent valuation study. Sustainability 9(5):814. https://doi.org/10.3390/su9050814
Mishra U, Wu J-Z, Sarkar B (2020) A sustainable production-inventory model for a controllable carbon emissions rate under shortages. J Clean Prod 256:1220268. https://doi.org/10.1016/j.jclepro.2020.120268
Mishra U, Wu J-Z, Sarkar B (2021) Optimum sustainable inventory management with backorder and deterioration under controllable carbon emissions. J Clean Prod 279:123699. https://doi.org/10.1016/j.jclepro.2020.123699
Moore RE (1979) Method and application of interval analysis. SIAM, Philadelphia
Nie H, Zhou T, Lu H, Huang S (2021) Evaluation of the efficiency of Chinese energy-saving household appliance subsidy policy: an economic benefit perspective. Energy Policy 149:112059. https://doi.org/10.1016/j.enpol.2020.112059
Panja S, Mondal SK (2019) Analyzing a four-layer green supply chain imperfect production inventory model for green products under type-2 fuzzy credit period. Comput Ind Eng 1129:435–453
Rahman MS, Manna AK, Shaikh AA, Bhunia AK (2020) An application of interval differential equation on a production inventory model with interval-valued demand via center-radius optimization technique and particle swarm optimization. Int J Intell Syst 35(8):1280–1326
RELIABLEPLANT. (2021) Ford reduces manufacturing impact on environment. https://www.reliableplant.com/Read/11570/ford-reduces-manufacturing-impact-on-environment
Ruidas S, Seikh MR, Nayak PK (2020) An EPQ model with stock and selling price dependent demand and variable production rate in interval environment. Int J Syst Assur Eng Manag 11(2):385–399
Ruidas S, Seikh MR, Nayak PK (2021) Application of particle swarm optimization technique in an interval-valued EPQ model. In: Meta-heuristic optimization techniques, published by De Gruyter, Berlin, Germany. https://doi.org/10.1515/9783110716214-004
Ruidas S, Seikh MR, Nayak PK (2022) Pricing strategy in an interval-valued production inventory model for high-tech products under demand disruption and price revision. J Ind Manag Optim. https://doi.org/10.3934/jimo.2022222
Ruidas S, Seikh MR, Nayak PK (2021) A production inventory model with interval-valued carbon emission parameters under price-sensitive demand. Comput Ind Eng 150:107154. https://doi.org/10.1016/j.cie.2021.107154
Ruidas S, Seikh MR, Nayak PK, Sarkar B (2019) A single period production inventory model in interval environment with price revision. Int J Appl Comput Math. https://doi.org/10.1007/s40819-018-0591-x
Sahoo L, Bhunia AK, Kapur PK (2012) Genetic algorithm based multi-objective reliability optimization in interval environment. Comput Ind Eng 62(1):152–160
Sepehri A, Mishra U, Sarkar B (2021) A sustainable production-inventory model with imperfect quality under preservation technology and quality improvement investment. J Clean Prod 310:127332. https://doi.org/10.1016/j.jclepro.2021.127332
Shaikh AA, Cárdenas-Barrón LE, Tiwari S (2019) A two-warehouse inventory model for non-instantaneous deteriorating items with interval-valued inventory costs and stock-dependent demand under inflationary conditions. Neural Comput Appl 31:1931–1948
Sustainable Future (2021) Are electric cars ‘green’? The answer is yes, but it’s complicated. https://www.cnbc.com/2021/07/26/lifetime-emissions-of-evs-are-lower-than-gasoline-cars-experts-say.html
Swami S, Shah J (2012) Channel coordination in green supply chain management. J Oper Res Soc 64:336–351
Taleizadeh AA, Soleymanfar VR, Govindan K (2018) Sustainable economic production quantity models for inventory systems with shortage. J Clean Prod 174:1011–1020
Toptal A, Özlü H, Konur D (2013) Joint decisions on inventory replenishment and emission reduction investment under different emission regulations. Int J Prod Res 52(1):243–269
United States Environmental Protection Agency, (2019). Sources of greenhouse gas emissions. https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions
Ward DO, Clark CD, Jensen KL, Yen ST, Russell CS (2011) Factors influencing willingness-to-pay for the energy star label. Energy Policy 39(3):1450–1458
Xue J, Gong R, Zhao L, Ji X, Xu Y (2019) A green supply chain decision model for energy-saving products that accounts for government subsidies. Sustainability 11(8):2209. https://doi.org/10.3390/su11082209
Xu X, He P, Hao Xu, Zhang Q (2017) Supply chain coordination with green technology under cap-and-trade regulation. Int J Prod Econ 183(Part B):433–442
Zhao L, Chen Y (2019) Optimal subsidies for green products: a maximal policy benefit perspective. Symmetry 11(1):63. https://doi.org/10.3390/sym11010063
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Appendices
Appendix A: Proof of Theorem 1
The first- and second-order partial derivatives of \({Prof}^{1}_L\) with respect to \(t_1\) gives
and \( \dfrac{\partial ^2 {Prof}^{1}_L}{\partial {t_1}^2}= \frac{-2(a-bp+c\alpha )}{Pt_1^3}\left\{ {c_{s}}_R+c_t{e_{s}}_R+\frac{G\alpha ^2}{2}\right\} <0.\) So, \({Prof}^{1}_L\) is concave in \(t_1\).
Similarly, \({Prof}^{1}_R\) is concave in \(t_1\).
Appendix B: Proof of Theorem 2
The first- and second-order partial derivatives of \({Prof}^{1}_L\) with respect to p gives
and \(\dfrac{\partial ^2 {Prof}^{1}_L}{\partial p^2}= -b-b=-2b<0.\) So, \({Prof}^{1}_L\) is concave in p.
Similarly, \({Prof}^{1}_R\) is concave in p.
Appendix C: Proof of Theorem 3
The first- and second-order partial derivatives of \({Prof}^{1}_L\) with respect to \(\alpha \) gives
and \(\dfrac{\partial ^2 {Prof}^{1}_L}{\partial \alpha ^2}= 2sc-\frac{G}{Pt_1}\left( a-bp+3c\alpha \right) \)
Now, \(\dfrac{\partial ^2 {Prof}^{1}_L}{\partial \alpha ^2}< 0 \Rightarrow 2sc-\frac{G}{Pt_1}\left( a-bp+3c\alpha \right) <0 \Rightarrow \alpha >\frac{1}{3c}\left\{ \frac{2scPt_1}{G}-a+bp\right\} .\)
Replacing \(\alpha \) by \(\alpha _{min}+\beta (\alpha _{max}-\alpha _{min})\), we get \( \alpha _{min}+\beta \left( \alpha _{max}-\alpha _{min}\right) >\frac{1}{3c}\left\{ \frac{2scPt_1}{G}-a+bp\right\} \) \( \text{, }{ i.e., } \beta >\frac{1}{\alpha _{max}-\alpha _{min}}\left\{ \frac{1}{3c} \left( \frac{2scPt_1}{G}-a+bp\right) -\alpha _{min}\right\} \).
So, \({Prof}^{1}_L\) is concave in \(\alpha \) provided the given condition is satisfied.
Similarly, \({Prof}^{1}_R\) is concave in \(\alpha \) under the given condition.
Appendix D: Proof of Theorem 4
The first two-order partial derivatives of \({Prof}^{2}_L\) with respect to \(t_1\) yield
\(\dfrac{\partial {Prof}^{2}_L}{\partial t_1}= \frac{a-bp+c\alpha }{Pt_1^2}\Bigg \{{c_{s}}_R+c_t{e_{s}}_R\left( 1-\theta +\theta e^{-mk}\right) +\frac{G\alpha ^2}{2}+k\Bigg \}+(a-bp+c\alpha )\Big \{{c_{h}}_RA+c_t{e_{h}}_RA\big (1-\theta +\theta e^{-mk}\big )\Big \}\) \(-{c_{h}}_R\frac{P}{2}-c_t{e_{h}}_R\frac{P}{2}\big (1-\theta +\theta e^{-mk}\big )\) and \(\dfrac{\partial ^2 {Prof}^{2}_L}{\partial {t_1}^2}= \dfrac{-2(a-bp+c\alpha )}{Pt_1^3}\Big \{{c_{s}}_R+c_t{e_{s}}_R\big (1-\theta +\theta e^{-mk}\big )+\frac{G\alpha ^2}{2}+k\Big \}\) \(<0\). So, \({Prof}^{2}_L\) is concave in \(t_1\).
Similarly, \({Prof}^{2}_R\) is concave in \(t_1\).
Appendix E: Proof of Theorem 5
The first two-order partial derivatives of \({Prof}^{2}_L\) with respect to p yield \( \dfrac{\partial {Prof}^{2}_L}{\partial p}=-b(p+s\alpha -{c_{p}}_R-{c_{r}}_Rx)+a-bp+c\alpha +\frac{bc_s}{Pt_1}-bt_1{c_{h}}_RA+c_t{e_{s}}_R\left( 1-\theta +\theta e^{-mk}\right) \frac{b}{Pt_1}+c_t({e_{p}}_R+{e_{r}}_Rx)b\left( 1-\theta +\theta e^{-mk}\right) -c_t{e_{h}}_RAb\left( 1-\theta +\theta e^{-mk}\right) \) \(t_1+\frac{Gb\alpha ^2}{2Pt_1}+\frac{kb}{Pt_1}\) and \( \dfrac{\partial ^2 {Prof}^{2}_L}{\partial p^2}= -b-b=-2b<0\). So, \({Prof}^{2}_L\) is concave in p.
Similarly, \({Prof}^{2}_R\) is concave in p.
Appendix F: Proof of Theorem 6
The first two-order partial derivatives of \({Prof}^{2}_L\) with respect to \(\alpha \) yield \( \dfrac{\partial {Prof}^{2}_L}{\partial \alpha }= (a-bp+c\alpha )s+c(p+s\alpha -{c_{p}}_R-{c_{r}}_Rx)-{c_{s}}_R\frac{c}{Pt_1}+ct_1{c_{h}}_RA -c_t{e_{s}}_R\left( 1-\theta +\theta e^{-mk}\right) \frac{c}{Pt_1}-c_t({e_{p}}_R+{e_{r}}_Rx)\left( 1-\theta +\theta e^{-mk}\right) c+c_t{e_{h}}_RA\left( 1-\theta +\theta e^{-mk}\right) ct_1-\frac{kc}{Pt_1}-\frac{G}{2Pt_1}(2a\alpha -2bp\alpha +3c\alpha ^2)\) and \( \dfrac{\partial ^2 {Prof}^{2}_L}{\partial \alpha ^2}= 2sc-\frac{G}{Pt_1}\left( a-bp+3c\alpha \right) \).
Therefore, by following similar procedure as in Appendix C, we can show that \({Prof}^{2}_L\) is concave in \(\alpha \) under the given condition.
Similarly, \({Prof}^{2}_R\) is concave in \(\alpha \) under the given condition.
Appendix G: Proof of Theorem 7
The first two-order partial derivatives of \({Prof}^{2}_L\) with respect to k yield \( \dfrac{\partial {Prof}^{2}_L}{\partial k}=\frac{a-bp+c\alpha }{Pt_1}c_t{e_{s}}_R\theta m \) \(e^{-mk}+c_t({e_{p}}_R+{e_{r}}_Rx)(a-bp+c\alpha )\theta me^{-mk}-c_t{e_{h}}_RAt_1(a-bp+c\alpha )\theta me^{-mk}+c_t{e_{h}}_R\frac{P}{2}t_1\theta me^{-mk}-\frac{a-bp+c\alpha }{Pt_1}\) and \( \dfrac{\partial ^2 {Prof}^{2}_L}{\partial k^2} = -\frac{a-bp+c\alpha }{Pt_1}c_t{e_{s}}_R\theta m^2e^{-mk}-c_t({e_{p}}_R+{e_{r}}_Rx)(a-bp+c\alpha )\theta m^2e^{-mk}+c_t{e_{h}}_RAt_1(a-bp+c\alpha )\theta m^2e^{-mk}-c_t{e_{h}}_R\frac{P}{2}t_1\theta m^2e^{-mk}\).
So, \(\dfrac{\partial ^2 {Prof}^{2}_L}{\partial k^2} < 0 \Rightarrow \theta m^2e^{-mk}\left[ (a-bp+c\alpha )\left\{ c_t{e_{h}}_RAt_1 -\frac{c_t{e_{s}}_R}{Pt_1}-c_t({e_{p}}_R+{e_{r}}_Rx)\right\} -c_t{e_{h}}_R\frac{P}{2}t_1\right] \) \(<0 \Rightarrow (a-bp+c\alpha )\left\{ c_t{e_{h}}_RAt_1-\frac{c_t{e_{s}}_R}{Pt_1} -c_t({e_{p}}_R+{e_{r}}_Rx)\right\} -c_t{e_{h}}_R\frac{P}{2}t_1<0.\)
So, \({Prof}^{2}_L\) is concave with respect to k under the stated condition.
Similarly, \({Prof}^{2}_R\) is concave with respect to k under the stated condition.
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Ruidas, S., Seikh, M.R., Nayak, P.K. et al. An interval-valued green production inventory model under controllable carbon emissions and green subsidy via particle swarm optimization. Soft Comput 27, 9709–9733 (2023). https://doi.org/10.1007/s00500-022-07806-1
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DOI: https://doi.org/10.1007/s00500-022-07806-1