Abstract
Increasing concerns over environmental pollution across the globe have encouraged us to replace some conventional products with green products. The production cost for green products being higher, the governments in various countries have initiated subsidy policies for green product manufacturers. The carbon regulatory authorities in different nations have started carbon taxation policies to lower the emission. Investment in emission reduction technologies can control the emission of carbon from a manufacturing firm. This paper explores the impacts of joint investment in greening innovation and emission reduction technology in a green production inventory model and provides some better insights to the real-life practitioner. Assuming a selling price and greenness level dependent demand, the optimal inventory decisions are examined under the cap and trade carbon regulatory policy. The model also considers the possibility of defective production and their repairing process. The aim is to find the optimal selling price, the optimal degree of greenness, optimal emission reduction technology investment, and optimal production run time that maximizes the optimal profit. Numerical illustration is presented to validate the model. Sensitivity analysis of the optimal solutions concerning the key inventory parameters is conducted for identifying several managerial implications. It is found that higher subsidy intensity increases the degree of greenness of the product. It is also seen that the simultaneous investment in greening innovation and emission reduction technology is beneficial for the green product manufacturer and the environment.
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Data Availability
All data generated or analyzed during this study are included in this published article (and its supplementary information files).
Abbreviations
- ERT:
-
Emission reduction technology
- PIM:
-
Production inventory model
- R&D:
-
Research and development
- GI:
-
Greening innovations
- EUETS:
-
European Union Emissions Trading Scheme
- PSO:
-
Particle swarm optimization
- QPSO:
-
Quantum-behaved particle swarm optimization
References
Bakker S, Trip JJ (2013) Policy options to support the adoption of electric vehicles in the urban environment. Trans Res Part D 25:18–23
Bao P, Gao F, Shi L, Guo S, (2021) An improved QPSO algorithm based on EXIF for camera self-calibration. In, (2021) IEEE International conference on mechatronics and automation. Takamatsu, Japan 762–767. https://doi.org/10.1109/ICMA52036.2021.9512646
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
Ca 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 Z, Sarker BR (2017) Integrated production-inventory and pricing decisions for a single-manufacturer multi-retailer system of deteriorating items under JIT delivery policy. Int J Adv Manuf Technol 89:2099–2117
Chen X, Benjaafar S, Elomri A (2013) The carbon-constrained EOQ. Oper Res Lett 41(2):172–179
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans on Evolu Compu 6(1):58–73
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 sixth international symposium on micro machine and human science, Nagoya, Japan, pp 39-43
Ghosh D, Shah J (2012) A comparative analysis of greening policies across supply chain structures. Int J Prod Econ 135(2):568–583
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
Guo D, He Y, 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
Guolong Y, Yong Z, Zhongwei C, Zuo Y (2021) A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling. Comput Sci Inform Syst 18(1):189–212
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. Sust Prod Cons 25:604–621
He G, Lu X-l (2021) An improved QPSO algorithm and its application in fuzzy portfolio model with constraints. Soft Comput 25:7695–7706
Hintermann B (2010) Allowance price drivers in the first phase of the EUETS. J Env Econ Manag 59(1):43–56
Huang YS, Fan 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
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
Kumar N, Manna AK, Shaikh AA, Bhunia AK (2021) Application of hybrid binary tournament-based quantum-behaved particle swarm optimization on an imperfect production inventory problem. Soft Comput 25:11245–11267
Kumar N, Shaikh AA, Mahato SK, Bhunia AK (2021) Applications of new hybrid algorithm based on advanced cuckoo search and adaptive Gaussian quantum behaved particle swarm optimization in solving ordinary differential equations. Exp Syst With Appl 172:114646
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
Lou GX, Xia HY, Zhang JQ, Fan TJ (2015) Investment strategy of emission-reduction technology in a supply chain. Sustainability 7(8):10684–10708
Lu X-l, He G (2021) QPSO algorithm based on Levy flight and its application in fuzzy portfolio. Appl Soft Comput 99:106894
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 Prob 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
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
RELIABLEPLANT (2021) Ford reduces manufacturing impact on environment. Retrieved from 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) 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 (2021) A production-repairing inventory model considering demand and the proportion of defective items as rough intervals. Oper Res Int J. https://doi.org/10.1007/s12351-021-00634-5
Ruidas S, Seikh MR, Nayak PK (2022) A production inventory model for high-tech products involving two production runs and a product variation. J Ind Manag Opt. https://doi.org/10.3934/jimo.2022038
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
Sun J, Feng B, Xu WB (2004) Particle swarm optimization with particles having quantum behavior. In: IEEE Proceedings of the 2004 Congress on Evolutionary Computation, Portland, OR, USA vol 1, pp 325–331, https://doi.org/10.1109/CEC.2004.1330875
Sun J, Wu X, Palade V, Fang W, Lai C, Xu W (2012) Convergence analysis and improvements of quantum behaved particle swarm optimization. Inform Sci 193(15):81–103
Sustainable Future (2021) Are electric cars ‘green’? The answer is yes, but it’s complicated. Retrieved from 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, Cárdenas-Barrón LE, Mohammadi B (2014) A deterministic multi-product single machine EPQ model with backordering, scraped products, rework and interruption in manufacturing process. Int J Prod Econ 150:9–27
Taleizadeh AA, Soleymanfar VR, Govindan K (2018) Sustainable economic production quantity models for inventory systems with shortage. J Clean Prod 174:1011–1020
Tang CS, Zhou S (2012) Research advances in environmentally and socially sustainable operations. Eur J Oper Res 223(3):585–594
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. Retrieved from 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
Xu X, He P, Xu H, Zhang Q (2017) Supply chain coordination with green technology under cap-and-trade regulation. Int J Prod Econ 183(Part B):433–442
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
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 1. Proof of Theorem 1
The first- and second-order partial derivatives of Prof with respect to \(t_1\) give \(\frac{\partial {Prof}}{\partial t_1}= \frac{a-bp+c\alpha }{Pt_1^2}\left\{ c_s+c_te_s\left( 1-\theta +\theta e^{-mk}\right) +\frac{G\alpha ^2}{2}+k\right\} +(a-bp+c\alpha )\Big \{c_hA+c_te_hA\left( 1-\theta +\theta e^{-mk}\right) \Big \}\) \(-c_h\frac{P}{2}-c_te_h\frac{P}{2}\left( 1-\theta +\theta e^{-mk}\right)\) and \(\frac{\partial ^2 {Prof}}{\partial {t_1}^2}= \frac{-2(a-bp+c\alpha )}{Pt_1^3}\left\{ c_s+c_te_s\left( 1-\theta +\theta e^{-mk}\right) +\frac{G\alpha ^2}{2}+k\right\}\) \(<0\). So, Prof is concave in \(t_1\).
Appendix 2. Proof of Theorem 2
The first two order partial derivatives of Prof with respect to p yield \(\frac{\partial {Prof}}{\partial p}=-b(p+s\alpha -c_p-c_rx)+a-bp+c\alpha +\frac{bc_s}{Pt_1}-bt_1c_hA+c_te_s\left( 1-\theta +\theta e^{-mk}\right) \frac{b}{Pt_1}+c_t(e_p+e_rx)b\left( 1-\theta +\theta e^{-mk}\right) -c_te_hAb\left( 1-\theta +\theta e^{-mk}\right)t_1\) \(+\frac{Gb\alpha ^2}{2Pt_1}+\frac{kb}{Pt_1}\) and \(\frac{\partial ^2 {Prof}}{\partial p^2}= -b-b=-2b<0\). So, Prof is concave in p.
Appendix 3. Proof of Theorem 3
The first two order partial derivatives of Prof with respect to \(\alpha\) yield \(\frac{\partial {Prof}}{\partial \alpha }= (a-bp+c\alpha )s+c(p+s\alpha -c_p-c_rx)-c_s\frac{c}{Pt_1}+ct_1c_hA -c_te_s\left( 1-\theta +\theta e^{-mk}\right) \frac{c}{Pt_1}-c_t(e_p+e_rx)\left( 1-\theta +\theta e^{-mk}\right) c+c_te_hA\left( 1-\theta +\theta e^{-mk}\right) ct_1-\frac{kc}{Pt_1}-\frac{G}{2Pt_1}(2a\alpha -2bp\alpha +3c\alpha ^2)\) and \(\frac{\partial ^2 {Prof}}{\partial \alpha ^2}= 2sc-\frac{G}{Pt_1}\left( a-bp+3c\alpha \right)\).
Now \(\frac{\partial ^2 {Prof}}{\partial \alpha ^2}< 0 \Rightarrow 2sc-\frac{G}{Pt_1}\left( a-bp+3c\alpha \right) <0 \Rightarrow \alpha >\frac{1}{3c}\left\{ \frac{2scPt_1}{G}-a+bp\right\} .\)
Replacing \(\alpha\) by \(\alpha _{\text {min}}+\beta (\alpha _{\text {max}}-\alpha _{\text {min}})\), we get \(\alpha _{\text {min}}+\beta \left( \alpha _{\text {max}}-\alpha _{\text {min}}\right)>\frac{1}{3c}\left\{ \frac{2scPt_1}{G}-a+bp\right\} \text { i.e. } \beta >\frac{1}{\alpha _{\text {max}}-\alpha _{\text {min}}}\left\{ \frac{1}{3c}\left( \frac{2scPt_1}{G}-a+bp\right) -\alpha _{\text {min}}\right\}\).
So, Prof is concave in \(\alpha\) under the given condition.
Appendix 4. Proof of Theorem 4
The first two order partial derivatives of Prof with respect to k yield \(\frac{\partial {Prof}}{\partial k}=\frac{a-bp+c\alpha }{Pt_1}c_te_s\theta me^{-mk}+c_t(e_p+e_rx)(a-bp+c\alpha )\theta me^{-mk}-c_te_hAt_1(a-bp+c\alpha )\theta me^{-mk}+c_te_h\frac{P}{2}t_1\theta me^{-mk}-\frac{a-bp+c\alpha }{Pt_1}\) and \(\frac{\partial ^2 {Prof}}{\partial k^2} = -\frac{a-bp+c\alpha }{Pt_1}c_te_s\theta m^2e^{-mk}-c_t(e_p+e_rx)(a-bp+c\alpha )\theta m^2e^{-mk}+c_te_hAt_1(a-bp+c\alpha )\theta m^2e^{-mk}-c_te_h\frac{P}{2}t_1\theta m^2e^{-mk}\).
So, \(\frac{\partial ^2 {Prof}}{\partial k^2}< 0 \Rightarrow \theta m^2e^{-mk}\left[ (a-bp+c\alpha )\left\{ c_te_hAt_1-\frac{c_te_s}{Pt_1}-c_t(e_p+e_rx)\right\} -c_te_h\frac{P}{2}t_1\right]<0 \Rightarrow (a-bp+c\alpha )\left\{ c_te_hAt_1-\frac{c_te_s}{Pt_1}-c_t(e_p+e_rx)\right\} -c_te_h\frac{P}{2}t_1<0.\)
So, Prof is concave with respect to k under the stated condition.
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Ruidas, S., Seikh, M.R. & Nayak, P.K. A Production Inventory Model for Green Products with Emission Reduction Technology Investment and Green Subsidy. Process Integr Optim Sustain 6, 863–882 (2022). https://doi.org/10.1007/s41660-022-00258-y
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DOI: https://doi.org/10.1007/s41660-022-00258-y