no. 2
Geometric programming solution of second degree difficulty for carbon ejection controlled reliable smart production system
RAIRO. Operations Research, Tome 56 (2022) no. 2, pp. 1013-1029

Smart manufacturing systems should always aim to be fully sustainable while simultaneously being as reliable as possible which is difficult to reach. Furthermore, climate change especially by carbon emission in the industry is a significant topic and carbon emission should be controlled and reduced to save the environment. Contributing towards a greener environment in a positive manner is done by reducing the number of insufficient items that are produced in a smart production system which also can be reached with higher reliability in the system. Therefore, this study models a smart reliable production system with controlled carbon ejection. To solve the proposed smart production system in this study, a geometric programming approach with a degree of difficulty level two is used which results in optimum results that are quasi-closed. Furthermore, numerical experiments are conducted to validate the proposed model and prove that by using a higher degree geometric programming approach, an optimal solution is found. The numerical results do not only show optimal solutions but also that the smart production system with controlled carbon ejection is reliable.

DOI : 10.1051/ro/2022028
Classification : 90B05, 90B06
Keywords: Smart production system, reliability, geometric programming, setup reduction, controlled carbon ejection 
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     title = {Geometric programming solution of second degree difficulty for carbon ejection controlled reliable smart production system},
     journal = {RAIRO. Operations Research},
     pages = {1013--1029},
     year = {2022},
     publisher = {EDP-Sciences},
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     mrnumber = {4407596},
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     url = {https://www.numdam.org/articles/10.1051/ro/2022028/}
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Kugele, Andreas Se Ho; Ahmed, Waqas; Sarkar, Biswajit. Geometric programming solution of second degree difficulty for carbon ejection controlled reliable smart production system. RAIRO. Operations Research, Tome 56 (2022) no. 2, pp. 1013-1029. doi: 10.1051/ro/2022028

[1] P. Alavian, Y. Eun, S. M. Meerkov and L. Zhang, Smart production systems: automating decision-making in manufacturing environment. Int. J. Prod. Res. 58 (2020) 828–845. | DOI

[2] Z. Asim, S. A. Jalil and S. Javaid, An uncertain model for integrated production-transportation closed-loop supply chain network with cost reliability. Sust. Prod. Consump. 17 (2019) 298–310.

[3] M. A. Bermeo-Ayerbe, C. Ocampo-Martínez and J. Diaz-Rozo, Adaptive predictive control for peripheral equipment management to enhance energy efficiency in smart manufacturing systems. J. Clean. Prod. 291 (2021) 125556. | DOI

[4] S. Bhuniya, S. Pareek, B. Sarkar and B. K. Sett, A Smart Production Process for the Optimum Energy Consumption with Maintenance Policy under a Supply Chain Management. Processes 9 (2021) 19. | DOI

[5] B.-Y. Cao and P.-H. Wang, The Origin and Development of Fuzzy Geometric Programming. Fuzzy Inf. Eng. 11 (2019) 203–211. | DOI

[6] A. Chassein and M. Goerigk, On the complexity of robust geometric programming with polyhedral uncertainty. Oper. Res. Let. 47 (2019) 21–24. | MR | Zbl | DOI

[7] D. Chavarra-Barrientos, R. Batres, P. K. Wright and A. Molina, A methodology to create a sensing, smart and sustainable manufacturing enterprise. Int. J. Prod. Res. 56 (2018) 584–603. | DOI

[8] Z. Chen, Z. Chen, D. Zhou, T. Xia and E. Pan, Reliability evaluation for multi-state manufacturing systems with quality-reliability dependency. Comp. Ind. Eng. 154 (2021) 107166. | DOI

[9] B. K. Dey, S. Bhuniya and B. Sarkar, Involvement of controllable lead time and variable demand for a smart manufacturing system under a supply chain management. Exp. Sys. App. 184 (2021) 115464. | DOI

[10] M. Dressler, S. Iliman and T. De Wolff, An approach to constrained polynomial optimization via nonnegative circuit polynomials and geometric programming. J. Symb. Comp. 91 (2019) 149–172. | MR | Zbl | DOI

[11] M. F. El-Wakeel and R. S. A. Salman, Multi-product, multi-venders inventory models with different cases of the rational function under linear and non-linear constraints via geometric programming approach. J. King Saud Univ. Sci. 31 (2019) 902–912. | DOI

[12] B. Ghavami, M. Raji, R. Rasaizadi and M. Mashinchi, Process variation-aware gate sizing with fuzzy geometric programming. Comp. Elec. Eng. 78 (2019) 259–270. | DOI

[13] M. Ghobakhloo, Determinants of information and digital technology implementation for smart manufacturing. Int. J. Prod. Res. 58 (2020) 2384–2405. | DOI

[14] M. S. Habib, O. Asghar, A. Hussain, M. Imran, M. P. Mughal and B. Sarkar, A robust possibilistic programming approach toward animal fat-based biodiesel supply chain network design under uncertain environment. J. Clean. Prod. 278 (2021) 122403. | DOI

[15] M. Hayhoe, F. Barreras and V. M. Preciado, Multitask learning and nonlinear optimal control of the COVID-19 outbreak: a geometric programming approach. Ann. Rev. Cont. 52 (2021) 495–507. | MR | DOI

[16] E. Jafarian, J. Razmi and M. F. Baki, A flexible programming approach based on intuitionistic fuzzy optimization and geometric programming for solving multi-objective nonlinear programming problems. Exp. Sys. with App. 93 (2018) 245–256. | DOI

[17] W. A. Jauhari, I. N. Pujawan and M. Suef, A closed-loop supply chain inventory model with stochastic demand, hybrid production, carbon emissions, and take-back incentives. J. Clean. Prod. 320 (2021) 128835. | DOI

[18] K. Kalaiarasi, M. S. Begum and M. Sumathi, Optimization of unconstrained multi-item (EPQ) model using fuzzy geometric programming with varying fuzzification and defuzzification methods by applying python. To appear in: Mat. Tod.: Proc. (2021) DOI: . | DOI

[19] A. Kusiak, Smart manufacturing. Int. J. Prod. Res. 56 (2018) 508–517. | DOI

[20] K.-N. F. Leung, A generalized geometric-programming solution to “An economic production quantity model with flexibility and reliability consideration”. Europ. J. Oper. Res. 176 (2007) 240–251. | Zbl | DOI

[21] S.-T. Liu, Profit maximization with quantity discount: an application of geometric programming. Appl. Math. Comp. 190 (2007) 1723–1729. | MR | Zbl | DOI

[22] S.-T. Liu, Using geometric programming to profit maximization with interval coefficients and quantity discount. Appl. Math. Comp. 209 (2009) 259–265. | MR | Zbl | DOI

[23] A. S. Mahapatra, H. N. Soni, M. S. Mahapatra, B. Sarkar and S. Majumder, A Continuous Review Production-Inventory System with a Variable Preparation Time in a Fuzzy Random Environment. Mathematics 9 (2021) 747. | DOI

[24] I. Moon, W. Y. Yun and B. Sarkar, Effects of variable setup cost, reliability, and production costs under controlled carbon emissions in a reliable production system. Europ. J. Ind. Eng. (2022).

[25] N. Nahas, Buffer allocation, equipment selection and line balancing optimisation in unreliable production lines. Eur. J. Ind. Eng. 14 (2020) 217–246. | DOI

[26] B. Sarkar, M. Sarkar, B. Ganguly and L. E. Cárdenas-Barrón, Combined effects of carbon emission and production quality improvement for fixed lifetime products in a sustainable supply chain management. Int. J. Prod. Econ. 231 (2021) 107867. | DOI

[27] B. Sarkar, B. Mridha and S. Pareek, A sustainable smart multi-type biofuel manufacturing with the optimum energy utilization under flexible production. J. Clean. Prod. 332 (2022) 129869. | DOI

[28] A. Sepehri, U. Mishra, M.-L. Tseng and B. Sarkar, Joint Pricing and Inventory Model for Deteriorating Items with Maximum Lifetime and Controllable Carbon Emissions under Permissible Delay in Payments. Mathematics 9 (2021) 470. | DOI

[29] S. Tiwari, Y. Daryanto and H. M. Wee, Sustainable inventory management with deteriorating and imperfect quality items considering carbon emission. J. Clean. Prod. 192 (2018) 281–292. | DOI

[30] M. Ullah, I. Asghar, M. Zahid, M. Omair, A. Alarjani and B. Sarkar, Ramification of remanufacturing in a sustainable three-echelon closed-loop supply chain management for returnable products. J. Clean. Prod. 290 (2021) 125609. | DOI

[31] X. Wang, Y. Zhu, H. Sun and F. Jia, Production decisions of new and remanufactured products: implications for low carbon emission economy. J. Clean. Prod. 171 (2018) 1225–1243. | DOI

[32] B. Wu and L. Cui, Reliability analysis of periodically inspected systems with competing risks under Markovian environments. Comp. Ind. Eng. 158 (2021) 107415. | DOI

[33] D. Yadav, R. Kumari, N. Kumar and B. Sarkar, Reduction of waste and carbon emission through the selection of items with cross-price elasticity of demand to form a sustainable supply chain with preservation technology. J. Clean. Prod. 297 (2021) 126298. | DOI

[34] H. Zhu, H. H. Goh, D. Zhang, T. Ahmad, H. Liu, S. Wang, S. Li, T. Liu, H. Dai and T. Wu, Key technologies for smart energy systems: Recent developments, challenges, and research opportunities in the context of carbon neutrality. J. Clean. Prod. 331 (2022) 129809. | DOI

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