Industry 4.0 technologies assessment: An integrated reverse supply chain model with the whale optimization algorithm

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Nour Branch, Islamic Azad University, Nour, Iran

2 Department of Industrial Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran

3 Department of Mechanical Engineering, Nour Branch, Islamic Azad University, Nour, Iran

Abstract

In recent years, integrated reverse supply chain practices have been adopted by companies that desire to reduce the negative environmental and social impacts within their supply chains. models and solutions assisted by industry 4.0 technologies have been developed to transform products in the end of their life cycle into new products with different use. There are several methods with different technologies to recycle the wastes, which have been selected and weighted based on the indicators of the industry 4.0 revolution and the wastes sent to recycling centers based on the technology weight. The understudy model is multi-objective, including minimizing transportation costs and environmental effects and maximizing customer response demand. The whale optimization algorithm and the NSGA-II algorithm were also used to solve this model. The results obtained from whale optimization and genetic algorithms have been comprised of each other through comparative indicators of quality, dispersion, uniformity, and solving time. The results showed that the whale algorithm has a higher ability to explore and extract possible points and achieve optimal solutions in all cases. The NSGA-II algorithm was also superior to the whale algorithm in terms of uniformity and solving time. The investigation of changes in solving time with increasing problem size was another confirmation of the NP-hard nature of the understudied problem.

Keywords

[1] P. Alikhani, S. Vesal, P. Kashefi, R.E. Pour, F. Khorvash, G. Askari and R. Meamar, Application and preventive maintenance of neurology medical equipment in Isfahan Alzahra hospital, Int. J. Preventive Med., 4(2) (2013) 323.
[2] E. Bottani and G. Casella, Minimization of the environmental emissions of closed-loop supply chains: A case study of returnable transport assets management, Sustainability 10 (2018) 329.
[3] R. Casper and E. Sundin, Reverse logistic transportation and packaging concepts in automotive remanufacturing, Proc. Manufact. 25 (2018) 154–160.
[4] A. Habibi-Yangjeh, Artificial neural network prediction of normalized polarity parameter for various solvents with diverse chemical structures, Bull. Korean Chem. Soc. 28(9) (2007) 1472.
[5] M. Jahre, Household waste collection as a reverse channel, Int. J. Phys. Distribution & Logistics Management, (1995).
[6] M. Jimenez, M. Arenas, A. Bilbao and M.V. Rodri, Linear programming with fuzzy parameters: an interactive method resolution, European journal of operational research, 177(3) (2007) 1599–1609.
[7] O. Kaya and B. Urek, A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain, Comput. Operat. Res. 65 (2016) 93–103.
[8] H.N. Kong, A green mixed integer linear programming model for optimization of byproduct gases in iron and steel industry, J. Iron Steel Res. 22(8) (2015) 681–685.
[9] O. Koppius, O. ¨ Ozdemir-Akyıldırım and E.V.D. Laan, ¨ Business value from closed-loop supply chains, Int. J. Supply Chain Manag. 3(4) (2014) 107–120.
[10] S. Liu, G. Zhang and L. Wang, IoT-enabled dynamic optimisation for sustainable reverse logistics, Proc. CIRP 69 (2018) 662–667.
[11] E. Manavalan and K. Jayakrishna, A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements, Comput. Indust. Engin. 127 (2019) 925–953.
[12] D. Nyl´en and J. Holmstr¨om, Digital innovation strategy: A framework for diagnosing and improving digital product and service innovation, Business Horizons 58(1) (2015) 57–67.
[13] S. Opricovic and G.H. Tzeng, Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS, European J. Operat. Res. 156(2) (2004) 445–455.
[14] M. Ramezani, A.M. Kimiagari, B. Karimi and T.H. Hejazi, Closed-loop supply chain network design under a fuzzy environment, Knowledge-Based Syst. 59 (2014) 108–120.
[15] P. Rosa, C. Sassanelli, A. Urbinati, D. Chiaroni and S. Terzi, Assessing relations between Circular Economy and Industry 4.0: a systematic literature review, Int. J. Prod. Res. 58(6) (2020) 1662–1687.
[16] M.A. Ruimin, Y.A.O. Lifei, J.I.N. Maozhu, R.E.N. Peiyu and L.V. Zhihan, Robust environmental closed-loop supply chain design under uncertainty, Chaos, Solitons Fract. 89 (2016) 195–202.
[17] R. Somplak, M. Pavlas, V. Nevrl´y, M. Tous and P. Popela, Contribution to global warming potential by waste producers: Identification by reverse logistic modelling, J. Cleaner Product. 208 (2019) 1294–1303.
[18] G.L. Tortorella and D. Fettermann, Implementation of industry 4.0 and lean production in Brazilian manufacturing companies, Int. J. Prod. Res. 56(8) (2018) 2975–2987.
[19] B.M. Tosarkani and S.H. Amin, A possibilistic solution to configure a battery closed-loop supply chain: multiobjective approach, Expert Syst. Appl. 92 (2018) 12–26.
[20] M.L. Tseng, R.R. Tan, A.S. Chiu, C.F. Chien and T.C. Kuo, Circular economy meets industry 4.0: can big data drive industrial symbiosis?, Resour. Conserv. Recycl., 131 (2018) 146e147.
[21] X. Wang, M. Zhao and H. He, Reverse logistic network optimization research for sharing bikes, Proc. Comput. Sci. 126 (2018) 1693–1703.
[22] L. Zhou, C. Du, C. Bai and Y. Song, An Internet of Things based COPD managing system: its development, challenges and first experiences, Clinical eHealth 2 (2019) 12–15.
Volume 13, Issue 1
March 2022
Pages 1803-1825
  • Receive Date: 09 September 2021
  • Revise Date: 06 October 2021
  • Accept Date: 19 November 2021