The relationship between important achievements in soft computing technology with the transportation and logistics industry in Iran: A review
محورهای موضوعی : کاربرد محاسبات نرم در علوم مهندسی
1 - Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Soft Computing, Transportation, Logistics, Digital Transformation, Communications.,
چکیده مقاله :
The transportation and logistics industry in Iran plays a pivotal role in the nation's economic development, yet it faces significant challenges, including inefficiencies and high operational costs. This review explores the critical relationship between advancements in soft computing technologies-such as fuzzy logic, neural networks, and genetic algorithms and their transformative impact on this sector. The necessity of this study arises from the urgent need for innovative solutions to enhance decision-making processes, optimize resource allocation, and improve overall service delivery within the Iranian transportation and logistics framework. This review synthesizes existing literature and case studies to highlight the novelty of integrating soft computing techniques in addressing complex logistical challenges. By employing a systematic methodology that includes qualitative analysis of recent advancements and their practical applications, this study identifies key trends and successful implementations within the industry. The findings reveal that soft computing technologies significantly enhance predictive modeling, route optimization, and demand forecasting, leading to improved efficiency and reduced costs. This research underscores the importance of embracing these technologies to foster sustainable growth in Iran's transportation and logistics sector, ultimately contributing to the broader economic landscape.
The transportation and logistics industry in Iran plays a pivotal role in the nation's economic development, yet it faces significant challenges, including inefficiencies and high operational costs. This review explores the critical relationship between advancements in soft computing technologies-such as fuzzy logic, neural networks, and genetic algorithms and their transformative impact on this sector. The necessity of this study arises from the urgent need for innovative solutions to enhance decision-making processes, optimize resource allocation, and improve overall service delivery within the Iranian transportation and logistics framework. This review synthesizes existing literature and case studies to highlight the novelty of integrating soft computing techniques in addressing complex logistical challenges. By employing a systematic methodology that includes qualitative analysis of recent advancements and their practical applications, this study identifies key trends and successful implementations within the industry. The findings reveal that soft computing technologies significantly enhance predictive modeling, route optimization, and demand forecasting, leading to improved efficiency and reduced costs. This research underscores the importance of embracing these technologies to foster sustainable growth in Iran's transportation and logistics sector, ultimately contributing to the broader economic landscape.
[1] Heidari, A., Jafari Navimipour, N. and Unal, M., 2022. The history of computing in Iran (Persia)-since the achaemenid empire. Technologies, 10(4), p.94.
[2] Herold, D.M., Ćwiklicki, M., Pilch, K. and Mikl, J., 2021. The emergence and adoption of digitalization in the logistics and supply chain industry: an institutional perspective. Journal of Enterprise Information Management, 34(6), pp.1917-1938.
[3] Wu, Z., Liao, H., Lu, K. and Zavadskas, E.K., 2021. Soft computing techniques and their applications in intelligent industrial control systems: A survey. International Journal of Computers Communications & Control, 16(1), p. 4142.
[4] Kharola, S., Ram, M., Mangla, S.K. and Kazancoglu, Y., 2023. Advances in soft computing applications, 1st Edition, New York. River Publishers.
[5] Jauha, S.K. and Pant, M., 2013. Recent trends in supply chain management: A soft computing approach. In Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), 2, pp. 465-478.
[6] Li, P., Xue, R., Shao, S., Zhu, Y. and Liu, Y., 2023. Current state and predicted technological trends in global railway intelligent digital transformation. Railway Sciences, 2(4), pp.397-412.
[7] Oladimeji, D., Gupta, K., Kose, N.A., Gundogan, K., Ge, L. and Liang, F., 2023. Smart transportation: an overview of technologies and applications. Sensors, 23(8), p.3880.
[8] Kalsoom, T., Ahmed, S., Rafi-ul-Shan, P.M., Azmat, M., Akhtar, P., Pervez, Z., Imran, M.A. and Ur-Rehman, M., 2021. Impact of IOT on Manufacturing Industry 4.0: A new triangular systematic review. Sustainability, 13(22), p.12506.
[9] Mashayekhy, Y., Babaei, A., Yuan, X.M. and Xue, A., 2022. Impact of Internet of Things (IoT) on inventory management: A literature survey. Logistics, 6(2), p.33.
[10] https://www.deloitte.com/global/en/our-thinking/insights/topics/digital-transformation/industry-4-0/digital-transformation-in-supply-chain.html
[11] Iliopoulou, C. and Kepaptsoglou, K., 2019. Combining ITS and optimization in public transportation planning: state of the art and future research paths. European Transport Research Review, 11, p. 27.
[12] Najafi, M., Keymanesh, M., Moshrefi, R. and Zefreh, M.M., 2019. Impact of Economic Inputs on Production of Transportation Services in Iran. International Journal of Transportation Engineering, 7(1), pp.77-89.
[13] Sumbal, M.S., Ahmed, W., Shahzeb, H. and Chan, F., 2023. Sustainable Technology Strategies for Transportation and Logistics Challenges: An Implementation Feasibility Study. Sustainability, 15(21), p.15224.
[14] Li, X., 2024. Optimization of logistics flow management through big data analytics for sustainable development and environmental cycles. Soft Computing, 28(3), pp.2701-2717.
[15] https://www2.deloitte.com/us/en/insights/focus/transportation/future-of-transport-industry.html
[16] Digital logistics: Technology race gathers momentum, McKinsey’s Operations Practice, 2023. https://www.mckinsey.com/capabilities/operations/our-insights/digital-logistics-technology-race-gathers-momentum#/auth-download/
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