Lojistik Bilgi Sistemleri ve İleri Teknoloji: Yapay Zeka Uygulamaları
Özet
Bilgi teknolojileri ve yapay zeka, lojistik sektöründe devrim niteliğinde yenilikler getirmiştir. Bu teknolojiler, operasyonel verimliliği artırmak, maliyetleri düşürmek ve müşteri memnuniyetini artırmak için kritik öneme sahiptir. Lojistik firmaları, bu teknolojilere yatırım yaparak rekabet avantajı elde edebilir ve gelecekteki zorluklara karşı daha dayanıklı hale gelebilir. Firmalar iş sürecini veya karar almayı geliştirmek, tedarikçiler ve müşterilerle ilişkileri geliştirmek, firmalarını rakiplerinden farklılaştırmak, buna bağlı olarak sektördeki verimliliği artırmak, maliyetleri düşürmek ve müşteri memnuniyetini yükseltmek için bilgi teknolojisini kullanır.
Günümüzde akıllı teknolojilerin yaygın olarak kullanıldığını ve çeşitli sektörlerde birçok yeni konu yarattığını görebiliriz. Genel olarak planlama, izleme, kontrol vb. konularda uygun ve verimlilik sağlamak için sistemlerin veya cihazların kendi kararlarını vermelerine olanak tanıyan özerkliğe odaklanır. Bu tür hedeflere ulaşmak için akıllı teknolojiler (Smart Technology: ST), Yapay Zeka (Artificial Intelligence: AI) ve Makine Öğrenimi (Machine Learning: ML), Büyük Veri (Big Data: BD), Blockchain vb. gibi belirtilen bilgi sistemleri ve teknolojiler tarafından kolaylaştırılabilir (Chung ve diğerleri, 2020)
Bilgi teknolojileri ve yapay zeka, lojistik operasyonda ve ulaşım ağında önemli değişikliklere neden olmuş ve bu nedenle akıllı teknoloji çağında birçok yeni planlama problemi, optimizasyon metodolojisi ve çözüm yaklaşımları ortaya çıkmıştır. Ancak bu konuyu kapsamlı bir şekilde araştıran daha önce yapılmış bir çalışma bulunmamaktadır, dolayısıyla bu bölümde akıllı teknolojilerle ilgili ayrıntılı bir çalışma yer almaktadır.
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Referanslar
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Cheng, T. C. E., Kriheli, B., Levner, E., & Ng, C. T. (2021). Scheduling an autonomous robot searching for hidden targets. Annals of Operations Research, 298(1), 95-109.
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Choi, T. M. (2020). Innovative “bring-service-near-your-home” operations under Corona-virus (COVID-19/SARS-CoV-2) outbreak: Can logistics become the messiah?. Transportation Research Part E: Logistics and Transportation Review, 140, 101961.
Choi, T. M. (2021). Risk analysis in logistics systems: A research agenda during and after the COVID-19 pandemic. Transportation Research Part E: Logistics and Transportation Review, 145, 102190.
Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1883.
Choi, T. M., Wen, X., Sun, X., & Chung, S. H. (2019). The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era. Transportation Research Part E: Logistics and Transportation Review, 127, 178-191.
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Dai, Z., Liu, X. C., Chen, X., & Ma, X. (2020). Joint optimization of scheduling and capacity for mixed traffic with autonomous and human-driven buses: A dynamic programming approach. Transportation Research Part C: Emerging Technologies, 114, 598-619.
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Gelareh, S., Merzouki, R., McGinley, K., Murray, R., 2013. Scheduling Intelligent and Autonomous Vehicles under a Unloading/Reloading Cooperation Strategy at Container Terminals. Transportation Research Part C, 33, 1–21.
Ghofrani, F., He, Q., Goverde, R. M., & Liu, X. (2018). Recent applications of big data analytics in railway transportation systems: A survey. Transportation Research Part C: Emerging Technologies, 90, 226-246.
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