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3 books on AI for Logistics [PDF]
July 25, 2025 | 16 |
Books on AI for logistics are invaluable resources for startups venturing into the development of AI solutions tailored for the logistics and supply chain industry. These texts delve into the applications of artificial intelligence in route optimization, demand forecasting, inventory management, and warehouse automation, providing startups with a deep understanding of the complexities and opportunities within the logistics field.
1. Transformative Impact of AI in Supply Chain Management
2025 by Arif, Jabir, Jawab, Fouad
The authors of this book argue that implementation of artificial intelligence into Logistics 5.0 (a concept that intends not to replace humans with AI, but organize human-AI collaboration) requires a fundamental rethinking of existing paradigms in supply chain management. Traditional models define logistics as a linear, efficiency-oriented process aimed at reducing costs, maximizing goods flow and caring for the environment. However, Logistics 5.0 challenges these models and redefines logistics not simply as a technical function, but as a complex, adaptive and value-oriented system. Instead of concentrating decision making in a central hub, Logistics 5.0 systems rely on autonomous agents - robots, platforms, connected devices - that interact locally and in real time. The use of AI for tactical and strategic decision making opens up new possibilities for SCM. Issues of accountability, transparency and bias need to be theoretically grounded to support the development of reliable and trustworthy logistics systems. Finally, contributions from related fields such as urban studies, smart city planning and technology acceptance models (TAM, UTAUT) are increasingly needed to understand how logistics interacts with broader socio-technical systems.
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2. AI-Powered Logistics: How Artificial Intelligence Will Revolutionize Transportation, shipping and logistics.
2024 by Hebooks
This AI-generated book may serve as an interesting source of what LLMs think about how AI disrupts logistics. According to the LLM that wrote this work, the main function of AI for logistics is predictive analytics. And indeed, you can optimize the route and timeline of a delivery, but if that delivery is not needed or moves excessive goods, no one benefits. ML algorithms can process historical data to predict demand patterns in certain locations. Thanks to these predictions, logistics companies can optimize inventory levels, distribution networks and transportation schedules (and in result save a lot of fuel and other resources). AI also optimizes dynamic routing of transportation flows. It takes into account changing parameters such as traffic, weather and changes in orders. Companies such as Amazon and DHL use AI to automate their warehouses. AI-powered robots move around warehouses, picking and packing products with high speed and accuracy. In the transportation industry, companies like Tesla are developing autonomous trucks. In the context of shipping, AI is becoming the backbone of efficiency. AI algorithms can analyze vast amounts of data related to routes, weather conditions and fuel consumption, optimizing sea voyages. Smart ports use AI for efficient docking, loading and unloading. AI-powered predictive maintenance ensures that ships are in optimal condition, reducing downtime and increasing overall productivity.
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3. AI-Based Transportation Planning and Operation
2021 by Keemin Sohn
This book contains several case studies on using machine learning to optimize transportation. I particularly liked the case study on measuring traffic volumes without the need for image labeling. Traffic volume detection is needed for example, to optimize the operation of traffic lights. Typically, traffic measurement (using computer vision) is a two-step procedure involving tracking and detection. Object detection algorithms such as YOLO and Fast-RCNN are usually applied to vehicle detection. However, tracking requires an additional algorithm that matches cars that appeared in the previous frame of a video with their appearance in the next frame. This two-step algorithm is widely used but requires significant computational resources for training, testing and evaluation. In the described study, a simpler algorithm based on an autoencoder is proposed - and it does not require data labeling for training. The autoencoder was trained on the pixel intensities of a virtual line superimposed on images in an unsupervised mode. The last hidden node of the coding part of the autoencoder generates a scalar signal that can be used to judge whether a car is passing. A cycle-consistent generative adversarial network (CycleGAN) was used to transform the original image of car into simplified illustrative image. This increased the efficiency of the autoencoder in determining the presence of a car. According to the authors, the proposed model is significantly lighter and faster than the YOLO-based model and its accuracy is comparable or higher.
Download PDF
How to download PDF:
1. Install Gooreader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Transformative Impact of AI in Supply Chain Management
2025 by Arif, Jabir, Jawab, Fouad
The authors of this book argue that implementation of artificial intelligence into Logistics 5.0 (a concept that intends not to replace humans with AI, but organize human-AI collaboration) requires a fundamental rethinking of existing paradigms in supply chain management. Traditional models define logistics as a linear, efficiency-oriented process aimed at reducing costs, maximizing goods flow and caring for the environment. However, Logistics 5.0 challenges these models and redefines logistics not simply as a technical function, but as a complex, adaptive and value-oriented system. Instead of concentrating decision making in a central hub, Logistics 5.0 systems rely on autonomous agents - robots, platforms, connected devices - that interact locally and in real time. The use of AI for tactical and strategic decision making opens up new possibilities for SCM. Issues of accountability, transparency and bias need to be theoretically grounded to support the development of reliable and trustworthy logistics systems. Finally, contributions from related fields such as urban studies, smart city planning and technology acceptance models (TAM, UTAUT) are increasingly needed to understand how logistics interacts with broader socio-technical systems.
Download PDF
2. AI-Powered Logistics: How Artificial Intelligence Will Revolutionize Transportation, shipping and logistics.
2024 by Hebooks
This AI-generated book may serve as an interesting source of what LLMs think about how AI disrupts logistics. According to the LLM that wrote this work, the main function of AI for logistics is predictive analytics. And indeed, you can optimize the route and timeline of a delivery, but if that delivery is not needed or moves excessive goods, no one benefits. ML algorithms can process historical data to predict demand patterns in certain locations. Thanks to these predictions, logistics companies can optimize inventory levels, distribution networks and transportation schedules (and in result save a lot of fuel and other resources). AI also optimizes dynamic routing of transportation flows. It takes into account changing parameters such as traffic, weather and changes in orders. Companies such as Amazon and DHL use AI to automate their warehouses. AI-powered robots move around warehouses, picking and packing products with high speed and accuracy. In the transportation industry, companies like Tesla are developing autonomous trucks. In the context of shipping, AI is becoming the backbone of efficiency. AI algorithms can analyze vast amounts of data related to routes, weather conditions and fuel consumption, optimizing sea voyages. Smart ports use AI for efficient docking, loading and unloading. AI-powered predictive maintenance ensures that ships are in optimal condition, reducing downtime and increasing overall productivity.
Download PDF
3. AI-Based Transportation Planning and Operation
2021 by Keemin Sohn
This book contains several case studies on using machine learning to optimize transportation. I particularly liked the case study on measuring traffic volumes without the need for image labeling. Traffic volume detection is needed for example, to optimize the operation of traffic lights. Typically, traffic measurement (using computer vision) is a two-step procedure involving tracking and detection. Object detection algorithms such as YOLO and Fast-RCNN are usually applied to vehicle detection. However, tracking requires an additional algorithm that matches cars that appeared in the previous frame of a video with their appearance in the next frame. This two-step algorithm is widely used but requires significant computational resources for training, testing and evaluation. In the described study, a simpler algorithm based on an autoencoder is proposed - and it does not require data labeling for training. The autoencoder was trained on the pixel intensities of a virtual line superimposed on images in an unsupervised mode. The last hidden node of the coding part of the autoencoder generates a scalar signal that can be used to judge whether a car is passing. A cycle-consistent generative adversarial network (CycleGAN) was used to transform the original image of car into simplified illustrative image. This increased the efficiency of the autoencoder in determining the presence of a car. According to the authors, the proposed model is significantly lighter and faster than the YOLO-based model and its accuracy is comparable or higher.
Download PDF
How to download PDF:
1. Install Gooreader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
