Max Gath presents a multiagent system for the optimization of transport logistics in highly complex and dynamic domains. The described solution dynamically optimizes processes and provides a high flexibility, scalability, robustness, and adaptability to individual customer demands. The experimental evaluation points out the effectiveness and efficiency by using the example of commonly applied benchmarks as well as two case studies in groupage traffic and in courier, express, and parcel services with same-day deliveries. Both case studies were performed with leading transport companies in Germany. The results demonstrate that the multiagent-based solution satisfies domain-specific requirements and exploits high optimization potential in real-world processes.
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Dr. Max Gath completed his doctoral degree at the Artificial Intelligence research group of TZI (Center for Communication and Information Technology), University of Bremen, Germany.
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Paperback. Condition: new. Paperback. Max Gath presents a multiagent system for the optimization of transport logistics in highly complex and dynamic domains. The described solution dynamically optimizes processes and provides a high flexibility, scalability, robustness, and adaptability to individual customer demands. The experimental evaluation points out the effectiveness and efficiency by using the example of commonly applied benchmarks as well as two case studies in groupage traffic and in courier, express, and parcel services with same-day deliveries. Both case studies were performed with leading transport companies in Germany. The results demonstrate that the multiagent-based solution satisfies domain-specific requirements and exploits high optimization potential in real-world processes. Max Gath presents a multiagent system for the optimization of transport logistics in highly complex and dynamic domains. The described solution dynamically optimizes processes and provides a high flexibility, scalability, robustness, and adaptability to individual customer demands. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783658140021
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