Machine learning is an Artificial Intelligence (or AI) application, an idea that came into being by giving machines access to data and letting them learn by themselves. AI has been making headlines, especially since C...Machine learning is an Artificial Intelligence (or AI) application, an idea that came into being by giving machines access to data and letting them learn by themselves. AI has been making headlines, especially since ChatGPT was introduced. Malaysia has taken many significant steps to embrace and integrate the technology into various sectors. These include encouraging large companies to build AI infrastructure, creating AI training opportunities (for example, the local media reported Microsoft and Google plan to invest USD 2.2 billion and USD 2 billion, respectively, in the said activities), and, as part of AI Talent Roadmap 2024-2030, establishing AI faculty in one of its public universities (i.e., “Universiti Teknologi Malaysia”) leading the way in the integration and teaching of AI throughout the country. This article introduces several products developed by the author (for the energy and transportation industries) and recommends their improvement by incorporating Machine learning.展开更多
The container sea-rail multimodal transport system faces complex challenges with de- mand uncertainties for joint slot allocation and dynamic pricing. The challenge is formulated as a two-stage optimal model based on ...The container sea-rail multimodal transport system faces complex challenges with de- mand uncertainties for joint slot allocation and dynamic pricing. The challenge is formulated as a two-stage optimal model based on revenue management （RM） as actual slots sale of multi-node container sea-rail multimodal transport usually includes contract sale to large shippers and free sale to scattered shippers. First stage in the model utilizes an origin-destination control approach, formulated as a stochastic integer programming equation, to settle long-term slot allocation in the contract market and empty container allocation. Second stage in the model is formulated as a stochastic nonlinear programming equation to solve a multiproduct joint dynamic pricing and inventory control problem for price settling and slot allocation in each period of free market. Considering the random nature of demand, the methods of chance constrained programming and robust optimi- zation are utilized to transform stochastic models into deterministic models. A numerical experiment is presented to verify the availability of models and solving methods. Results of considering uncertain/certain demand are compared, which show that the two-stage optimal strategy integrating slot allocation with dynamic pricing considering random de- mand is revealed to increase the revenue for multimodal transport operators （MTO） while concurrently satisfying shippers＇ demand. Research resulting from this paper will contribute to the theory and practice of container sea-rail multimodal transport revenue management and provide a scientific decision-making tool for MTO.展开更多
文摘Machine learning is an Artificial Intelligence (or AI) application, an idea that came into being by giving machines access to data and letting them learn by themselves. AI has been making headlines, especially since ChatGPT was introduced. Malaysia has taken many significant steps to embrace and integrate the technology into various sectors. These include encouraging large companies to build AI infrastructure, creating AI training opportunities (for example, the local media reported Microsoft and Google plan to invest USD 2.2 billion and USD 2 billion, respectively, in the said activities), and, as part of AI Talent Roadmap 2024-2030, establishing AI faculty in one of its public universities (i.e., “Universiti Teknologi Malaysia”) leading the way in the integration and teaching of AI throughout the country. This article introduces several products developed by the author (for the energy and transportation industries) and recommends their improvement by incorporating Machine learning.
基金supported by the National Natural Science Foundation of China(No.71372088)the scientific research fund of Education Department of Liaoning Province (No.L2014179,L2013207)
文摘The container sea-rail multimodal transport system faces complex challenges with de- mand uncertainties for joint slot allocation and dynamic pricing. The challenge is formulated as a two-stage optimal model based on revenue management （RM） as actual slots sale of multi-node container sea-rail multimodal transport usually includes contract sale to large shippers and free sale to scattered shippers. First stage in the model utilizes an origin-destination control approach, formulated as a stochastic integer programming equation, to settle long-term slot allocation in the contract market and empty container allocation. Second stage in the model is formulated as a stochastic nonlinear programming equation to solve a multiproduct joint dynamic pricing and inventory control problem for price settling and slot allocation in each period of free market. Considering the random nature of demand, the methods of chance constrained programming and robust optimi- zation are utilized to transform stochastic models into deterministic models. A numerical experiment is presented to verify the availability of models and solving methods. Results of considering uncertain/certain demand are compared, which show that the two-stage optimal strategy integrating slot allocation with dynamic pricing considering random de- mand is revealed to increase the revenue for multimodal transport operators （MTO） while concurrently satisfying shippers＇ demand. Research resulting from this paper will contribute to the theory and practice of container sea-rail multimodal transport revenue management and provide a scientific decision-making tool for MTO.
