This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse function...This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.展开更多
Automated Guided Vehicles(AGVs)have been introduced into various applications,such as automated warehouse systems,flexible manufacturing systems,and container terminal systems.However,few publications have outlined pr...Automated Guided Vehicles(AGVs)have been introduced into various applications,such as automated warehouse systems,flexible manufacturing systems,and container terminal systems.However,few publications have outlined problems in need of attention in AGV applications comprehensively.In this paper,several key issues and essential models are presented.First,the advantages and disadvantages of centralized and decentralized AGVs systems were compared;second,warehouse layout and operation optimization were introduced,including some omitted areas,such as AGVs fleet size and electrical energy management;third,AGVs scheduling algorithms in chessboardlike environments were analyzed;fourth,the classical route-planning algorithms for single AGV and multiple AGVs were presented,and some Artificial Intelligence(AI)-based decision-making algorithms were reviewed.Furthermore,a novel idea for accelerating route planning by combining Reinforcement Learning(RL)andDijkstra’s algorithm was presented,and a novel idea of the multi-AGV route-planning method of combining dynamic programming and Monte-Carlo tree search was proposed to reduce the energy cost of systems.展开更多
Due to associated uncertainties,modelling the spatial distribution of depth to bedrock(DTB) is an important and challenging concern in many geo-engineering applications.The association between DTB,the safety and econo...Due to associated uncertainties,modelling the spatial distribution of depth to bedrock(DTB) is an important and challenging concern in many geo-engineering applications.The association between DTB,the safety and economy of design structures implies that generating more precise predictive models can be of vital interest.In the present study,the challenge of applying an optimally predictive threedimensional(3D) spatial DTB model for an area in Stockholm,Sweden was addressed using an automated intelligent computing design procedure.The process was developed and programmed in both C++and Python to track their performance in specified tasks and also to cover a wide variety of diffe rent internal characteristics and libraries.In comparison to the ordinary Kriging(OK) geostatistical tool,the superiority of the developed automated intelligence system was demonstrated through the analysis of confusion matrices and the ranked accuracies of different statistical errors.The re sults showed that in the absence of measured data,the intelligence models as a flexible and efficient alternative approach can account for associated uncertainties,thus creating more accurate spatial 3D models and providing an appropriate prediction at any point in the subsurface of the study area.展开更多
文摘This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.
文摘Automated Guided Vehicles(AGVs)have been introduced into various applications,such as automated warehouse systems,flexible manufacturing systems,and container terminal systems.However,few publications have outlined problems in need of attention in AGV applications comprehensively.In this paper,several key issues and essential models are presented.First,the advantages and disadvantages of centralized and decentralized AGVs systems were compared;second,warehouse layout and operation optimization were introduced,including some omitted areas,such as AGVs fleet size and electrical energy management;third,AGVs scheduling algorithms in chessboardlike environments were analyzed;fourth,the classical route-planning algorithms for single AGV and multiple AGVs were presented,and some Artificial Intelligence(AI)-based decision-making algorithms were reviewed.Furthermore,a novel idea for accelerating route planning by combining Reinforcement Learning(RL)andDijkstra’s algorithm was presented,and a novel idea of the multi-AGV route-planning method of combining dynamic programming and Monte-Carlo tree search was proposed to reduce the energy cost of systems.
基金funded through the support of the Swedish Transport Administration through Better Interactions in Geotechnics(BIG)the Rock engineering Research Foundation(BeFo)Tyrens AB。
文摘Due to associated uncertainties,modelling the spatial distribution of depth to bedrock(DTB) is an important and challenging concern in many geo-engineering applications.The association between DTB,the safety and economy of design structures implies that generating more precise predictive models can be of vital interest.In the present study,the challenge of applying an optimally predictive threedimensional(3D) spatial DTB model for an area in Stockholm,Sweden was addressed using an automated intelligent computing design procedure.The process was developed and programmed in both C++and Python to track their performance in specified tasks and also to cover a wide variety of diffe rent internal characteristics and libraries.In comparison to the ordinary Kriging(OK) geostatistical tool,the superiority of the developed automated intelligence system was demonstrated through the analysis of confusion matrices and the ranked accuracies of different statistical errors.The re sults showed that in the absence of measured data,the intelligence models as a flexible and efficient alternative approach can account for associated uncertainties,thus creating more accurate spatial 3D models and providing an appropriate prediction at any point in the subsurface of the study area.
