Automated Guided Vehicle(AGV)scheduling problem is an emerging research topic in the recent literature.This paper studies an integrated scheduling problem comprising task assignment and path planning for AGVs.To reduc...Automated Guided Vehicle(AGV)scheduling problem is an emerging research topic in the recent literature.This paper studies an integrated scheduling problem comprising task assignment and path planning for AGVs.To reduce the transportation cost of AGVs,this work also proposes an optimization method consisting of the total running distance,total delay time,and machine loss cost of AGVs.A mathematical model is formulated for the problem at hand,along with an improved Discrete Invasive Weed Optimization algorithm(DIWO).In the proposed DIWO algorithm,an insertion-based local search operator is developed to improve the local search ability of the algorithm.A staggered time departure heuristic is also proposed to reduce the number of AGV collisions in path planning.Comprehensive experiments are conducted,and 100 instances from actual factories have proven the effectiveness of the optimization method.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
With the wide application of automated guided vehicles(AGVs) in large scale outdoor scenarios with complex terrain,the collaborative work of a large number of AGVs becomes the main trend.The effective multi-agent path...With the wide application of automated guided vehicles(AGVs) in large scale outdoor scenarios with complex terrain,the collaborative work of a large number of AGVs becomes the main trend.The effective multi-agent path finding(MAPF) algorithm is urgently needed to ensure the efficiency and realizability of the whole system. The complex terrain of outdoor scenarios is fully considered by using different values of passage cost to quantify different terrain types. The objective of the MAPF problem is to minimize the cost of passage while the Manhattan distance of paths and the time of passage are also evaluated for a comprehensive comparison. The pre-path-planning and real-time-conflict based greedy(PRG) algorithm is proposed as the solution. Simulation is conducted and the proposed PRG algorithm is compared with waiting-stop A^(*) and conflict based search(CBS) algorithms. Results show that the PRG algorithm outperforms the waiting-stop A^(*) algorithm in all three performance indicators,and it is more applicable than the CBS algorithm when a large number of AGVs are working collaboratively with frequent collisions.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62273221 and 52205529)the Discipline with Strong Characteristics of Liaocheng University Intelligent Science and Technology(No.319462208).
文摘Automated Guided Vehicle(AGV)scheduling problem is an emerging research topic in the recent literature.This paper studies an integrated scheduling problem comprising task assignment and path planning for AGVs.To reduce the transportation cost of AGVs,this work also proposes an optimization method consisting of the total running distance,total delay time,and machine loss cost of AGVs.A mathematical model is formulated for the problem at hand,along with an improved Discrete Invasive Weed Optimization algorithm(DIWO).In the proposed DIWO algorithm,an insertion-based local search operator is developed to improve the local search ability of the algorithm.A staggered time departure heuristic is also proposed to reduce the number of AGV collisions in path planning.Comprehensive experiments are conducted,and 100 instances from actual factories have proven the effectiveness of the optimization method.
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Supported by the National Key Research and Development Program of China(No.2020YFC1807904).
文摘With the wide application of automated guided vehicles(AGVs) in large scale outdoor scenarios with complex terrain,the collaborative work of a large number of AGVs becomes the main trend.The effective multi-agent path finding(MAPF) algorithm is urgently needed to ensure the efficiency and realizability of the whole system. The complex terrain of outdoor scenarios is fully considered by using different values of passage cost to quantify different terrain types. The objective of the MAPF problem is to minimize the cost of passage while the Manhattan distance of paths and the time of passage are also evaluated for a comprehensive comparison. The pre-path-planning and real-time-conflict based greedy(PRG) algorithm is proposed as the solution. Simulation is conducted and the proposed PRG algorithm is compared with waiting-stop A^(*) and conflict based search(CBS) algorithms. Results show that the PRG algorithm outperforms the waiting-stop A^(*) algorithm in all three performance indicators,and it is more applicable than the CBS algorithm when a large number of AGVs are working collaboratively with frequent collisions.
