The Experts below are selected from a list of 21885 Experts worldwide ranked by ideXlab platform
Jian Tang - One of the best experts on this subject based on the ideXlab platform.
-
dynamic routing model and solution methods for fleet management with mobile technologies
International Journal of Production Economics, 2008Co-Authors: Bernard K S Cheung, K L Choy, Wenzhong Shi, Jian TangAbstract:We develop and analyze a mathematical model for dynamic fleet management that captures the characteristics of modern vehicle operations. The model takes into consideration dynamic data such as vehicle locations, travel time, and incoming customer orders. The solution method includes an effective procedure for solving the static problem and an efficient re-optimization procedure for updating the Route Plan as dynamic information arrives. Computational experiments show that our re-optimization procedure can generate near-optimal solutions.
Hossein Afkhami - One of the best experts on this subject based on the ideXlab platform.
-
modeling of Route Planning system based on q value based dynamic programming with multi agent reinforcement learning algorithms
Engineering Applications of Artificial Intelligence, 2014Co-Authors: Mortaza Zolfpourarokhlo, Ali Selamat, Siti Zaiton Mohd Hashim, Hossein AfkhamiAbstract:In this paper, a new model for a Route Planning system based on multi-agent reinforcement learning (MARL) algorithms is proposed. The combined Q-value based dynamic programming (QVDP) with Boltzmann distribution was used to solve vehicle delay's problems by studying the weights of various components in road network environments such as weather, traffic, road safety, and fuel capacity to create a priority Route Plan for vehicles. The important part of the study was to use a multi-agent system (MAS) with learning abilities which in order to make decisions about routing vehicles between Malaysia's cities. The evaluation was done using a number of case studies that focused on road networks in Malaysia. The results of these experiments indicated that the travel durations for the case studies predicted by existing approaches were between 0.00 and 12.33% off from the actual travel times by the proposed method. From the experiments, the results illustrate that the proposed approach is a unique contribution to the field of computational intelligence in the Route Planning system.
De Zhang - One of the best experts on this subject based on the ideXlab platform.
-
Individualized Tour Route Plan Algorithm Based on Tourist Sight Spatial Interest Field
ISPRS International Journal of Geo-Information, 2019Co-Authors: Xiao Zhou, Yinhu Zhan, Guanghui Feng, De ZhangAbstract:Smart tourism is the new frontier field of the tourism research. To solve current problems of smart tourism and tourism geographic information system (GIS), individualized tour guide Route Plan algorithm based on tourist sight spatial interest field is set up in the study. Feature interest tourist sight extracting matrix is formed and basic modeling data is obtained from mass tourism data. Tourism groups are determined by age index. Different age group tourists have various interests; thus interest field mapping model is set up based on individual needs and interests. Random selecting algorithm for selecting interest tourist sights by smart machine is designed. The algorithm covers all tourist sights and relative data information to ensure each tourist sight could be selected equally. In the study, selected tourist sights are set as important nodes while iteration intervals and sub-iteration intervals are defined. According to the principle of proximity and completely random, motive iteration clusters and sub-clusters are formed by all tourist sight parent nodes. Tourist sight data information and geospatial information are set as quantitative indexes to calculate motive iteration values and motive iteration decision trees of each cluster are formed, and then all motive iteration values are stored in descending order in a vector. For each cluster, there is an optimal motive iteration tree and a local optimal solution. For all clusters, there is a global optimal solution. Simulation experiments are performed and results data as well as motive iteration trees are analyzed and evaluated. The evaluation results indicate that the algorithm is effective for mass tourism data mining. The final optimal tour Routes Planned by the smart machine are closely related to tourists’ needs, interests, and habits, which are fully integrated with geospatial services. The algorithm is an effective demonstration of the application on mass tourism data mining.
Jun Imai - One of the best experts on this subject based on the ideXlab platform.
-
a distributed Route Planning method for multiple mobile robots using lagrangian decomposition technique
International Conference on Robotics and Automation, 2003Co-Authors: Tatsushi Nishi, Masakazu Ando, Masami Konishi, Jun ImaiAbstract:For the transportation in semiconductor fabricating bay, Route Planning of multiple AGVs (Automated Guided Vehicles) is expected to minimize the total transportation time without collision and deadlock among AGVs. In this paper, we propose a distributed Route Planning method for multiple mobile robots using Lagrangian decomposition technique. The proposed method has a characteristic that each mobile robot individually creates a near optimal Route through the repetitive data exchange among the AGVs and the local optimization of its Route using Dijkstra's algorithm. The proposed method is successively applied to transportation Route Planning problem in semiconductor fabricating bay. The optimality of the solution generated by the proposed method is evaluated by using the duality gap derived by using Lagrangian relaxation method. A near optimal solution within 5% of duality gap for a large scale transportation system consisting of 143 nodes and 15 AGVs can be obtained only within five seconds of computation time. The proposed method is implemented on 3 AGVs system and the Route Plan is derived taking the size of AGV into account. It is experimentally shown that the proposed method can be found to be effective for various types of problems despite the fact that each Route for AGV is created without considering the entire objective function.
Bernard K S Cheung - One of the best experts on this subject based on the ideXlab platform.
-
dynamic routing model and solution methods for fleet management with mobile technologies
International Journal of Production Economics, 2008Co-Authors: Bernard K S Cheung, K L Choy, Wenzhong Shi, Jian TangAbstract:We develop and analyze a mathematical model for dynamic fleet management that captures the characteristics of modern vehicle operations. The model takes into consideration dynamic data such as vehicle locations, travel time, and incoming customer orders. The solution method includes an effective procedure for solving the static problem and an efficient re-optimization procedure for updating the Route Plan as dynamic information arrives. Computational experiments show that our re-optimization procedure can generate near-optimal solutions.
