Future Generation Computer Systems
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
Ant Colony Optimization
IEEE Computational Intelligence Magazine
Adaptive control of a hybrid electric vehicle
IEEE Transactions on Intelligent Transportation Systems
Optimal vehicle routing with real-time traffic information
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Coordinated road-junction traffic control by dynamic programming
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Anonymous Vehicle Reidentification Using Heterogeneous Detection Systems
IEEE Transactions on Intelligent Transportation Systems
Algorithms for Itinerary Planning in Multimodal Transportation Networks
IEEE Transactions on Intelligent Transportation Systems
Genetic Programming for the Automatic Design of Controllers for a Surface Ship
IEEE Transactions on Intelligent Transportation Systems
A Dynamic Programming Algorithm for Scheduling In-Vehicle Messages
IEEE Transactions on Intelligent Transportation Systems
Binary-Representation-Based Genetic Algorithm for Aircraft Arrival Sequencing and Scheduling
IEEE Transactions on Intelligent Transportation Systems
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Intelligent Transportation Systems
Engineering Applications of Artificial Intelligence
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This paper presents a method of block-layout design between successive stations for mass rapid transit systems (MRTSs). The aim is to save energy under the framework of the fixed-block signaling (FBS) system and the equi-block principle. Unlike past research regarding the energy savings of train operation, this paper proposes a combinatorial optimization model to reduce the computation time. In the presented approach, the problem of minimizing the energy consumption between successive stations is first formulated as a combinatorial optimization problem. Then, the train-speed trajectory for saving energy is optimized by a MAX-MIN ant system (MMAS) of ant colony optimization (ACO) algorithms. Finally, the block layout is designed in accordance with the shortest block length under the equi-block principle. It is shown that the method presents a significant improvement for the reduction of computational burden on the block-layout design. The feasibility and benefits are verified via simulation study. Analyses and discussions are also given.