An OvS-MultiObjective Algorithm Approach for Lane Reversal Problem
The lane reversal has proven to be a useful method to mitigate traffic congestion during rush hour or in case of specific events that affect high traffic volumes. In this work we propose a methodology that is placed within optimization via Simulation, by means of which a multi-objective genetic algorithm and simulations of traffic are used to determine the configuration of ideal lane reversal.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Wasserstein Distributionally Robust Inverse Multiobjective Optimization
Inverse multiobjective optimization provides a general framework for the unsupervised learning task of inferring parameters of a multiobjective decision making problem (DMP), based on a set of observed decisions from the…
Decision MakingMultiobjective OptimizationPortfolio OptimizationEvolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons
Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has consider…
A Pareto Optimal D* Search Algorithm for Multiobjective Path Planning
Path planning is one of the most vital elements of mobile robotics, providing the agent with a collision-free route through the workspace. The global path plan can be calculated with a variety of informed search algorith…
Multiobjective OptimizationA Pareto Front-Based Multiobjective Path Planning Algorithm
Path planning is one of the most vital elements of mobile robotics. With a priori knowledge of the environment, global path planning provides a collision-free route through the workspace. The global path plan can be calc…
Multiobjective OptimizationMODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization
Recently, a deep reinforcement learning method is proposed to solve multiobjective optimization problem. In this method, the multiobjective optimization problem is decomposed to a number of single-objective optimization …
Deep Reinforcement LearningMultiobjective Optimizationreinforcement-learningReinforcement Learning+1