In this paper,an oil well production scheduling problem for the light load oil well during petroleum field exploitation was studied.The oil well production scheduling was to determine the turn on/off status and oil fl...In this paper,an oil well production scheduling problem for the light load oil well during petroleum field exploitation was studied.The oil well production scheduling was to determine the turn on/off status and oil flow rates of the wells in a given oil reservoir,subject to a number of constraints such as minimum up/down time limits and well grouping.The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost.Due to the NP-hardness of the problem,an improved particle swarm optimization(PSO) algorithm with a new velocity updating formula was developed to solve the problem approximately.Computational experiments on randomly generated instances were carried out to evaluate the performance of the model and the algorithm's effectiveness.Compared with the commercial solver CPLEX,the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.展开更多
This study proposes an integer linear program model for ride-sharing,electric,autonomous mobility on demand(REAMoD)system operations and develops a model predictive control(MPC)algorithm to optimize the decisions of r...This study proposes an integer linear program model for ride-sharing,electric,autonomous mobility on demand(REAMoD)system operations and develops a model predictive control(MPC)algorithm to optimize the decisions of ride matching,vehicle routing,rebalancing,and charging.The system ensures that electric autonomous vehicles provide transportation services for up to two customers to share a ride and that they can be charged automatically during the operating period.The RE-AMoD problem is formulated as a network flow optimization problem considering ride-sharing and charging control.The objective is to minimize the customers’waiting time while minimizing the system’s energy consumption.An iterative MPC is developed to compute the optimal control policy for real-time control.The case study uses real-world data from San Francisco to validate the model performance by comparing benchmark models in an RE-AMoD simulation platform and investigating the impact of ridesharing and smart charging strategies on system performance by comparing models with no ride-sharing and heuristic charging strategies.The results show that the smart charging policy is critical for realizing ride-sharing's full advantages in RE-AMoD systems.Allowing the sharing of trips significantly improves system performance in terms of reducing fleet sizes and energy consumption while improving the customer level of service.展开更多
基金Supported by National High Technology Research and Development Program of China(2013AA040704)the Fund for the National Natural Science Foundation of China(61374203)
文摘In this paper,an oil well production scheduling problem for the light load oil well during petroleum field exploitation was studied.The oil well production scheduling was to determine the turn on/off status and oil flow rates of the wells in a given oil reservoir,subject to a number of constraints such as minimum up/down time limits and well grouping.The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost.Due to the NP-hardness of the problem,an improved particle swarm optimization(PSO) algorithm with a new velocity updating formula was developed to solve the problem approximately.Computational experiments on randomly generated instances were carried out to evaluate the performance of the model and the algorithm's effectiveness.Compared with the commercial solver CPLEX,the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.
基金supported by the Swedish Strategic Research Area in Transportation(TRENoP).
文摘This study proposes an integer linear program model for ride-sharing,electric,autonomous mobility on demand(REAMoD)system operations and develops a model predictive control(MPC)algorithm to optimize the decisions of ride matching,vehicle routing,rebalancing,and charging.The system ensures that electric autonomous vehicles provide transportation services for up to two customers to share a ride and that they can be charged automatically during the operating period.The RE-AMoD problem is formulated as a network flow optimization problem considering ride-sharing and charging control.The objective is to minimize the customers’waiting time while minimizing the system’s energy consumption.An iterative MPC is developed to compute the optimal control policy for real-time control.The case study uses real-world data from San Francisco to validate the model performance by comparing benchmark models in an RE-AMoD simulation platform and investigating the impact of ridesharing and smart charging strategies on system performance by comparing models with no ride-sharing and heuristic charging strategies.The results show that the smart charging policy is critical for realizing ride-sharing's full advantages in RE-AMoD systems.Allowing the sharing of trips significantly improves system performance in terms of reducing fleet sizes and energy consumption while improving the customer level of service.
