Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level...Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level does not depend on the leader’s decision. A new model is proposed to solve this deficiency. It is proved the feasibility of the new model when the reaction set of the lower level is lower semicontinuous. And the numerical results show that the new model has optimal solutions when the reaction set of the lower level is discrete, lower semi-continuous and non-lower semi-continuous.展开更多
This paper presents an adaptive path planner for unmanned aerial vehicles （UAVs） to adapt a real-time path search procedure to variations and fluctuations of UAVs’ relevant performances, with respect to sensory cap...This paper presents an adaptive path planner for unmanned aerial vehicles （UAVs） to adapt a real-time path search procedure to variations and fluctuations of UAVs’ relevant performances, with respect to sensory capability, maneuverability, and flight velocity limit. On the basis of a novel adaptability-involved problem statement, bi-level programming （BLP） and variable planning step techniques are introduced to model the necessary path planning components and then an adaptive path planner is developed for the purpose of adaptation and optimization. Additionally, both probabilistic-risk-based obstacle avoidance and performance limits are described as path search constraints to guarantee path safety and navigability. A discrete-search-based path planning solution, embedded with four optimization strategies, is especially designed for the planner to efficiently generate optimal flight paths in complex operational spaces, within which different surface-to-air missiles （SAMs） are deployed. Simulation results in challenging and stochastic scenarios firstly demonstrate the effectiveness and efficiency of the proposed planner, and then verify its great adaptability and relative stability when planning optimal paths for a UAV with changing or fluctuating performances.展开更多
An algorithm is proposed in this paper for solving two-dimensional bi-level linear programming problems without making a graph. Based on the classification of constraints, algorithm removes all redundant constraints, ...An algorithm is proposed in this paper for solving two-dimensional bi-level linear programming problems without making a graph. Based on the classification of constraints, algorithm removes all redundant constraints, which eliminate the possibility of cycling and the solution of the problem is reached in a finite number of steps. Example to illustrate the method is also included in the paper.展开更多
In this work we propose a solution method based on Lagrange relaxation for discrete-continuous bi-level problems, with binary variables in the leading problem, considering the optimistic approach in bi-level programmi...In this work we propose a solution method based on Lagrange relaxation for discrete-continuous bi-level problems, with binary variables in the leading problem, considering the optimistic approach in bi-level programming. For the application of the method, the two-level problem is reformulated using the Karush-Kuhn-Tucker conditions. The resulting model is linearized taking advantage of the structure of the leading problem. Using a Lagrange relaxation algorithm, it is possible to find a global solution efficiently. The algorithm was tested to show how it performs.展开更多
To explore the parking pricing of multiple parking facilities, this paper proposes a bi-level programming model, in which the interactions between parking operators and travelers are explicitly considered. The upper-l...To explore the parking pricing of multiple parking facilities, this paper proposes a bi-level programming model, in which the interactions between parking operators and travelers are explicitly considered. The upper-level sub-model simulates the price decision-making behaviors of the parking operators whose objectives may vary under different operation regimes, such as monopoly market, oligopoly competition, and social optimum. The lower level represents a network equilibrium model that simulates how travelers choose modes, routes, and parking facilities. The proposed model is solved by a sensitivity based algorithm, and applied to a numerical experiment, in which three types of parking facilities are studied, i.e., the off-road parking lot, the curb parking lot, and the parking-and-ride (P&R) facility. The results show in oligopoly market that the level of parking price reaches the lowest point, nonetheless the social welfare decreases to the lowest simultaneously;and the share of P&R mode goes to the highest value, however the total network costs rise also to the highest. While the monopoly market and the social optimum regimes result in solutions of which P&R facilities suffer negative profits and have to be subsidized.展开更多
基金supported by the National Natural Science Foundationof China (70771080)the National Science Foundation of Hubei Province(20091107)Hubei Province Key Laboratory of Systems Science in Metallurgical Process (B201003)
文摘Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level does not depend on the leader’s decision. A new model is proposed to solve this deficiency. It is proved the feasibility of the new model when the reaction set of the lower level is lower semicontinuous. And the numerical results show that the new model has optimal solutions when the reaction set of the lower level is discrete, lower semi-continuous and non-lower semi-continuous.
基金the National Natural Science Foundation of China(No.60904066)
文摘This paper presents an adaptive path planner for unmanned aerial vehicles （UAVs） to adapt a real-time path search procedure to variations and fluctuations of UAVs’ relevant performances, with respect to sensory capability, maneuverability, and flight velocity limit. On the basis of a novel adaptability-involved problem statement, bi-level programming （BLP） and variable planning step techniques are introduced to model the necessary path planning components and then an adaptive path planner is developed for the purpose of adaptation and optimization. Additionally, both probabilistic-risk-based obstacle avoidance and performance limits are described as path search constraints to guarantee path safety and navigability. A discrete-search-based path planning solution, embedded with four optimization strategies, is especially designed for the planner to efficiently generate optimal flight paths in complex operational spaces, within which different surface-to-air missiles （SAMs） are deployed. Simulation results in challenging and stochastic scenarios firstly demonstrate the effectiveness and efficiency of the proposed planner, and then verify its great adaptability and relative stability when planning optimal paths for a UAV with changing or fluctuating performances.
文摘An algorithm is proposed in this paper for solving two-dimensional bi-level linear programming problems without making a graph. Based on the classification of constraints, algorithm removes all redundant constraints, which eliminate the possibility of cycling and the solution of the problem is reached in a finite number of steps. Example to illustrate the method is also included in the paper.
文摘In this work we propose a solution method based on Lagrange relaxation for discrete-continuous bi-level problems, with binary variables in the leading problem, considering the optimistic approach in bi-level programming. For the application of the method, the two-level problem is reformulated using the Karush-Kuhn-Tucker conditions. The resulting model is linearized taking advantage of the structure of the leading problem. Using a Lagrange relaxation algorithm, it is possible to find a global solution efficiently. The algorithm was tested to show how it performs.
文摘To explore the parking pricing of multiple parking facilities, this paper proposes a bi-level programming model, in which the interactions between parking operators and travelers are explicitly considered. The upper-level sub-model simulates the price decision-making behaviors of the parking operators whose objectives may vary under different operation regimes, such as monopoly market, oligopoly competition, and social optimum. The lower level represents a network equilibrium model that simulates how travelers choose modes, routes, and parking facilities. The proposed model is solved by a sensitivity based algorithm, and applied to a numerical experiment, in which three types of parking facilities are studied, i.e., the off-road parking lot, the curb parking lot, and the parking-and-ride (P&R) facility. The results show in oligopoly market that the level of parking price reaches the lowest point, nonetheless the social welfare decreases to the lowest simultaneously;and the share of P&R mode goes to the highest value, however the total network costs rise also to the highest. While the monopoly market and the social optimum regimes result in solutions of which P&R facilities suffer negative profits and have to be subsidized.
