As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with t...As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with the objective of scheduling multiple transport routes considering loading constraints along with time penalty function to minimize the total cost. Then a genetic algorithm( GA) is developed. The specific encoding and genetic operators for FVRPTW are devised.Especially,in order to accelerate its convergence,an improved termination condition is given. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm and a series of experiments are conducted over a set of finished vehicle routing problems. The results demonstrate that the proposed approach has superior performance and satisfies users in practice. Contributions of the study are the modeling and solving of a complex FVRPTW in logistics industry.展开更多
For smooth optimization problem with equMity constraints, new continuously differentiable penalty function is derived. It is proved exact in the sense that local optimizers of a nonlinear program are precisely the opt...For smooth optimization problem with equMity constraints, new continuously differentiable penalty function is derived. It is proved exact in the sense that local optimizers of a nonlinear program are precisely the optimizers of the associated penalty function under some nondegeneracy assumption. It is simple in the sense that the penalty function only includes the objective function and constrained functions, and it doesn＇t include their gradients. This is achieved by augmenting the dimension of the program by a variable that controls the weight of the penalty terms.展开更多
Regularized minimization problems with nonconvex, nonsmooth, even non-Lipschitz penalty functions have attracted much attention in recent years, owing to their wide applications in statistics, control,system identific...Regularized minimization problems with nonconvex, nonsmooth, even non-Lipschitz penalty functions have attracted much attention in recent years, owing to their wide applications in statistics, control,system identification and machine learning. In this paper, the non-Lipschitz ?_p(0 < p < 1) regularized matrix minimization problem is studied. A global necessary optimality condition for this non-Lipschitz optimization problem is firstly obtained, specifically, the global optimal solutions for the problem are fixed points of the so-called p-thresholding operator which is matrix-valued and set-valued. Then a fixed point iterative scheme for the non-Lipschitz model is proposed, and the convergence analysis is also addressed in detail. Moreover,some acceleration techniques are adopted to improve the performance of this algorithm. The effectiveness of the proposed p-thresholding fixed point continuation(p-FPC) algorithm is demonstrated by numerical experiments on randomly generated and real matrix completion problems.展开更多
The guide-weight method is introduced to solve the topology optimization problems of thermoelastic structures in this paper.First,the solid isotropic microstructure with penalization(SIMP)with different penalty factor...The guide-weight method is introduced to solve the topology optimization problems of thermoelastic structures in this paper.First,the solid isotropic microstructure with penalization(SIMP)with different penalty factors is selected as a material interpolation model for the thermal and mechanical fields.The general criteria of the guide-weight method is then presented.Two types of iteration formulas of the guide-weight method are applied to the topology optimization of thermoelastic structures,one of which is to minimize the mean compliance of the structure with material constraint,whereas the other one is to minimize the total weight with displacement constraint.For each type of problem,sensitivity analysis is conducted based on SIMP model.Finally,four classical 2-dimensional numerical examples and a 3-dimensional numerical example considering the thermal field are selected to perform calculation.The factors that affect the optimal topology are discussed,and the performance of the guide-weight method is tested.The results show that the guide-weight method has the advantages of simple iterative formula,fast convergence and relatively clear topology result.展开更多
Some classical penalty function algorithms may not always be convergent under big penalty parameters in Matlab software,which makes them impossible to find out an optimal solution to constrained optimization problems....Some classical penalty function algorithms may not always be convergent under big penalty parameters in Matlab software,which makes them impossible to find out an optimal solution to constrained optimization problems.In this paper,a novel penalty function(called M-objective penalty function) with one penalty parameter added to both objective and constrained functions of inequality constrained optimization problems is proposed.Based on the M-objective penalty function,an algorithm is developed to solve an optimal solution to the inequality constrained optimization problems,with its convergence proved under some conditions.Furthermore,numerical results show that the proposed algorithm has a much better convergence than the classical penalty function algorithms under big penalty parameters,and is efficient in choosing a penalty parameter in a large range in Matlab software.展开更多
基金Supported by the National Natural Science Foundation of China(No.51565036)
文摘As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with the objective of scheduling multiple transport routes considering loading constraints along with time penalty function to minimize the total cost. Then a genetic algorithm( GA) is developed. The specific encoding and genetic operators for FVRPTW are devised.Especially,in order to accelerate its convergence,an improved termination condition is given. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm and a series of experiments are conducted over a set of finished vehicle routing problems. The results demonstrate that the proposed approach has superior performance and satisfies users in practice. Contributions of the study are the modeling and solving of a complex FVRPTW in logistics industry.
基金supported by the National Natural Science Foundation of China under Grant No.10971118the Science foundation of Shandong Province(J10LG04)
文摘For smooth optimization problem with equMity constraints, new continuously differentiable penalty function is derived. It is proved exact in the sense that local optimizers of a nonlinear program are precisely the optimizers of the associated penalty function under some nondegeneracy assumption. It is simple in the sense that the penalty function only includes the objective function and constrained functions, and it doesn＇t include their gradients. This is achieved by augmenting the dimension of the program by a variable that controls the weight of the penalty terms.
基金supported by National Natural Science Foundation of China(Grant Nos.11401124 and 71271021)the Scientific Research Projects for the Introduced Talents of Guizhou University(Grant No.201343)the Key Program of National Natural Science Foundation of China(Grant No.11431002)
文摘Regularized minimization problems with nonconvex, nonsmooth, even non-Lipschitz penalty functions have attracted much attention in recent years, owing to their wide applications in statistics, control,system identification and machine learning. In this paper, the non-Lipschitz ?_p(0 < p < 1) regularized matrix minimization problem is studied. A global necessary optimality condition for this non-Lipschitz optimization problem is firstly obtained, specifically, the global optimal solutions for the problem are fixed points of the so-called p-thresholding operator which is matrix-valued and set-valued. Then a fixed point iterative scheme for the non-Lipschitz model is proposed, and the convergence analysis is also addressed in detail. Moreover,some acceleration techniques are adopted to improve the performance of this algorithm. The effectiveness of the proposed p-thresholding fixed point continuation(p-FPC) algorithm is demonstrated by numerical experiments on randomly generated and real matrix completion problems.
基金supported by the National Natural Science Foundation of China(Grant No.51375251)the National Basic Research Program("973"Program)(Grant No.2013CB035400)of China
文摘The guide-weight method is introduced to solve the topology optimization problems of thermoelastic structures in this paper.First,the solid isotropic microstructure with penalization(SIMP)with different penalty factors is selected as a material interpolation model for the thermal and mechanical fields.The general criteria of the guide-weight method is then presented.Two types of iteration formulas of the guide-weight method are applied to the topology optimization of thermoelastic structures,one of which is to minimize the mean compliance of the structure with material constraint,whereas the other one is to minimize the total weight with displacement constraint.For each type of problem,sensitivity analysis is conducted based on SIMP model.Finally,four classical 2-dimensional numerical examples and a 3-dimensional numerical example considering the thermal field are selected to perform calculation.The factors that affect the optimal topology are discussed,and the performance of the guide-weight method is tested.The results show that the guide-weight method has the advantages of simple iterative formula,fast convergence and relatively clear topology result.
基金supported by the National Natural Science Foundation of China under Grant No.11271329
文摘Some classical penalty function algorithms may not always be convergent under big penalty parameters in Matlab software,which makes them impossible to find out an optimal solution to constrained optimization problems.In this paper,a novel penalty function(called M-objective penalty function) with one penalty parameter added to both objective and constrained functions of inequality constrained optimization problems is proposed.Based on the M-objective penalty function,an algorithm is developed to solve an optimal solution to the inequality constrained optimization problems,with its convergence proved under some conditions.Furthermore,numerical results show that the proposed algorithm has a much better convergence than the classical penalty function algorithms under big penalty parameters,and is efficient in choosing a penalty parameter in a large range in Matlab software.
