Some effective heuristics for no-wait flowshops with setup times to minimize total completion time

In this work, we consider a complex flowshop scheduling problem in which both no-wait and separate setup times are considered. The optimisation criterion is the minimisation of the total completion time. We propose an effective dominance rule for the four machine case that can also be used for m machines. Five simple and fast heuristics are proposed along with two easy to code stochastic local search methods, one of them being based on Iterated Local Search (ILS). An extensive computational evaluation is carried out with two sets of 5,400 instances. All seven methods are compared to two recent algorithms. The results, confirmed by thorough statistical analyses, show that the proposed methods are more effective and efficient when compared to the best existing algorithms in the literature for the considered problem.     

A parameter-free self-adapting boundary genetic search for pipe network optimization

Commercial application of genetic algorithms (GAs) to engineering design problems, including optimal design of pipe networks, could be facilitated by the development of algorithms that require the least number of parameter tuning. This paper presents an attempt to eliminate the need for defining a priori the proper penalty parameter in GA search for pipe networks optimal designs. The method is based on the assumption that the optimal solution of a pipe network design problem lies somewhere on, or near, the boundary of the feasible region. The proposed method uses the ratio of the best feasible and infeasible designs at each generation to guide the direction of the search towards the boundary of the feasible domain by automatically adjusting the value of the penalty parameter. The value of the ratio greater than unity is interpreted as the search being performed in the feasible region and vice versa. The new adapted value of the penalty parameter at each generation is therefore calculated as the product of its current value and the aforementioned ratio. The genetic search so constructed is shown to converge to the boundary of the feasible region irrespective of the starting value of the constraint violation penalty parameter. The proposed method is described here in the context of pipe network optimisation problems but is equally applicable to any other constrained optimisation problem. The effectiveness of the method is illustrated with a benchmark pipe network optimization example from the literature.     

A Heuristic for the Vehicle Routing Problem with Time Windows

In this paper we propose a heuristic algorithm to solve the Vehicle Routing Problem with Time Windows. Its framework is a smart combination of three simple procedures: the classical k-opt exchanges improve the solution, an ad hoc procedure reduces the number of vehicles and a second objective function drives the search out of local optima. No parameter tuning is required and no random choice is made: these are the distinguishing features with respect to the recent literature. The algorithm has been tested on benchmark problems which prove it to be more effective than comparable algorithms.     

自动选取多非线性参数初始值的循环搜索法

1几何中心化加权最小二乘法给定观测数据集(x_i,y_i),i=1,2,…,n．(1．1)欲拟合加权非线性模型y_i=f(x_i,θ) ε_i,(1．2)其中模型函数f的参数为θ=(θ_1,…,θ_p)~T,随机观测误差项满足ε_i～N[0,(?)~2m(z_i,λ)],(1．3) m(z_i,λ)为已知正值函数,z_i可以是x_i,也可以是μ_i=f(x_i,θ)或二者的组合,λ为权参数,而(?)~2为未知的方差参数,λ和θ可统称为模型参数．模型的似然函数为     

From stars to comets: Improved local search for universal facility location

We improve the approximation ratio for the Universal Facility Location Problem to 6.702 by a local search algorithm with an extended pivot operation.     

Path relinking for the vehicle routing problem

This paper describes a tabu search heuristic with path relinking for the vehicle routing problem. Tabu search is a local search method that explores the solution space more thoroughly than other local search based methods by overcoming local optima. Path relinking is a method to integrate intensification and diversification in the search. It explores paths that connect previously found elite solutions. Computational results show that tabu search with path relinking is superior to pure tabu search on the vehicle routing problem.     

Solving the flight perturbation problem with meta heuristics

When there is a perturbation in a carefully constructed aircraft schedule, e.g. an aircraft breakdown, it is important to minimize the negative consequences of this disturbance. Here, a tabu search and a simulated annealing approach to the flight perturbation problem are presented. The heuristics use a tree-search algorithm to find new schedules for the aircraft, and utilize a path relinking strategy to explore paths between structurally different solutions. The computational results indicate that the solution strategies, especially the tabu search, can be successfully used to solve the flight perturbation problem.     

A Truncated Nonmonotone Gauss-Newton Method for Large-Scale Nonlinear Least-Squares Problems

In this paper, a Gauss-Newton method is proposed for the solution of large-scale nonlinear least-squares problems, by introducing a truncation strategy in the method presented in [9]. First, sufficient conditions are established for ensuring the convergence of an iterative method employing a truncation scheme for computing the search direction, as approximate solution of a Gauss-Newton type equation. Then, a specific truncated Gauss-Newton algorithm is described, whose global convergence is ensured under standard assumptions, together with the superlinear convergence rate in the zero-residual case. The results of a computational experimentation on a set of standard test problems are reported.     

两种万维网搜索算法的比较

Google 创始人sergey Brin 和Lawrence Page 把万维网搜索算法PageRank 定义成某个非周期不可约马氏链的唯一平稳分布.本文讨论了万维网搜索算法中使用的两个重要的马氏链-maximal 不可约马氏链和minimal 不可约马氏链-收敛到平稳分布的收敛速度.结果表明,在阻尼因子α>1/2~(1/2)时,maximal 马氏链比minimal 马氏链的收敛速度快.本文也给出了minimal 马氏链k 步转移矩阵的表达式,及其平稳分布关于参数α的各阶导数和Maclaurin 级数展开.     

New Inexact Line Search Method for Unconstrained Optimization

We propose a new inexact line search rule and analyze the global convergence and convergence rate of related descent methods. The new line search rule is similar to the Armijo line-search rule and contains it as a special case. We can choose a larger stepsize in each line-search procedure and maintain the global convergence of related line-search methods. This idea can make us design new line-search methods in some wider sense. In some special cases, the new descent method can reduce to the Barzilai and Borewein method. Numerical results show that the new line-search methods are efficient for solving unconstrained optimization problems. The work was supported by NSF of China Grant 10171054, Postdoctoral Fund of China, and K. C. Wong Postdoctoral Fund of CAS Grant 6765700. The authors thank the anonymous referees for constructive comments and suggestions that greatly improved the paper.