Efficient algorithms for flexible job shop scheduling with parallel machines

Job shop scheduling with a bank of machines in parallel is important from both theoretical and practical points of view. Herein we focus on the scheduling problem of minimizing the makespan in a flexible two-center job shop. The first center consists of one machine and the second has k parallel machines. An easy-to-perform approximate algorithm for minimizing the makespan with one-unit-time operations in the first center and k-unit-time operations in the second center is proposed. The algorithm has the absolute worst-case error bound of k − 1 , and thus for k = 1 it is optimal. Importantly, it runs in linear time and its error bound is independent of the number of jobs to be processed. Moreover, the algorithm can be modified to give an optimal schedule for k = 2 .     

A stochastic approximation algorithm to compute bid prices for joint capacity allocation and overbooking over an airline network

In this article, we develop a stochastic approximation algorithm to find good bid price policies for the joint capacity allocation and overbooking problem over an airline network. Our approach is based on visualizing the total expected profit as a function of the bid prices and searching for a good set of bid prices by using the stochastic gradients of the total expected profit function. We show that the total expected profit function that we use is differentiable with respect to the bid prices and derive a simple expression that can be used to compute its stochastic gradients. We show that the iterates of our stochastic approximation algorithm converge to a stationary point of the total expected profit function with probability 1. Our computational experiments indicate that the bid prices computed by our approach perform significantly better than those computed by standard benchmark strategies and the performance of our approach is relatively insensitive to the frequency with which we recompute the bid prices over the planning horizon. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Annealing adaptive search,cross‐entropy,and stochastic approximation in global optimization

The Annealing Adaptive Search (AAS) algorithm for global optimization searches the solution space by sampling from a sequence of Boltzmann distributions. For a class of optimization problems, it has been shown that the complexity of AAS increases at most linearly in the problem dimension. However, despite its desirable property, sampling from a Boltzmann distribution at each iteration of the algorithm remains a practical challenge. Prior work to address this issue has focused on embedding Markov chain‐based sampling techniques within the AAS framework. In this article, based on ideas from the recent Cross‐Entropy method and Model Reference Adaptive Search, we propose an algorithm, called Model‐based Annealing Random Search (MARS), that complements prior work by sampling solutions from a sequence of surrogate distributions that iteratively approximate the target Boltzmann distributions. We establish a novel connection between MARS and the well‐known Stochastic Approximation method. By exploiting this connection, we prove the global convergence of MARS and characterize its asymptotic convergence rate behavior. Our empirical results indicate promising performance of the algorithm in comparison with some of the existing methods. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

The knapsack problem with a minimum filling constraint

We study a knapsack problem with an additional minimum filling constraint, such that the total weight of selected items cannot be less than a given threshold. The problem has several applications in shipping, e‐commerce, and transportation service procurement. When the threshold equals the knapsack capacity, even finding a feasible solution to the problem is NP‐hard. Therefore, we consider the case when the ratio α of threshold to capacity is less than 1. For this case, we develop an approximation scheme that returns a feasible solution with a total profit not less than (1 ‐ ε) times the total profit of an optimal solution for any ε > 0, and with a running time polynomial in the number of items, 1/ε, and 1/(1‐α). © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

An economic lot‐sizing problem with perishable inventory and economies of scale costs: Approximation solutions and worst case analysis

The costs of many economic activities such as production, purchasing, distribution, and inventory exhibit economies of scale under which the average unit cost decreases as the total volume of the activity increases. In this paper, we consider an economic lot‐sizing problem with general economies of scale cost functions. Our model is applicable to both nonperishable and perishable products. For perishable products, the deterioration rate and inventory carrying cost in each period depend on the age of the inventory. Realizing that the problem is NP‐hard, we analyze the effectiveness of easily implementable policies. We show that the cost of the best Consecutive‐Cover‐Ordering (CCO) policy, which can be found in polynomial time, is guaranteed to be no more than (4 + 5)/7 ≈ 1.52 times the optimal cost. In addition, if the ordering cost function does not change from period to period, the cost of the best CCO policy is no more than 1.5 times the optimal cost. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

分布参数管道的一种改进的小分段数有限元模型

在分段数为１和２时，现有有限元模型的一阶谐振频率比一维分布参数模型的一阶谐振频率低许多，因而其适用的频率范围较低，计算精度较差。本文提出了一种改进的小分段数有限元模型，通过修正流体管道的并联导纳使分段数为１和２时的有限元模型的一阶谐振频率与一维分布参数模型一致，从而提高了其使用的频率范围和计算精度。利用单根管道阀门关闭的水击问题的仿真计算对改进模型的效果进行了验证。在分段数为２时，原有的有限元模型的计算结果与分布参数模型的计算结果相差较大，而改进后的模型的计算结果与分布参数模型的计算结果基本一致。     

正则化方法在拉普拉斯数值反演中的应用

本文用正则化方法给出了拉普拉斯变换的数值反演方法,并考虑了问题的不适定性,此方法在实际中有较强的应用价值.     

空空导弹攻击区处理的拟合——插值法

用最小二乘拟合与插值相结合的方法对空空导弹的发射包线进行处理,降低了拟合阶次。数字仿真结果表明,该方法不但在平常发射条件下极大地提高了拟合的精度和速度,而且在一些传统方法几乎无法使用的异常情况下仍有较好的结果。     

属性约简与近似集的关系研究

上近似和下近似是在粗集理论中最基本的概念,而粗集理论的主要思想又是在保持分类能力不变的前提下通过对属性的约简导出问题的决策和分类规则。本文讨论了属性的约简和相对约简与上近似和下近似的关系,并在此基础上提出了一种新的相对约简———保近似约简。     

Balancing perfectly periodic service schedules: An application from recycling and waste management

A national recycling and waste management company provides periodic services to its customers from over 160 service centers. The services are performed periodically in units of weeks over a planning horizon. The number of truck‐hours allocated to this effort is determined by the maximum weekly workload during the planning horizon. Therefore, minimizing the maximum weekly workload results in minimum operating expenses. The perfectly periodic service scheduling (PPSS) problem is defined based on the practices of the company. It is shown that the PPSS problem is strongly NP‐hard. Attempts to solve large instances by using an integer programming formulation are unsuccessful. Therefore, greedy BestFit heuristics with three different sorting schemes are designed and tested for six real‐world PPSS instances and 80 randomly generated data files. The heuristics provide effective solutions that are within 2% of optimality on average. When the best found BestFit schedules are compared with the existing schedules, it is shown that operational costs are reduced by 18% on average. © 2012 Wiley Periodicals, Inc. Naval Research Logistics 59: 160–171, 2012