Optimal clustering: A model and method

Classifying items into distinct groupings is fundamental in scientific inquiry. The objective of cluster analysis is to assign n objects to up to K mutually exclusive groups while minimizing some measure of dissimilarity among the items. Few mathematical programming approaches have been applied to these problems. Most clustering methods to date only consider lowering the amount of interaction between each observation and the group mean or median. Clustering used in information systems development to determine groupings of modules requires a model that will account for the total group interaction. We formulate a mixed-integer programming model for optimal clustering based upon scaled distance measures to account for this total group interaction. We discuss an efficient, implicit enumeration algorithm along with some implementation issues, a method for computing tight bounds for each node in the solution tree, and a small example. A computational example problem, taken from the computer-assisted process organization (CAPO) literature, is presented. Detailed computational results indicate that the method is effective for solving this type of cluster analysis problem.     

Estimation for dirichlet mixed models

Dirichlet mixed models find wide application. Estimation is usually achieved through the method of moments. Here we present an iterative hybrid algorithm for obtaining the maximum likelihood estimate employing both modified Newton-Raphson and E-M methods. This successful MLE algorithm enables calculation of a jack-knife MLE. Simulation comparison of the three estimates is provided. The MLE substantially improves upon the moments estimator particularly with increasing dimension. The jack-knife MLE in turn offers dramatic improvement over the MLE.     

舰机时间协同对潜检查搜索样式

舰机一体化反潜是反潜作战最有效的形式,而舰机协同反潜搜索是其最重要的组成部分.从实际应用的角度出发,研究了舰机时间协同检查搜索样式,并给出了基于该样式的实际应用例子.     

CON/SLK due date assignment and scheduling on a single machine with two agents

We consider scheduling problems involving two agents (agents A and B), each having a set of jobs that compete for the use of a common machine to process their respective jobs. The due dates of the A‐jobs are decision variables, which are determined by using the common (CON) or slack (SLK) due date assignment methods. Each agent wants to minimize a certain performance criterion depending on the completion times of its jobs only. Under each due date assignment method, the criterion of agent A is always the same, namely an integrated criterion consisting of the due date assignment cost and the weighted number of tardy jobs. Several different criteria are considered for agent B, including the maxima of regular functions (associated with each job), the total (weighted) completion time, and the weighted number of tardy jobs. The overall objective is to minimize the performance criterion of agent A, while keeping the objective value of agent B no greater than a given limit. We analyze the computational complexity, and devise polynomial or pseudo‐polynomial dynamic programming algorithms for the considered problems. We also convert, if viable, any of the devised pseudopolynomial dynamic programming algorithms into a fully polynomial‐time approximation scheme. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 416–429, 2016     

Confidence coefficient of approximate two-sided confidence intervals for the binomial probability

The confidence coefficient of a two-sided confidence interval for the binomial parameter p is the infimum of the coverage probability of the interval as p ranges between 0 and 1. The confidence coefficients for five different approximate confidence intervals are computed and compared to the confidence coefficient for the two-sided Clopper-Pearson confidence interval. Pratt's approximation method [10] yields virtually the same confidence coefficients as the Clopper-Pearson interval, and is easily computed without resorting to interative methods.     

Algorithms and heuristics for variable-yield lot sizing

We consider the multiperiod lot-sizing problem in which the production yield (the proportion of usable goods) is variable according to a known probability distribution. We review two economic order quantity (EOQ) models for the stationary demand continuous-time problem and derive an EOQ model when the production yield follows a binomial distribution and backlogging of demand is permitted. A dynamic programming algorithm for an arbitrary sequence of demand requirements is presented. Heuristics based on both the EOQ model and appropriate modification of the underlying perfect-yield lot-sizing policies are discussed, and extensive computational evaluation of these heuristics is presented. Two of these heuristics are then modified to include the notion of supply safety stock. The modified heuristics consistently produce near-optimal lot-sizing policies for problems with stationary and time-varying demands.     

The continuous multiple-modular design problem

In this article we extend our previous work on the continuous single-module design problem to the multiple-module case. It is assumed that there is a fixed cost associated with each additional module used. The Kuhn–Tucker conditions characterize local optima among which there is a global optimum. Modules are associated with partitions and a special class, guillotine partitions, are characterized. Branch-and-bound, partial enumeration, and heuristic procedures for finding optimum or good guillotine partitions are discussed and illustrated with examples.     

Approximate probability distributions for the extreme spread

The extreme spread, or greatest distance between all pairs of impact points on a target, is often used as a rapid measure of dispersion or precision of shot groups on a target. It is therefore desirable to know its statistical properties. Since the exact theoretical distribution has not yet been worked out, this paper examines the accuracy of several approximations which are checked against large sample monte carlo values. We find in particular that for the sample sizes considered the extreme spread can be approximated well by a Chi variate.