Managing stochastic demand in an Inventory Routing Problem with transportation procurement

We study an Inventory Routing Problem in which the supplier has a limited production capacity and the stochastic demand of the retailers is satisfied with procurement of transportation services. The aim is to minimize the total expected cost over a planning horizon, given by the sum of the inventory cost at the supplier, the inventory cost at the retailers, the penalty cost for stock-out at the retailers and the transportation cost. First, we show that a policy based just on the average demand can have a total expected cost infinitely worse than the one obtained by taking into account the overall probability distribution of the demand in the decision process. Therefore, we introduce a stochastic dynamic programming formulation of the problem that allows us to find an optimal policy in small size instances. Finally, we design and implement a matheuristic approach, integrating a rollout algorithm and an optimal solution of mixed-integer linear programming models, which is able to solve realistic size problem instances. Computational results allow us to provide managerial insights concerning the management of stochastic demand.     

A Bayesian estimate of the concordance correlation coefficient with skewed data

Concordance correlation coefficient (CCC) is one of the most popular scaled indices used to evaluate agreement. Most commonly, it is used under the assumption that data is normally distributed. This assumption, however, does not apply to skewed data sets. While methods for the estimation of the CCC of skewed data sets have been introduced and studied, the Bayesian approach and its comparison with the previous methods has been lacking. In this study, we propose a Bayesian method for the estimation of the CCC of skewed data sets and compare it with the best method previously investigated. The proposed method has certain advantages. It tends to outperform the best method studied before when the variation of the data is mainly from the random subject effect instead of error. Furthermore, it allows for greater flexibility in application by enabling incorporation of missing data, confounding covariates, and replications, which was not considered previously. The superiority of this new approach is demonstrated using simulation as well as real‐life biomarker data sets used in an electroencephalography clinical study. The implementation of the Bayesian method is accessible through the Comprehensive R Archive Network. Copyright © 2015 John Wiley & Sons, Ltd.     

有机硅产业群的形成及其启示

本文首先对产业集群研究的历史进行了回顾 ,列举了产业集群促进区域经济发展的若干实例。通过对江西星火工业园有机硅产业集群案例的实证分析揭示了产业集群的形成因素 ,探讨了地方政府在集群规划中的作用 ,并提出了地方政府应该采取的若干对策     

C中易出错的若干问题

分析了C语言编程中存在的若干问题 ,并指明了原因 ,给出了解决方法。     

线性规划中的灵敏度分析

考虑了线性规划模型中的灵敏度分析，解决了当约束条件减少时，最优解如何变化的问题。     

Testing for Stochastic Dominance Efficiency

We consider consistent tests for stochastic dominance efficiency at any order of a given portfolio with respect to all possible portfolios constructed from a set of assets. We justify block bootstrap approaches to achieve valid inference in a time series setting. The test statistics are computed using linear and mixed integer programming formulations. Monte Carlo results show that the bootstrap procedure performs well in finite samples. The empirical application reveals that the Fama and French market portfolio is first and second-order stochastic dominance efficient, although it is mean–variance inefficient.     

An application of the integrated AHP-PGP model for performance measurement of supply chain management

The performance measurement problem has gained great attention in business and operations management literature. The first objective of this study is to determine the required performance measures and to develop a model for performance evaluation, based on these selected measures using analytical hierarchy process (AHP) methodology. The second objective is to explain how an integrated AHP-PGP (pre-emptive goal programming) model can be used in performance measurement while optimising the overall performance. An integration of AHP and multi-objective PGP is used to consider both quantitative and qualitative performance measures in optimising the overall performance of the system. The integrated model is presented with a real-world application using source data provided by a survey conducted in India. Findings demonstrate that the integrated AHP-PGP model can be useful to all supply chain industries in their day-to-day performance measurement decisions.     

Forecasting lead times with economic data

Procurement lead time estimates are critical factors in production planning; however, they are generally based on a buyer's experience. The Westinghouse Corporation developed a linear model which successfully predicted procurement lead times for hot and cold rolled steel. This paper discusses nine models which use utilization and inventory data to predict lead time planning factors for aluminum, magnesium, steel and titanium products. The Lockheed-Georgia Company, which produces aerospace products, provided data for this research. The functional form of the models is significant and suggests that a nonlinear transformation of utilization based upon simple queueing models is a significant predictor of lead time duration.     

Combinatoric classification of multivariate normal variates

Consider classifying an n × I observation vector as coming from one of two multivariate normal distributions which differ both in mean vectors and covariance matrices. A class of dis-crimination rules based upon n independent univariate discrim-inate functions is developed yielding exact misclassification probabilities when the population parameters are known. An efficient search of this class to select the procedure with minimum expected misclassification is made by employing an algorithm of the implicit enumeration type used in integer programming. The procedure is applied to the classification of male twins as either monozygotic or dizygotic.     

模糊条件下企业联盟和集团最优组建成本

将企业联盟和企业集团的组建过程分解为四个阶段,建立了其组建成本的数学模型;给出了企业联盟和企业集团在确定性情况下组建成本的模型,并将其扩展为适合于实际情况的组建成本模糊模型,即对该组建成本模型中与时间因素相关的各参数模糊化,再用模糊优化和非线性规划技术对该问题求解,得到不同组建阶段的最优时间分配,其方法对企业选择相应的发展战略有参考意义。