Dynamic Pricing to Minimize Maximum Regret

We consider a dynamic pricing problem that involves selling a given inventory of a single product over a short, two‐period selling season. There is insufficient time to replenish inventory during this season, hence sales are made entirely from inventory. The demand for the product is a stochastic, nonincreasing function of price. We assume interval uncertainty for demand, that is, knowledge of upper and lower bounds but not a probability distribution, with no correlation between the two periods. We minimize the maximum total regret over the two periods that results from the pricing decisions. We consider a dynamic model where the decision maker chooses the price for each period contingent on the remaining inventory at the beginning of the period, and a static model where the decision maker chooses the prices for both periods at the beginning of the first period. Both models can be solved by a polynomial time algorithm that solves systems of linear inequalities. Our computational study demonstrates that the prices generated by both our models are insensitive to errors in estimating the demand intervals. Our dynamic model outperforms our static model and two classical approaches that do not use demand probability distributions, when evaluated by maximum regret, average relative regret, variability, and risk measures. Further, our dynamic model generates a total expected revenue which closely approximates that of a maximum expected revenue approach which requires demand probability distributions.     

中华女子学院教工运动会管理系统的设计与应用

中华女子学院教工运动会管理系统采用Visual Basic＋Access的体系结构，具有友好的操作界面，使用方便，能够完成从赛前报名、比赛编组、成绩统计、得分计算到成绩报告单的打印输出等运动会的各项信息管理和组织功能，大大提高了工作效率，并在实际应用中发挥了良好作用。     

On Integer Programming Approaches for Morphological Template Decomposition Problems in Computer Vision

In morphological image processing and analysis, a template or structuringelement is applied to an image. Often savings in computation time and abetter fit to the given computer architecture can be achieved by using thetechnique of template decomposition. Researchers have written a multitude ofpapers on finding such decompositions for special classes of templates.Justifying recent integer programming approaches to the morphologicaltemplate decomposition problem in its general form, this paper proves theNP-completeness of this problem.     

非线性混合整数规划的罚函数解法

主要对非线性混合整规划问题的求解进行探讨。利用罚函数把非线性混合整规划问题转化为等价的非线性规划问题,从而可通过求解一个无约束线性规划问题而得到原问题的最优解。     

Joint Initial Stocking and Transshipment—Asymptotics and Bounds

A common problem faced by many firms in their supply chains can be abstracted as follows. Periodically, or at the beginning of some selling season, the firm needs to distribute finished goods to a set of stocking locations, which, in turn, supply customer demands. Over the selling season, if and when there is a supply‐demand mismatch somewhere, a re‐distribution or transshipment will be needed. Hence, there are two decisions involved: the one‐time stocking decision at the beginning of the season and the supply/transshipment decision throughout the season. Applying a stochastic dynamic programming formulation to a two‐location model with compound Poisson demand processes, we identify the optimal supply/transshipment policy and show that the optimal initial stocking quantities can be obtained via maximizing a concave function whereas the contribution of transshipment is of order square‐root‐of T. Hence, in the context of high‐volume, fast‐moving products, the initial stocking quantity decision is a much more important contributor to the overall profit. The bounds also lead to a heuristic policy, which exhibits excellent performance in our numerical study; and we further prove both the bounds and the heuristic policy are asymptotically optimal when T approaches infinity. Extension to multiple locations is also discussed.     

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。