Retailer Transshipment versus Central Depot Allocation for Supply Network Design

We consider a supply chain structure with shipments from an external warehouse directly to retailers and compare two enhancement options: costly transshipment among retailers after demand has been realized vs. cost‐free allocation to the retailers from the development of a centralized depot. Stochastic programming models are developed for both the transshipment and allocation structures. We study the impact of cost parameters and demand coefficient of variation on both system structures. Our results show an increasing convex relationship between average costs and demand coefficient of variation, and furthermore that this increase is more pronounced for the allocation structure. We employ simulation and nonlinear search techniques to computationally compare the cost performance of allocation and transshipment structures under a wide range of system parameters such as demand uncertainty and correlation; lead times from the external warehouse to retailers, from warehouse to central depot, and from depot to retailers; and transshipment, holding, and penalty costs. The transshipment approach is found to outperform allocation for a broad range of parameter inputs including many situations for which transshipment is not an economically sound decision for a single period. The insights provided enable the manager to choose whether to invest in reducing lead times or demand uncertainty and assist in the selection of investments across identical and nonidentical retailers.     

中国医药高技术产业及其市场资源配置的态势分析

本文在论述了医药产业作为高技术产业所具有的高技术、高投入、高风险以及相对垄断性等特征之后,分析了我国医药市场的营销概况和医药企业的发展现状,对比了我国与发达国家的医药企业在经济规模、市场营销和研究开发（R&D）投入方面所存在的差距 , 提出我国在加入WTO后医药产业对所面临挑战的对策。     

Uncertainty Analysis Based on Probability Bounds (P-Box) Approach in Probabilistic Safety Assessment

A wide range of uncertainties will be introduced inevitably during the process of performing a safety assessment of engineering systems. The impact of all these uncertainties must be addressed if the analysis is to serve as a tool in the decision-making process. Uncertainties present in the components (input parameters of model or basic events) of model output are propagated to quantify its impact in the final results. There are several methods available in the literature, namely, method of moments, discrete probability analysis, Monte Carlo simulation, fuzzy arithmetic, and Dempster-Shafer theory. All the methods are different in terms of characterizing at the component level and also in propagating to the system level. All these methods have different desirable and undesirable features, making them more or less useful in different situations. In the probabilistic framework, which is most widely used, probability distribution is used to characterize uncertainty. However, in situations in which one cannot specify (1) parameter values for input distributions, (2) precise probability distributions (shape), and (3) dependencies between input parameters, these methods have limitations and are found to be not effective. In order to address some of these limitations, the article presents uncertainty analysis in the context of level-1 probabilistic safety assessment (PSA) based on a probability bounds (PB) approach. PB analysis combines probability theory and interval arithmetic to produce probability boxes (p-boxes), structures that allow the comprehensive propagation through calculation in a rigorous way. A practical case study is also carried out with the developed code based on the PB approach and compared with the two-phase Monte Carlo simulation results.     

股权比例、过度担保与隐蔽掏空——来自我国上市公司对子公司担保的证据

本文借鉴LLSv的掏空模型,模型化推导了控股股东对上市公司的隐蔽掏空模式,建立了控股股东持股比例与掏空程度的分段函数关系.以2004年我国沪深A股上市公司对子公司担保的386起事件为样本,并把样本分为过度担保组和适度担保组,运用事件研究法进行实证检验.发现过度担保样本组累计超额收益率显著为负,而适度担保组的市场反应为正但不显著,由此推测过度担保的上市公司具有向控股股东输送利益的倾向.多元回归结果表明,对于第一大股东持股比例和国家持股比例的回归系数而言,60%均是其有效的临界点.当大股东持股比例低于60%时,大股东存在通过上市公司对子公司担保的方式掏空上市公司的现象,但回归系数不显著;当持股比例高于60%时,则产生了显著的利益协调效应.能有效地抑制掏空.通过分类变量和股权临界变量的多种组合,回归发现,国有性质的上市公司被控股股东掏空程度更高;与第一大股东持股比例变量相比.国家持股比例变量的股权临界值对CAR的影响更加显著.     

Demand and Production Management with Uniform Guaranteed Lead Time

Recently, innovation‐oriented firms have been competing along dimensions other than price, lead time being one such dimension. Increasingly, customers are favoring lead time guarantees as a means to hedge supply chain risks. For a make‐to‐order environment, we explicitly model the impact of a lead time guarantee on customer demands and production planning. We study how a firm can integrate demand and production decisions to optimize expected profits by quoting a uniform guaranteed maximum lead time to all customers. Our analysis highlights the increasing importance of lead time for customers, as well as the tradeoffs in achieving a proper balance between revenue and cost drivers associated with lead‐time guarantees. We show that the optimal lead time has a closed‐form solution with a newsvendor‐like structure. We prove comparative statics results for the change in optimal lead time with changes in capacity and cost parameters and illustrate the insights using numerical experimentation.     

Lessons from the flipside: How do acquirers learn from divestitures to complete acquisitions?

We examine whether experience from prior divestitures is associated with higher completion likelihood in subsequent acquisitions. We argue that through divestitures, firms could extract meaningful knowledge that is helpful for completing their acquisitions, such as a general understanding of acquisition deal-making stages, and the stories and aims of the target in an M&A transaction. In addition, viewing divestitures as the flipside of acquisitions, we conjecture that the selling firm can observe how the divested component of their business is acquired, and vicariously learn from these observations. We also investigate the relative importance of learning from divestiture vis-à-vis acquisition experience in determining acquisition deal completion. Finally, we contend that the effect of learning from divestitures on acquisition deal completion depends on acquisition experience and deal value. We find evidence supporting our conjectures in a sample of 2164 M&A transactions from the worldwide computer and printing industries between 1991 and 2010.     

Trends and challenges of developing human capital in India

With the population touching 1.2 billion, half of which is around 25 years of age, India is set to be among one of the top countries for human capital in the next two decades. While the country has been making rapid progress economically, human capital additions may remain marginal and may not keep up with the pace of economic growth unless accompanied by planned development of manpower. The Indian corporate sector was the first to start a dedicated HR function 30 years ago and the nation was the first to have a Ministry of HRD 20 years ago. Despite the early start, the last two to three decades have provided a mixed bag of experiences and expectations. Conservative HRD policies have not helped India's skill base to develop at the same rate as economic progress. An inadequate research base and the low quality of people entering the HR profession have prevented corporate HRD from becoming more productive than it is now. A lot of attention is directed towards recruitment, retention and compensation strategies rather than to talent multiplication. At the same time, innovative leaders and chief executive officers (CEOs) who have taken up the mantle of HR have made a mark. The future of human capital formation will be bright at the national level if the government makes more intense efforts to build human capital and becomes more innovative and expansive. At the corporate level, the credibility of HR is still suspect as it has not been able to match its steps with those of India's more innovative leaders. Preparation of a new breed of HR professionals combined with education of CEOs in what good HR can do may go a long way in helping Indian HR develop a strong footing.     

Comparative evaluation of genetic algorithms for job-shop scheduling

Many optimization problems from the industrial engineering world, in particular the manufacturing systems, are very complex in nature and quite hard to solve by conventional optimization techniques. There has been increasing interest in imitating living beings to solve such kinds of hard optimization problems. Simulating the natural evolutionary process of human beings results in stochastic optimization tech niques called evolutionary algorithms, which can often outperform conventional optimization methods when applied to difficult real-world problems. There are currently three main avenues of this research: genetic algorithms (GAs), evolutionary programming (EP) and evolution strategies (ESs). Among them, genetic algorithms are perhaps the most widely known types of evolutionary algorithms today. During the past years, several GAs for the job-shop scheduling problems have been proposed, each with different chromosome representation. In this paper, the different GAs are collected from the literature and an attempt has been made to evaluate them. The benchmark problems available in open literature are used for evaluation and the performance measure considered is makespan. The algorithms are coded in C+ +.     

India—the plan for steel

This paper discusses the use of planning techniques in assisting the attainment of sectoral plans for an economy. The paper examines the necessary threshold conditions which have to pertain in those sectors of the economy which consume, in this case steel, the product being planned through an exercise in demand forecasting. The second part of the paper broadly identifies the problems involved in attaining a specified steel production by utilizing techniques which would be of applicability to other sectors of the economy.     

Computational Comparison Studies of Quadratic Assignment Like Formulations for the In Silico Sequence Selection Problem in De Novo Protein Design

In this paper an O(n²) mathematical formulation for in silico sequence selection in de novo protein design proposed by Klepeis et al. (2003, 2004), in which the number of additional variables and linear constraints scales with the square of the number of binary variables, is compared to three O(n) formulations. It is found that the O(n²) formulation is superior to the O(n) formulations on most sequence search spaces. The superiority of the O(n²) formulation is due to the reformulation linearization techniques (RLTs), since the O(n²) formulation without RLTs is found to be computationally less efficient than the O(n) formulations. In addition, new algorithmic enhancing components of RLTs with inequality constraints, triangle inequalities, and Dead-End Elimination (DEE) type preprocessing are added to the O(n²) formulation. The current best O(n²) formulation, which is the original formulation from Klepeis et al. (2003, 2004) plus DEE type preprocessing, is proposed for in silico sequence search. For a test problem with a search space of 3.4×10⁴⁵ sequences, this new improved model is able to reduce the required CPU time by 67%.