Resource Distribution in Multiple Attacks with Imperfect Detection of the Attack Outcome

This article extends the previous research of consecutive attacks strategy by assuming that an attacker observes the outcome of each attack imperfectly. With given probabilities it may wrongly identify a destroyed target as undestroyed, and wrongly identify an undestroyed target as destroyed. The outcome of each attack is determined by a contest success function that depends on the amount of resources allocated by the defender and the attacker to each attack. The article suggests a probabilistic model of the multiple attacks and analyzes how the target destruction probability and the attacker's relative resource expenditure are impacted by the two probabilities of incorrect observation, the attacker's and defender's resource ratio, the contest intensity, the number of attacks, and the resource distribution across attacks. We analyze how the attacker chooses the number of attacks, the attack stopping rule, and the optimal resource distribution across attacks to maximize its utility.     

A new metamodel-based method for solving semi-expensive simulation optimization problems

In this article, a new algorithm for rather expensive simulation problems is presented, which consists of two phases. In the first phase, as a model-based algorithm, the simulation output is used directly in the optimization stage. In the second phase, the simulation model is replaced by a valid metamodel. In addition, a new optimization algorithm is presented. To evaluate the performance of the proposed algorithm, it is applied to the (s,S) inventory problem as well as to five test functions. Numerical results show that the proposed algorithm leads to better solutions with less computational time than the corresponding metamodel-based algorithm.     

Building up Resilience in a Pharmaceutical Supply Chain through Inventory,Dual Sourcing and Agility Capacity

This paper is inspired by a risk management problem faced by a leading pharmaceutical company. Key operational risk mitigation measures include Risk Mitigation Inventory (RMI), Dual Sourcing and Agility Capacity. We study the relationship between these three measures by modeling the drug manufacturing firm that is exposed to supply chain disruption risk. The firm determines optimal RMI levels for assumed Dual Sourcing and Agility Capacity. We quantify the decrease in RMI levels in the presence of Dual Sourcing and Agility Capacity. Furthermore, using an example, we analyze RMI, Dual Sourcing and Agility Capacity decisions jointly. It turns out that RMI and Agility Capacity can be substitutes as long as no Dual Source is available. Once the Dual Source is available, Agility Capacity and Dual Sourcing appear to be substitutes. We further show that for long disruption times, the optimal Dual Source production rate may decrease in the disruption time. Within our modeling framework, we introduce an operational metric that quantifies Supply Chain Resilience. Supply chain disruptions can have a severe business impact and need to be managed appropriately.     

Multinomial Selection in the Presence of Infinite Alternatives

We propose a new procedure for the multinomial selection problem to solve a real problem of any modern Air Force: the elaboration of better air-to-air tactics for Beyond Visual Range air-to-air combat that maximize its aircraft survival probability H(θ, ω), as well as enemy aircraft downing probability G(θ, ω). In this study, using a low-resolution simulator with generic parameters for the aircraft and missiles, we could increase an average success rate of 16.69% and 16.23% for H(θ, ω) and G(θ, ω), respectively, to an average success rate of 76.85% and 79.30%. We can assure with low probability of being wrong that the selected tactic has greater probability of yielding greater success rates in both H(θ, ω) and G(θ, ω) than any simulated tactic.     

中国决策情境下的多源流模型及其优化研究

作为当今公共政策研究领域最炙手可热的理论模型之一,约翰·金登的多源流分析理论业已得到国内外学者的广泛应用与认可。在对该模型的相关内容进行简单铺陈后,通过辨析国内外不同的政策主体参与逻辑,进一步厘清了我国公共决策的独有特征。研究重点是在整合提炼西方既有修正思路的基础上,沿袭模型的模糊性假设,结合我国具体实践案例,对多源流模型进行优化与补充。研究表明,中国决策情境下的多源流模型优化主要有提高源流间的融合性、政治源流的核心化、焦点事件的独立化以及特殊情况下的临时决策等四种路径,四种优化与补充路径的有机结合将为进一步提升模型适用性提供帮助。     

采矿工程专业人才培养方案优化与实践

随着全球经济一体化的发展,我国煤炭行业得到了快速发展,煤炭工业需要大量的高素质复合型采矿工程专业毕业生,因此,修订和完善采矿专业人才培养方案具有重要意义。河南理工大学采矿工程专业为了不断提高人才培养质量,对采矿工程专业人才培养方案进行了优化,2010年10月通过全国工程教育专家委员会的认证工作。新的人才培养方案已经得到实施,效果明显。     

大学生专业操作能力优化教学模式的构建——以人力资源管理专业课程为例

高校人才培养与企业实际需求存在匹配度较低的问题,因而有必要构建新的大学生专业操作能力优化教学模式.新的教学模式涵盖目标描述、目标支撑、流程管理、经验教训、工具与表单五个方面.它可以让学生的学习从抽象到具体,从理论到实践,从宏观到微观,从被动接受到主动思考,达到对该模块更深刻的学习、掌握与运用,从而更加适应企业与社会的需求.     

地方高校大学英语课程结构的优化

本文阐述了地方高校大学英语教学长期延用的课程结构体系中存在的一些主要问题,并针对这些问题,提出了建立新型课程结构应该考虑的五个方面。     

基本药物遴选方法的宏观经济效果比较

基本药物遴选的结果与百姓医保报销利益密切相关。我国医改新政关于基本药物遴选的方法学习西方国家，又有所发展。应用比较经济学原理，使用中国统计年鉴数据，测量了世界卫生组织的专家委员会法、中国现行的专家库两轮评审法、循证评价法、药物经济法等四种方法遴选所得基本药物方案的宏观卫生经济数据，得出在医疗卫生体制改革的社会医学领域可以借鉴自然科学统筹优化的组合方法，以获得综合更优的社会期望。     

Bounding Optimal Expected Revenues for Assortment Optimization under Mixtures of Multinomial Logits

We consider assortment problems under a mixture of multinomial logit models. There is a fixed revenue associated with each product. There are multiple customer types. Customers of different types choose according to different multinomial logit models whose parameters depend on the type of the customer. The goal is to find a set of products to offer so as to maximize the expected revenue obtained over all customer types. This assortment problem under the multinomial logit model with multiple customer types is NP‐complete. Although there are heuristics to find good assortments, it is difficult to verify the optimality gap of the heuristics. In this study, motivated by the difficulty of finding optimal solutions and verifying the optimality gap of heuristics, we develop an approach to construct an upper bound on the optimal expected revenue. Our approach can quickly provide upper bounds and these upper bounds can be quite tight. In our computational experiments, over a large set of randomly generated problem instances, the upper bounds provided by our approach deviate from the optimal expected revenues by 0.15% on average and by less than one percent in the worst case. By using our upper bounds, we are able to verify the optimality gaps of a greedy heuristic accurately, even when optimal solutions are not available.