A new effective branch-and-bound algorithm to the high order MIMO detection problem

This paper develops a branch-and-bound method based on a new convex reformulation to solve the high order MIMO detection problem. First, we transform the original problem into a \(\{-1,1\}\) constrained quadratic programming problem with the smallest size. The size of the reformulated problem is smaller than those problems derived by some traditional transformation methods. Then, we propose a new convex reformulation which gets the maximized average objective value as the lower bound estimator in the branch-and-bound scheme. This estimator balances very well between effectiveness and computational cost. Thus, the branch-and-bound algorithm achieves a high total efficiency. Several simulations are used to compare the performances of our method and other benchmark methods. The results show that this proposed algorithm is very competitive for high accuracy and relatively good efficiency.     

Updating Parameters of the Chicken Processing Line Model

A mathematical model of chicken processing that quantitatively describes the transmission of Campylobacter on chicken carcasses from slaughter to chicken meat product has been developed in Nauta et al. (2005). This model was quantified with expert judgment. Recent availability of data allows updating parameters of the model to better describe processes observed in slaughterhouses. We propose Bayesian updating as a suitable technique to update expert judgment with microbiological data. Berrang and Dickens's data are used to demonstrate performance of this method in updating parameters of the chicken processing line model.     

Exact solution method to solve large scale integer quadratic multidimensional knapsack problems

In this paper we develop a branch-and-bound algorithm for solving a particular integer quadratic multi-knapsack problem. The problem we study is defined as the maximization of a concave separable quadratic objective function over a convex set of linear constraints and bounded integer variables. Our exact solution method is based on the computation of an upper bound and also includes pre-procedure techniques in order to reduce the problem size before starting the branch-and-bound process. We lead a numerical comparison between our method and three other existing algorithms. The approach we propose outperforms other procedures for large-scaled instances (up to 2000 variables and constraints). A extended abstract of this paper appeared in LNCS 4362, pp. 456–464, 2007.     

Optimal space coverage with white convex polygons

Assume that we are given a set of points some of which are black and the rest are white. The goal is to find a set of convex polygons with maximum total area that cover all white points and exclude all black points. We study the problem on three different settings (based on overlapping between different convex polygons): (1) In case convex polygons are permitted to have common area, we present a polynomial algorithm. (2) In case convex polygons are not allowed to have common area but are allowed to have common vertices, we prove the NP-hardness of the problem and propose an algorithm whose output is at least \(\left( \frac{OPT}{log(2n/OPT) + 2log(n)}\right) ^{1/4}\). (3) Finally, in case convex polygons are not allowed to have common area or common vertices, also we prove the NP-hardness of the problem and propose an algorithm whose output is at least \(\frac{3\sqrt{3}}{4.\pi }\left( \frac{OPT}{log(2n/OPT) + 2log(n)}\right) ^{1/4}\).     

Lean healthcare: review,classification and analysis of literature

This study presents a literature review of 107 papers on lean healthcare to evaluate its evolution by updating previous literature reviews and to propose a classification and analysis of the papers reviewed. The literature classification was performed based on six parameters: research method, country, healthcare area, implementation, lean tools and methods and results. From the analysis performed, this paper presents a quantitative analysis of the state of the art concerning lean healthcare and indicates current research trends, based on the stage of evolution of the area, that may guide further studies on the subject. An example is lean healthcare expansion to other countries, such as Brazil and the Netherlands. Another aspect is the application of lean healthcare in hospital as a whole, not limited to a specific setting. Finally, a few studies detail the lean implementation process and use infrequently applied tools, present the barriers and main critical factors found in the lean implementation.     

Equity‐Based Incentives and Supply Chain Buy‐Back Contracts*

We analyze the effect of equity‐based incentives in a supply chain with a downstream firm and an upstream supplier. By using the operational decision as a signal to influence external investors’ beliefs, the downstream firm's manager intends to maximize a convex combination of the interim share price and the terminal cash flows. We show that equity‐based incentives create a side effect. Specifically, with a universal buy‐back contract, the deadweight loss of signaling induced by equity‐based incentives could spread throughout the supply chain and cause chain‐wide damages. To mitigate such undesirable consequences, we propose a new mechanism to eliminate the inefficiency. We derive the optimal mechanism that maximizes the downstream firm's profits subject to the constraint that the supply chain efficiency is not undermined. In contrast to the full‐information benchmark, this mechanism gives positive surplus to the supplier. [Submitted: January 5, 2011. Revisions received: June 20, 2011; December 11, 2011. Accepted: December 22, 2011.]     

Fibonacci helps to evacuate from a convex region in a grid network

This study considers an evacuation problem where the evacuees try to escape to the boundary of an affected area, which is convex, and a grid network is embedded in the area. The boundary is unknown to the evacuees and we propose an online evacuation strategy based on the Fibonacci sequence. This strategy is proved to have a competitive ratio of 19.5, which is better than the best previously reported result of 21.     

一类序贯决策方法的提出:透镜与直觉模糊信息的视角

与大多数关注经典秘书问题的序贯决策研究不同,本文针对决策对象不变而其状态更新的一类序贯决策问题,提供决策方法。相较已有研究而言,本文从透镜与直觉模糊信息的视角提出这类序贯决策的一般方法,更加具有普适性,可应用于应急管理、风险投资等多个方向。该决策方法具有如下特点：1）借鉴透镜模型,通过当期可观测的线索,预测这类序贯决策问题的下一期状态;2）考虑到决策环境具有不确定性,采用直觉模糊数对决策者获取的信息线索进行刻画;3）为最大化模拟现实决策者,在前景理论框架下提出状态更新的序贯决策方法。本文提出的决策方法依据在于决策者对决策对象的预测"前景"值和预测绩效,基于此提出了具体的决策步骤,并辅以案例进行分析。通过对不同决策情形下决策结果的比较分析和对案例的稳健性检验,验证了该决策方法的适用性和有效性。     

Conditional Linear Combination Tests for Weakly Identified Models

We introduce the class of conditional linear combination tests, which reject null hypotheses concerning model parameters when a data‐dependent convex combination of two identification‐robust statistics is large. These tests control size under weak identification and have a number of optimality properties in a conditional problem. We show that the conditional likelihood ratio test of Moreira, 2003 is a conditional linear combination test in models with one endogenous regressor, and that the class of conditional linear combination tests is equivalent to a class of quasi‐conditional likelihood ratio tests. We suggest using minimax regret conditional linear combination tests and propose a computationally tractable class of tests that plug in an estimator for a nuisance parameter. These plug‐in tests perform well in simulation and have optimal power in many strongly identified models, thus allowing powerful identification‐robust inference in a wide range of linear and nonlinear models without sacrificing efficiency if identification is strong.     

How to Design Rating Schemes of Risk Matrices: A Sequential Updating Approach

Risk matrices have been widely used as a risk evaluation tool in many fields due to their simplicity and intuitive nature. Designing a rating scheme, i.e., determining the number of ratings used in a risk matrix and assigning different ratings to different cells, is an essential part of risk matrix construction. However, most of the related literature has focused on applying a risk matrix to various fields, instead of researching how to design risk matrices. Based on the analysis of several current rules, we propose a new approach, namely, the sequential updating approach (SUA), to design the rating scheme of a risk matrix in a reliable way. In this article, we propose three principles and a rating algorithm based on these principles. The three principles, namely, adjusted weak consistency, consistent internality, and continuous screening, characterize a good rating scheme. The resulting rating scheme has been proven to be unique. A global rating algorithm is then proposed to create the design that satisfies the three principles. We then explore the performance of the SUA. An illustrative application is first given to explain the feasibility of our approach. The sensitivity analysis shows that our method captures a resolution‐reliability tradeoff for decisionmakers in choosing an appropriate rating scheme for a risk matrix. Finally, we compare the designs based on the SUA and Cox's axioms, highlighting the advantages of the SUA.     

Testing Parameters in GMM Without Assuming that They Are Identified

We propose a generalized method of moments (GMM) Lagrange multiplier statistic, i.e., the K statistic, that uses a Jacobian estimator based on the continuous updating estimator that is asymptotically uncorrelated with the sample average of the moments. Its asymptotic χ² distribution therefore holds under a wider set of circumstances, like weak instruments, than the standard full rank case for the expected Jacobian under which the asymptotic χ² distributions of the traditional statistics are valid. The behavior of the K statistic can be spurious around inflection points and maxima of the objective function. This inadequacy is overcome by combining the K statistic with a statistic that tests the validity of the moment equations and by an extension of Moreira's (2003) conditional likelihood ratio statistic toward GMM. We conduct a power comparison to test for the risk aversion parameter in a stochastic discount factor model and construct its confidence set for observed consumption growth and asset return series.     

基于群体决策的水资源可持续利用评价的理想区间法

水资源可持续利用评价是可持续发展研究的核心技术,其实质是建立各评价指标与可持续利用等级之间的非线性关系,其中如何合理确定各评价指标的权重是目前研究的难点.提出了用加速遗传算法优化识别判断矩阵群体的Hadamard凸组合矩阵所对应的各评价指标的权重、并改进理想区间法的系统评价新方法(GD-IIM).GD-IIM直接由评价标准样本数据驱动,利用全部隶属度值信息计算可持续利用评价等级值,避免了应用最大隶属度原则进行判断所可能造成的失真,提高了评价精度.用GD-IIM评价黑龙江省14个区域水资源可持续利用的结果说明,GD-IIM直观简便、通用、精度高,可在实际系统评价中推广应用.     

一种基于时差的工期索赔分析方法

工期索赔问题是目前项目管理中的一类重要问题，针对这一问题出现了多种分析方法，但都不能被普遍接受，一个关键的难点在于多个工序发生延误时工序间相互作用的原理及其对总工期造成的影响尚未研究清楚，导致时差所有权不合理、延误责任分担不公平、索赔分析结果与实际情况不一致等问题。针对这些问题，本文基于时差理论提出了一种分析工期索赔的新方法。本文首先提出了工期延误中的"组合效应"，指多个工序延误时总工期实际延误总量经常不等于每个延误工序单独推迟总工期的分量之和的现象，以组合效应研究为入手点，本文利用CPM网络的时差特性分析了延误工序间相互影响的原理，进而揭示了多工序延误时"组合效应"的规律；然后在此基础上根据组合效应的影响因素确定组合效应在各延误工序中的分摊比例，进一步得出各工序在工期索赔中应承担的责任，由此提出了基于时差的工期索赔分析方法；最后通过一个项目算例将这种方法与目前常用方法进行了比较。由于组合效应清晰地反映了延误工序之间的相互影响及其对总工期影响的内在规律，因此本文提出的方法责任分摊更加公平合理，更加符合实际情况，且借助时差参数实现程序化使其免于频繁的网络更新更便于应用，能够有效弥补目前工期索赔分析方法的不足，为项目管理人员提供一种工期索赔分析的有力工具。     

An improved online evacuation strategy from a convex region on grid networks

This study considers an evacuation problem where an evacuee tries to escape to the boundary of a convex affected area on a grid network. The boundary is unknown to the evacuee and cannot be identified until reaching an arbitrary point of it. We propose an online evacuation strategy with a competitive ratio less than 17.5, which improves the previously best result of 19.5.     

摩擦市场上允许买空卖空的投资组合问题

证券交易市场上存在着诸如交易费用、税收等摩擦.投资者在交易过程中,不可避免地要受到市场摩擦的影响.本文以投资者所获取的最大投资效用为目标函数,建立了摩擦市场上最优投资组合问题的数学模型;同时对于之前解决此类问题的很多文章中“证券市场不允许买空卖空风险资产和借贷无风险资产”的假设条件做了扩展,得到一个摩擦市场上适用于“允许买空卖空或借贷”的证券投资组合的二次规划模型.     

A structured procedure for materials planning during production transfer

This paper explores materials planning procedures to ensure the materials’ availability during production transfers. The paper defines a production transfer as the preparation, physical transfer and start-up of relocated production. A structured procedure of materials planning during production transfer is developed based on theory, and then validated and refined based on the analysis of four case studies. The paper shows that there is a need for a structured procedure of materials planning during production transfers. It also explains the importance of activities that create prerequisites for the materials’ availability during production transfer, such as updating and adapting documentation, planning and control systems, and describes the activities that ensure the materials’ availability, such as preventive and corrective actions. A valid estimation of the time needed to reach a steady state and a combination of several preventive actions improves the ability to ensure that materials are available. The cases showed differences across company size, because large companies took more and farther-reaching preventive actions.     

Dependence Assessment in Human Reliability Analysis Using Evidence Theory and AHP

Dependence assessment among human errors in human reliability analysis (HRA) is an important issue. Many of the dependence assessment methods in HRA rely heavily on the expert's opinion, thus are subjective and may sometimes cause inconsistency. In this article, we propose a computational model based on the Dempster‐Shafer evidence theory (DSET) and the analytic hierarchy process (AHP) method to handle dependence in HRA. First, dependence influencing factors among human tasks are identified and the weights of the factors are determined by experts using the AHP method. Second, judgment on each factor is given by the analyst referring to anchors and linguistic labels. Third, the judgments are represented as basic belief assignments (BBAs) and are integrated into a fused BBA by weighted average combination in DSET. Finally, the CHEP is calculated based on the fused BBA. The proposed model can deal with ambiguity and the degree of confidence in the judgments, and is able to reduce the subjectivity and improve the consistency in the evaluation process.     

A Combined D.C. Optimization—Ellipsoidal Branch-and-Bound Algorithm for Solving Nonconvex Quadratic Programming Problems

In this paper we propose a new branch-and-bound algorithm by using an ellipsoidal partition for minimizing an indefinite quadratic function over a bounded polyhedral convex set which is not necessarily given explicitly by a system of linear inequalities and/or equalities. It is required that for this set there exists an efficient algorithm to verify whether a point is feasible, and to find a violated constraint if this point is not feasible. The algorithm is based upon the fact that the problem of minimizing an indefinite quadratic form over an ellipsoid can be efficiently solved by some available (polynomial and nonpolynomial time) algorithms. In particular, the d.c. (difference of convex functions) algorithm (DCA) with restarting procedure recently introduced by Pham Dinh Tao and L.T. Hoai An is applied to globally solving this problem. DCA is also used for locally solving the nonconvex quadratic program. It is restarted with current best feasible points in the branch-and-bound scheme, and improved them in its turn. The combined DCA-ellipsoidal branch-and-bound algorithm then enhances the convergence: it reduces considerably the upper bound and thereby a lot of ellipsoids can be eliminated from further consideration. Several numerical experiments are given.     

Scheduling Updates of Probabilistic Risk Assessments: The Arkansas Nuclear One-Unit 1 Experience

This paper presents the results of a study that identified how often a probabilistic risk assessment (PRA)should be updated to accommodate the changes that take place at nuclear power plants. Based on a 7-year analysis of design and procedural changes at one plant, we consider 5 years to be the maximum interval for updating PRAs. This conclusion is preliminary because it is based on the review of changes that occurred at a single plant, and it addresses only PRAs that involve a Level 1 analysis (i.e., a PRA including calculation of core damage frequency only). Nevertheless, this conclusion indicates that maintaining a useful PRA requires periodic updating efforts. However, the need for this periodic update stems only partly from the number of changes that can be expected to take place at nuclear power plants–changes that individually have only a moderate to minor impact on the PRA, but whose combined impact is substantial and necessitates a PRA update. Additionally, a comparison of two generations of PRAs performed about 5 years apart indicates that PRAs must be periodically updated to reflect the evolution of PRA methods. The most desirable updating interval depends on these two technical considerations as well as the cost of updating the PRA. (Cost considerations, however, were beyond the scope of this study.)     

Updating Uncertainty in an Integrated Risk Assessment: Conceptual Framework and Methods

Bayesian methods are presented for updating the uncertainty in the predictions of an integrated Environmental Health Risk Assessment (EHRA) model. The methods allow the estimation of posterior uncertainty distributions based on the observation of different model outputs along the chain of the linked assessment framework. Analytical equations are derived for the case of the multiplicative lognormal risk model where the sequential log outputs (log ambient concentration, log applied dose, log delivered dose, and log risk) are each normally distributed. Given observations of a log output made with a normally distributed measurement error, the posterior distributions of the log outputs remain normal, but with modified means and variances, and induced correlations between successive log outputs and log inputs. The analytical equations for forward and backward propagation of the updates are generally applicable to sums of normally distributed variables. The Bayesian Monte-Carlo (BMC) procedure is presented to provide an approximate, but more broadly applicable method for numerically updating uncertainty with concurrent backward and forward propagation. Illustrative examples, presented for the multiplicative lognormal model, demonstrate agreement between the analytical and BMC methods, and show how uncertainty updates can propagate through a linked EHRA. The Bayesian updating methods facilitate the pooling of knowledge encoded in predictive models with that transmitted by research outcomes (e.g., field measurements), and thereby support the practice of iterative risk assessment and value of information appraisals.