An iterated local search with multiple perturbation operators and time varying perturbation strength for the aircraft landing problem

Landing aircraft safely is an important operation that air traffic controllers have to deal with on a daily basis. For each arriving aircraft a runway and a landing time must be allocated. If these allocations can be done in an efficient way, it could give the airport a competitive advantage. The Aircraft Landing Problem (ALP) aims to minimize the deviation from a preferred target time of each aircraft. It is an NP-hard problem, meaning that we may have to resort to heuristic methods as exact methods may not be suitable, especially as the problem size increases. This paper proposes an iterated local search (ILS) algorithm for the ALP. ILS is a single solution based search methodology that successively invokes a local search procedure to find a local optimum solution. A perturbation operator is used to modify the current solution in order to escape from the local optimum and to provide a new solution for the local search procedure. As different problems and/or instances have different characteristics, the success of the ILS is highly dependent on the local search, the perturbation operator(s) and the perturbation strength. To address these issues, we utilize four perturbation operators and a time varying perturbation strength which changes as the algorithm progresses. A variable neighborhood descent algorithm is used as our local search. The proposed ILS generates high quality solutions for the ALP benchmark instances taken from the scientific literature, demonstrating its efficiency in terms of both solution quality and computational time. Moreover, the proposed ILS produces new best results for some instances.     

Inverse Combinatorial Optimization: A Survey on Problems, Methods, and Results

Given a (combinatorial) optimization problem and a feasible solution to it, the corresponding inverse optimization problem is to find a minimal adjustment of the cost function such that the given solution becomes optimum.Several such problems have been studied in the last twelve years. After formalizing the notion of an inverse problem and its variants, we present various methods for solving them. Then we discuss the problems considered in the literature and the results that have been obtained. Finally, we formulate some open problems.     

An approach to stochastic programming for medium term planning

A number of methods of obtaining the distribution of the optimum of the ‘wait and see’ stochastic programming model have been proposed, but computational experience for these is currently limited to the solution of small problems. The purpose of this paper is to discuss the role of the ‘wait and see’ model in planning, and to propose a method of analysis based on the minimax and maximax decision criteria. The approach requires the solution of a special class of non-linear programming problems. Computational results to date suggest that it will be possible to analyse practically sized problems in this way.     

A LOWER BOUND FOR THE SINGLE-LEVEL DYNAMIC HORIZON LOT-SIZING PROBLEM

Conventional production planning methods assume the existence of a medium- or longrange demand horizon. However, demand usually is known over a much shorter range; scheduling decisions must be made within this “decision window,” which rolls forward in time. This paper presents a new lower bound for lot-sizing heuristics in a rolling-horizon framework and compares it to the well-known Wagner-Whitin bound. The new bound indicates heuristic schedules that have costs close to the optimum. Rolling-horizon schedule costs are compared to corresponding static-horizon schedule costs (assuming the whole horizon is known in advance), using the ratio of decision-window size to the natural order cycle as a parameter. For values below unity, the rolling-horizon policy is significantly more costly. For values above one, the two policies have similar costs and actually converge as the parameter value increases.     

Risk/Benefit Assessments of Human Diseases: Optimum Dose for Intervention

A simple procedure is proposed in order to quantify the tradeoff between a loss suffered from an illness due to exposure to a microbial pathogen and a loss due to a toxic effect, perhaps a different illness, induced by a disinfectant employed to reduce the microbial exposure. Estimates of these two types of risk as a function of disinfectant dose and their associated relative losses provide information for the estimation of the optimum dose of disinfectant that minimizes the total expected loss. The estimates of the optimum dose and expected relative total loss were similar regardless of whether the beta-Poisson, log-logistic, or extreme value function was used to model the risk of illness due to exposure to a microbial pathogen. This is because the optimum dose of the disinfectant and resultant expected minimum loss depend upon the estimated slope (first derivative) of the models at low levels of risk, which appear to be similar for these three models at low levels of risk. Similarly, the choice among these three models does not appear critical for estimating the slope at low levels of risk for the toxic effect induced by the use of a disinfectant. For the proposed procedure to estimate the optimum disinfectant dose, it is not necessary to have absolute values for the losses due to microbial-induced or disinfectant-induced illness, but only relative losses are required. All aspects of the problem are amenable to sensitivity analyses. The issue of risk/benefit tradeoffs, more appropriately called risk/risk tradeoffs, does not appear to be an insurmountable problem.     

Note on “many-sided shadow prices”

It has been previously shown, with the aid of a simple example, that linear programmes may have two different non-negative marginal values at the optimum if the solution is degenerate. It is demonstrated in this note that other marginal values exist which may take negative values.     

基于不同保鲜方式的生鲜品定价订货决策

本文考虑两种不同保鲜方式下生鲜品的不同变质率以及相互需求替代,构建了联合定价和订货模型。研究无库存约束、单一库存约束和双库存约束三种情形下零售商对不同保鲜方式生鲜品的定价和订货决策。探讨市场容量、生鲜变质率、需求替代率以及最大订货量对最优决策的影响。研究显示：市场容量越大、需求替代率越高,生鲜品零售商的获利空间越大;提高保鲜投入和保鲜努力,降低高(低)变质率保鲜方式生鲜品的变质率可以提高两种保鲜方式生鲜品的销售价格和高(低)变质率保鲜方式生鲜品的销售量;高(低)变质率保鲜方式生鲜品单一库存约束会提高该生鲜品的最优价格和低(高)变质率保鲜方式生鲜品的最优订货量;双库存约束时,两种保鲜方式生鲜品的最优价格都会提高,最优订货量为最大订货量。     

FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms

Various methods and algorithms have been developed for multiclass classification problems in recent years. How to select an effective algorithm for a multiclass classification task is an important yet difficult issue. Since the multiclass algorithm selection normally involves more than one criterion, such as accuracy and computation time, the selection process can be modeled as a multiple criteria decision making (MCDM) problem. While the evaluations of algorithms provided by different MCDM methods are in agreement sometimes, there are situations where MCDM methods generate very different results. To resolve this disagreement and help decision makers pick the most suitable classifier(s), this paper proposes a fusion approach to produce a weighted compatible MCDM ranking of multiclass classification algorithms. Several multiclass datasets from different domains are used in the experimental study to test the proposed fusion approach. The results prove that MCDM methods are useful tools for evaluating multiclass classification algorithms and the fusion approach is capable of identifying a compromised solution when different MCDM methods generate conflicting rankings.     

在线逆向拍卖期权和单方支付契约下的供应链协调机制分析

根据报童模型和信息揭示原理建立了在线逆向拍卖期权和单方支付契约下生产商、销售商和系统的利润模型。通过对模型的优化得到两种电子佣金制度下的最优期权订购量。比较最优期权订购量和系统最优生产容量,分析了不同电子佣金制度及期权和单方支付契约类型下供应链协调特征。得到以下结论:对于具有单季节随机市场需求特征产品的期权和单方支付契约拍卖中,若生产商执行单方支付,系统不能被协调。若销售商执行单方支付,系统的协凋与电子佣金的制度有关;在赢得契约生产商支付电子佣金的制度下,最利于系统的协调和利润的分配。     

On statistical bounds of heuristic solutions to location problems

Combinatorial optimization problems such as locating facilities frequently rely on heuristics to minimize the objective function. The optimum is often sought iteratively; a criterion is therefore necessary to be able to decide when the procedure attains such an optimum. Pre-setting the number of iterations is dominant in OR applications, however, the fact that the quality of the solution cannot be ascertained by pre-setting the number of iterations makes it less preferable. A small and, almost dormant, branch of the literature suggests usage of statistical principles to estimate the minimum and its bounds as a tool to decide upon the stopping criteria and also to evaluate the quality of the solution. In the current work we have examined the functioning of statistical bounds obtained from four different estimators using simulated annealing. P-median test problems taken from Beasley’s OR-library were used for the sake of testing. Our findings show that the Weibull estimator and 2nd order Jackknife estimators are preferable and the requirement of sample size to be about 10. It should be noted that reliable statistical bounds are found to depend critically on a sample of heuristic solutions of high quality; we have therefore provided a simple statistic for checking the quality. The work finally concludes with an illustration of applying statistical bounds to the problem of locating 70 post distribution centers in a region in Sweden.     

Performance-Net: A Decision Support System for Reconfiguring a Bank's Branch Network

This paper presents a Decision Support System (DSS) that enables the management of a retail bank to evaluate and reconfigure its branch network. The DSS uses computational methods and knowledge that arises from information about the bank's operational and fixed costs, as well as demographic characteristics from the geographical area where branches are located. The DSS that we call Performance-Net estimates the performance of a branch network and determines the optimum number of branches and the optimum mix of services that each one should provide in order to maximize the bank's revenue- generating measures. Its computational engine is based on a linear programming optimization model and its implementation is developed using the standard MS Excel program. Performance-Net provides efficient solutions, is particularly user-friendly and can reach excellent answers for a wide variety of “what if” parametric scenarios.     

信息不完全确定的区域产品模糊区间聚类方法

区域产品分类与选择是区域经济发展中最重要和最基础的工作.在产品选择与分类时,需要确定指标权系数和分类阈值等参数,这在实际应用中是比较困难的.针对这种情况,提出了一种信息不完全确定的区域产品模糊区间聚类方法.该方法构建了指标权系数信息不完全确定的最优模糊区间聚类模型,利用遗传算法和改进的FCM算法联合求解所得优化模型,得到指标权系数、最优聚类中心和最优划分,进而确定各产品所属类别.最后将该方法应用于某区域的产品分类和主导产品的确定中,实例计算说明该方法的可行性和有效性.     

Stepping Out of Your Own Shadow: A Didactic Example of How Facing Uncertainty Can Improve Decision-Making

Advocates of quantitative uncertainty analysis (QUA) have invested substantial effort in explaining why uncertainty is a crucial aspect of risk and yet have devoted much less effort to explaining how QUA can improve the risk manager's performance. This paper develops a teaching example, using a personal decision problem with subtle parallels to societal risk management, to show how choices made with increasing appreciation of uncertainty are superior ones. In the hypothetical, five analysts explain the same uncertain prospect (whether to invest in a volatile stock issue), with increasing attention to the nuances of uncertainty. The path through these different perspectives on the decision demonstrates four general points applicable to environmental risk management: (1) Various point estimates with equal claim to being "best estimates" can differ markedly from each other and lead to diametrically different choices; (2) "conservatism" has both relative and absolute meanings, with different implications for decision-making; (3) both inattention to and fixation on "outliers" in the uncertainty distribution can lead the manager astray; and (4) the best QUA is one that helps discriminate among real options, that points to optimum pathways toward new information, and that spurs on the iterative search for new decision options that may outperform any of the initial ones offered.     

自然资源耗竭与经济可持续增长：基于四部门内生增长模型分析

本文在已有研究文献的基础上发展了一个基于水平创新的四部门内生增长模型,探讨了人口增长、自然资源耗竭、内生技术进步与长期经济增长的关系.通过对模型的社会最优均衡分析和竞争性市场均衡分析,分别求出了均衡解、均衡解存在性条件以及在人口增长和资源约束下经济可持续增长所需的条件,并证明了分权经济条件下的市场均衡解往往是非帕累托最优的,进一步分析了导致市场效率损失的主要原因及其政策涵义.     

RATIONALIZATION OF MULTINATION,MULTIPLANT OPERATIONS

This study formulates a linear programming model of a multination, multiplant operation composed of three production stages and a distribution stage. The objective of the model is to maximize the weighted sum of net income produced by all operational units located in different countries. Major cost items include production, distribution, transportation, income tax, and import duty. The correct assessment of import duties on a lot of goods is made possible by identifying the entire sequence of countries in which the lot is processed. A solution to the linear program determines an optimum size of the lot and an optimum sequence of countries passed through by this lot.     

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.     

Comparison of Sensitivity Analysis Methods Based on Applications to a Food Safety Risk Assessment Model

Sensitivity analysis (SA) methods are a valuable tool for identifying critical control points (CCPs), which is one of the important steps in the hazard analysis and CCP approach that is used to ensure safe food. There are many SA methods used across various disciplines. Furthermore, food safety process risk models pose challenges because they often are highly nonlinear, contain thresholds, and have discrete inputs. Therefore, it is useful to compare and evaluate SA methods based upon applications to an example food safety risk model. Ten SA methods were applied to a draft Vibrio parahaemolyticus (Vp) risk assessment model developed by the Food and Drug Administration. The model was modified so that all inputs were independent. Rankings of key inputs from different methods were compared. Inputs such as water temperature, number of oysters per meal, and the distributional assumption for the unrefrigerated time were the most important inputs, whereas time on water, fraction of pathogenic Vp, and the distributional assumption for the weight of oysters were the least important inputs. Most of the methods gave a similar ranking of key inputs even though the methods differed in terms of being graphical, mathematical, or statistical, accounting for individual effects or joint effect of inputs, and being model dependent or model independent. A key recommendation is that methods be further compared by application on different and more complex food safety models. Model independent methods, such as ANOVA, mutual information index, and scatter plots, are expected to be more robust than others evaluated.     

Identification and Review of Sensitivity Analysis Methods

Identification and qualitative comparison of sensitivity analysis methods that have been used across various disciplines, and that merit consideration for application to food-safety risk assessment models, are presented in this article. Sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and verifying and validating a model. Ten sensitivity analysis methods, including four mathematical methods, five statistical methods, and one graphical method, are identified. The selected methods are compared on the basis of their applicability to different types of models, computational issues such as initial data requirement and complexity of their application, representation of the sensitivity, and the specific uses of these methods. Applications of these methods are illustrated with examples from various fields. No one method is clearly best for food-safety risk models. In general, use of two or more methods, preferably with dissimilar theoretical foundations, may be needed to increase confidence in the ranking of key inputs.     

Optimal stimulation level theory, exploratory consumer behaviour and product adoption: an analysis of underlying structures across product categories

Research of the last decades focused on answering several questions in view of optimum stimulation theory. Which variables do influence general explorative tendencies such as risk taking, variety seeking, or curiosity-motivated behaviour and how do the general explorative tendencies effect the willingness to choose new or familiar products on a repurchase occasion? The approach of this research is the attempt to analyse a wide range of variables, which have successfully been tested to influence innovative behaviour, within one simultaneous model. This research is applied across different product-categories. It is confirming the nomological validity of OSL-theory including some contextual variables by attesting a principal consistency between all (six) models that have been established throughout the range of several examined product categories. The parameters between inherent factors do not contradict when comparing the single models with each other, parameter values do only differ slightly according to category-specific peculiarities. As a consequence of these results, an overall fairly good picture is drawn of what is behind the keenness for innovations of early adopters in general (regardless of the product category).