基于蒙特卡罗法的平面度测量不确定度评定

形位误差的测量不确定度评定是目前测量领域研究的热点;但由于其测量的复杂性和测量结果评定的多样性,导致在实际测量结果中形位误差测量的不确定度评定成了难题;为此,根据形状误差评定准则,选取最小二乘法建立数学模型,确定形状误差数学模型中各参数值的传递系数和单点不确定度,并分析具体的测量方法和测量过程中的不确定度来源,根据传统的GUM法对其进行不确定度评定;然后采用蒙特卡罗伪随机数的方法来模拟实际测量数据,从而得到平面度误差的不确定度;通过设置实验对比,验证了蒙特卡罗法评定平面度不确定度的可靠性和准确性;该方法不需要求出数学模型中的传递系数,利用MATLAB软件很容易实现,为平面度误差测量结果不确定度评定提供了更加简便的方法,值得推广和应用。     

Microbiological proficiency testing: a personal perspective

The author considers the fundamental differences between analyses in microbiology and those in chemistry and physics, deducing special issues for microbiological proficiency testing. He concludes that the variability and uncertainty implicit in microbiological analysis requires a broader range of proficiency scheme providers providing a broader range of services than in chemistry and physics. Received: 10 November 2000 Accepted: 3 December 2000     

Application of possibility theory to investment decisions

Carlson and Fuller (2001, Fuzzy Sets and Systems, 122, 315–326) introduced the concept of possibilistic mean, variance and covariance of fuzzy numbers. In this paper, we extend some of these results to a nonlinear type of fuzzy numbers called adaptive fuzzy numbers (see Bodjanova (2005, Information Science, 172, 73–89) for detail). We then discuss the application of these results to decision making problems in which the parameters may involve uncertainty and vagueness. As an application, we develop expression for fuzzy net present value (FNPV) of future cash flows involving adaptive fuzzy numbers by using their possibilistic moments. An illustrative numerical example is given to illustrate the results.     

Simulated annealing algorithm for the robust spanning tree problem

This paper addresses the robust spanning tree problem with interval data, i.e. the case of classical minimum spanning tree problem when edge weights are not fixed but take their values from some intervals associated with edges. The problem consists of finding a spanning tree that minimizes so-called robust deviation, i.e. deviation from an optimal solution under the worst case realization of interval weights. As it was proven in Kouvelis and Yu (Robust Discrete Optimization and Its Applications, Kluwer Academic, Norwell, 1997), the problem is NP-hard, therefore it is of great interest to tackle it with some metaheuristic approach, namely simulated annealing, in order to calculate an approximate solution for large scale instances efficiently. We describe theoretical aspects and present the results of computational experiments. To the best of our knowledge, this is the first attempt to develop a metaheuristic approach for solving the robust spanning tree problem.     

Simulated annealing in the presence of noise

In many practical optimization problems, evaluation of a solution is subject to noise, e.g., due to stochastic simulations or measuring errors. Therefore, heuristics are needed that are capable of handling such noise. This paper first reviews the state-of-the-art in applying simulated annealing to noisy optimization problems. Then, two new algorithmic variants are proposed: an improved version of stochastic annealing that allows for arbitrary annealing schedules, and a new approach called simulated annealing in noisy environments (SANE). The latter integrates ideas from statistical sequential selection in order to reduce the number of samples required for making an acceptance decision with sufficient statistical confidence. Finally, SANE is shown to significantly outperform other state-of-the-art simulated annealing techniques on a stochastic travelling salesperson problem.     

Coordinated control of distribution supply chains in the presence of fuzzy customer demand

This paper considers a single product inventory control in a Distribution Supply Chain (DSC). The DSC operates in the presence of uncertainty in customer demands. The demands are described by imprecise linguistic expressions that are modelled by discrete fuzzy sets. Inventories at each facility within the DSC are replenished by applying periodic review policies with optimal order up-to-quantities. Fuzzy customer demands imply fuzziness in inventory positions at the end of review intervals and in incurred relevant costs per unit time interval. The determination of the minimum of defuzzified total cost of the DSC is a complex problem which is solved by applying decomposition; the original problem is decomposed into a number of simpler independent optimisation subproblems, where each retailer and the warehouse determine their optimum periodic reviews and order up-to-quantities. An iterative coordination mechanism is proposed for changing the review periods and order up-to-quantities for each retailer and the warehouse in such a way that all parties within the DSC are satisfied with respect to total incurred costs per unit time interval. Coordination is performed by introducing fuzzy constraints on review periods and fuzzy tolerances on retailers and warehouse costs in local optimisation subproblems.     

Proactive heuristic procedures for robust project scheduling: An experimental analysis

Solution-robust project scheduling is a growing research field aiming at constructing proactive schedules to cope with multiple disruptions during project execution. When stochastic activity durations are considered, including time buffers between activities is a proven method to improve the stability of a baseline schedule.     

Uncertainty in the determination of glucose in aqueous solutions by high‐performance liquid chromatography with evaporative light scattering detection

The determination of glucose and other carbohydrates is the most widespread chemical analysis that is performed within the industries of food, beverage, forage, biomass, pulp and paper, pharmaceuticals among others. Besides that, sugar refineries need to control their products, by‐products and effluents, and furthermore, glucose in the sucrose refining process, is considered an impurity, which shall be controlled. Being HPLC the most currently instrumental technique used for glucose analysis, the evaporative light scattering detector (ELSD) offers advantages (sensitivity, possibility for operating in gradient mode) over the also used refractive index detector. In this work, an HPLC‐ELSD methodology was optimised and validated, aiming the estimate of the uncertainty associated with the results at low levels of concentration of glucose to be measured. Linearity of the response was obtained in the range of glucose concentrations from 20 to 300 mg/L, with an analysis time of 10 min. The global uncertainty was estimated accordingly to the bottom‐up approach used by Eurachem. It was 13% on average for concentrations from 100 to 300 mg/L. For lower concentrations, uncertainty increased significantly up to 30% in the vicinity of the LOD of the method.     

Grey dynamic input-output analysis

A system whose information is partially known and partially unknown is named as a grey system and it is general situation in reality. On the basis of grey number/matrix and their covered operation, some definitions of grey functions are enumerated. Combining grey system theory with traditional dynamic input-output analysis, we propose the grey dynamic input-output analysis. It has two types, i.e., the continuous and the disperse. The most important results are the computational formulas of the matrix-covered set of inverse grey investment coefficient matrix. By using them, we can forecast and control the economic system under the uncertain situation. For the calculation of the matrix-covered set of inverse grey matrices, we obtain the desired formulas, which can be applied to general grey matrices. The formulas can greatly promote the development of grey theory and enrich the applied fields of uncertain mathematics. The modified case illustrates our method.