A CONTINGENCY APPROACH TO CHOICE

The moment of choice, when a decision option is identified as being preferred, is a key stage in a decision-making process. Some explicit or implicit mechanism, be it optimising, satisficing or a combination of the two, is required to make a choice. Dependable decision-making requires an appropriate choice mechanism, but the process and criteria by which a choice mechanism is selected are seldom addressed. A contingency approach to choice is proposed in which a choice mechanism is selected according to the characteristics of the information available and the meta-level constraints and objectives of the decision-maker. Information used in the choice is characterised according to the extent and type of uncertainty. The importance in effective decision-making of meta-level requirements, for example for inclusive and transparent mechanisms, is discussed. Normative and fuzzy choice mechanisms are cast within a generic framework of choice mechanisms. A new theory of choice based on interval probabilities is introduced for use in some situations where existing theories of choice are inappropriate.     

CONTROL OF CONSTRUCTION ACTIVITIES: III. FUZZY-BASED CONTROLLER

In this paper, a real-time fuzzy-based controller of construction activities is proposed. Because of the numerous uncertainties associated with construction activities, their control requires a different approach than the traditional feedback methods. These methods are essentially based on the knowledge of a transfer function that models the input/output relationships for the controlled system. For a highly complex and uncertain system such as a construction activity, a fuzzy-based control strategy was found to be a suitable and effective approach. The proposed control system is built in two main levels, the process and the activity levels. Each level comprises two main units, namely, the fuzzy controller and the self learning algorithm. The implementation of the control actions can be performed by either manipulating the states of the variables for the purpose of improving the process output, or changing the process behavior function for the same purpose. Practical examples are presented wherever possible to illustrate the proposed control strategy.     

基于改进模糊自适应补偿的柔性关节机器人PD控制

针对柔性关节机器人具有不确定性、轨迹跟踪精度低和抖动的问题,提出一种改进模糊自适应补偿的PD控制方法。该方法在原有模糊自适应控制和PD控制的基础上进行改进,采用改进模糊自适应控制对PD控制进行补偿,以提高存在不确定性条件下的关节轨迹跟踪精度并抑制抖动。通过Lyapunov理论证明了系统的稳定性。仿真结果表明：新型控制器具有良好的自适应能力,与传统PD控制和模糊自适应控制相比,新型控制策略显著提高了关节的轨迹跟踪精度并在一定程度上抑制了关节抖动。     

微波消解-ICP-AES法对电镀金层杂质元素含量测定的不确定度评价

采用微波消解-ICP-AES法测定了电镀金层中杂质元素的含量,并按照JJF1059—1999《测量不确定度评定与表示》对测量过程中对各种因素导致的不确定度进行评定。分析了测量不确定度分量的主要来源,对各不确定度分量进行了评定和计算。结果表明以微波消解-ICP-AES法测定电镀金层中杂质元素的含量时,测量不确定度主要由标准溶液的浓度、标准曲线非线性、测量重复性、仪器稳定性引入。通过对不确定度的评定,可以保证分析结果的准确性。     

乙丙橡胶门尼粘度标准物质的定值与不确定度评定

定值是标准物质研制的重要环节,也是标准物质研制水平高低的体现。标准物质研制中定值的准确和不确定度度评定的合理则对标准物质研制的水平和推广应用起着重要的作用。对均匀性、稳定性检验后符合标准物质研制的乙丙橡胶(EPDM)4050样品,联合多家实验室进行定值。EPDM 4050门尼粘度标准物质参与定值的各实验室的数据在0.01的显著水平时,定值的数据处于正态分布、无可疑值、处于等精度,定值数据的平均值为EPDM 4050门尼粘度标准物质的标称值,EPDM 4050标准物质在ML(1+4)100℃、ML(1+8)100℃下的标称值分别为42.4±0.5、40.4±0.4。     

电感耦合等离子体发射光谱法测量青海地区矿泉水中锶含量的不确定度

采用电感耦合等离子体发射光谱(ICP-AES)法测定矿泉水中锶的不确定度。分析了标准溶液配制过程及仪器测定过程的各种不确定度影响因素,对各不确定度分量进行了评定与计算,当锶的质量浓度为0.8373 mg/L时,测量结果的扩展不确定度为0.0192 mg/L(k=2)。该法适合于以线性回归法得出检测结果的不确定度评估。     

Robust capacitated facility location model for acquisitions under uncertainty

We study a new robust formulation for strategic location and capacity planning considering potential company acquisitions under uncertainty. Long-term logistics network planning is among the most difficult decisions for supply-chain managers. While costs, demands, etc. may be known or estimated well for the short-term, their future development is uncertain and difficult to predict.A new model formulation for the robust capacitated facility location problem is presented to cope with uncertainty in planning. Minimizing the expectation of the relative regrets across scenarios over multiple periods is the objective. It is achieved by dynamically assigning multi-level production allocations, locations and capacity adjustments for uncertain parameter development over time. Considering acquisitions for profit maximization and its supply-chain impact is new as well as the simultaneous decision of capacity adjustment and facility location over time. The solution of the novel robust formulation provides a single setup where good results can be achieved for any realized scenario. Hence, the solution may not be optimal for one particular scenario but may be good, i.e. the highest expected profit to gain, for any highly probable future realization. We show that robust mixed-integer linear programming model achieves superior results to the deterministic configurations in exhaustive computational tests. This dynamic robust formulation allows the supply-chain to favorably adapt to acquisitions and uncertain developments of revenue, demand and costs and hence reduces the potential negative impacts of uncertainty on supply-chain operations.     

Generalized eigenvalue minimization for uncertain first-order plus time-delay processes

This paper shows how to apply generalized eigenvalue minimization to processes that can be described by a first-order plus time-delay model with uncertain gain, time constant and delay. An algorithm to transform the uncertain first-order plus time delay model into a state-space model with uncertainty polyhedron is firstly described. The accuracy of the transformation is studied using numerical examples. Then, the uncertainty polyhedron is rewritten as a linear-matrix-inequality constraint and generalized eigenvalue minimization is adopted to calculate a feedback control law. Case studies show that even if uncertainties associated with the first-order plus time delay model are significant, a stable feedback control law can be found. The proposed control is tested by comparing with a robust internal model control. It is also tested by applying it to the temperature control of air-handing units.     

Risk perceptions and approaches in multi-organizations: A research review 2000–2012

Shared risks and opportunities set specific premises for risk management (RM) in temporary multi-organizations (TMOs). However, most project RM research is presented from the perspective of a single-organizational project delivery team or covers limited risk perceptions and RM approaches. This paper aims to address how well the body of knowledge on multi-organizational RM corresponds to a state-of-art understanding on project RM and to identify which gaps need to be addressed in future research. The review involves: 1) the preferred view of risk as threat and/or opportunity, 2) the nature of addressed risks as anticipated or unanticipated risks or unrealistic assumptions, 3) the role of the multi-organization as the source of risks and/or resources for risk management (RM), and 4) the allocation of risk responsibilities. The review covers research papers published between 2000 and 2012 in four journals: International Journal of Project Management (IJPM), Project Management Journal (PMJ), Journal of Construction Engineering and Management (JCEM), and IEEE Transactions on Software Engineering (TSE). 105 eligible research papers were identified. The results and conclusion outline the identified main gaps in multi-organizational RM research compared to the state-of-art RM research and TMO-specific characteristics. The results can be used to inform research agendas on more holistic and dynamic multi-organizational RM concepts.     

Assessment and weighting of meteorological ensemble forecast members based on supervised machine learning with application to runoff simulations and flood warning

Numerical weather forecasts, such as meteorological forecasts of precipitation, are inherently uncertain. These uncertainties depend on model physics as well as initial and boundary conditions. Since precipitation forecasts form the input into hydrological models, the uncertainties of the precipitation forecasts result in uncertainties of flood forecasts. In order to consider these uncertainties, ensemble prediction systems are applied. These systems consist of several members simulated by different models or using a single model under varying initial and boundary conditions. However, a too wide uncertainty range obtained as a result of taking into account members with poor prediction skills may lead to underestimation or exaggeration of the risk of hazardous events. Therefore, the uncertainty range of model-based flood forecasts derived from the meteorological ensembles has to be restricted.In this paper, a methodology towards improving flood forecasts by weighting ensemble members according to their skills is presented. The skill of each ensemble member is evaluated by comparing the results of forecasts corresponding to this member with observed values in the past. Since numerous forecasts are required in order to reliably evaluate the skill, the evaluation procedure is time-consuming and tedious. Moreover, the evaluation is highly subjective, because an expert who performs it makes his decision based on his implicit knowledge.Therefore, approaches for the automated evaluation of such forecasts are required. Here, we present a semi-automated approach for the assessment of precipitation forecast ensemble members. The approach is based on supervised machine learning and was tested on ensemble precipitation forecasts for the area of the Mulde river basin in Germany. Based on the evaluation results of the specific ensemble members, weights corresponding to their forecast skill were calculated. These weights were then successfully used to reduce the uncertainties within rainfall-runoff simulations and flood risk predictions.