Bilevel formulation of a policy design problem considering multiple objectives and incomplete preferences

A bilevel optimization formulation of policy design problems considering multiple objectives and incomplete preferences of the stakeholders is presented. The formulation is presented for Feed-in-Tariff (FIT) policy design for decentralized energy infrastructure. The upper-level problem is the policy designer's problem and the lower-level problem is a Nash equilibrium problem resulting from market interactions. The policy designer has two objectives: maximizing the quantity of energy generated and minimizing policy cost. The stakeholders decide on quantities while maximizing net present value and minimizing capital investment. The Nash equilibrium problem in the presence of incomplete preferences is formulated as a stochastic linear complementarity problem and solved using expected value formulation, expected residual minimization formulation, and the Monte Carlo technique. The primary contributions in this article are the mathematical formulation of the FIT policy, the extension of computational policy design problems to multiple objectives, and the consideration of incomplete preferences of stakeholders for policy design problems.     

Human mobility models and opportunistic communications system design

In this paper, we seek to improve understanding of the structure of human mobility, with a view to using this for designing algorithms for the dissemination of data among mobile users. We analyse community structures and node centrality from the human mobility traces and use these two metrics to design efficient forwarding algorithms in terms of delivery ratio and delivery cost for mobile networks. This is the first empirical study of community and centrality using real human mobility datasets.     

Incorporating operational experience and design changes in availability forecasts

Reliability or availability forecasts which are based solely on past operating experience will be precise if the sample is large enough, and unbiased if nothing in the future design, environment, operating region or anything else changes. Unfortunately, life is never like that.

This paper considers the methodology and philosophy of modifying forecasts based on past experience to take account also of changes in design, construction methods, operating philosophy, environment, operator training and so on, between the plants which provided the operating experience and the plant for which the forecasts is being made. This emphasises the importance of collecting, assessing, and learning from past data and of a thorough knowledge of future designs, and procurement, operation, and maintenance policies. The difference between targets and central estimates is also discussed. The paper concludes that improvements in future availability can be made by learning from past experience, but that certain conditions must be fulfilled in order to do so.     

协同制造链的系统不确定性分析与建模

针对协同制造链(CMC)中存在大量不确定性的问题,分析了CMC运行过程中出现的不确定性及其来源,建立了不确定性数学模型和传播模型.通过对系统不确定性评价与预测过程的分析,提出了基于故障树分析法进行系统不确定度评定的贝叶斯模型,给出了具体评价过程,实现了CMC的不确定性评价与预测,并采用案例验证了该方法的可行性.CMC的...     

Process models in design and development

Many models of the design and development process have been published over the years, representing it for different purposes and from different points of view. This article contributes an organising framework that clarifies the topology of the literature on these models and thereby relates the main perspectives that have been developed. The main categories of model are introduced. Their contexts, advantages, and limitations are considered through discussion of selected examples. It is demonstrated that the framework integrates coverage of earlier reviews and as such provides a new perspective on the literature. Finally, key characteristics of design and development process models are discussed considering their applications in practice, and opportunities for further research are suggested. Overall, the article should aid researchers in positioning new models and new modelling approaches in relation to state-of-the-art. It may also be of interest to practitioners and educators seeking an overview of developments in this area.     

A new optimization framework using genetic algorithm and artificial neural network to reduce uncertainties in petroleum reservoir models

In this article, a new optimization framework to reduce uncertainties in petroleum reservoir attributes using artificial intelligence techniques (neural network and genetic algorithm) is proposed. Instead of using the deterministic values of the reservoir properties, as in a conventional process, the parameters of the probability density function of each uncertain attribute are set as design variables in an optimization process using a genetic algorithm. The objective function (OF) is based on the misfit of a set of models, sampled from the probability density function, and a symmetry factor (which represents the distribution of curves around the history) is used as weight in the OF. Artificial neural networks are trained to represent the production curves of each well and the proxy models generated are used to evaluate the OF in the optimization process. The proposed method was applied to a reservoir with 16 uncertain attributes and promising results were obtained.     

Incorporating human factors in order picking planning models: framework and research opportunities

Order picking (OP) activities, essential to logistics operations, are laborious and time-intensive. Humans are central actors in the OP process and determine both OP effectiveness and efficiency. Many researchers have developed models for planning OP activities and increasing the efficiencies of such systems by suggesting different warehouse layouts, OP routes or storage assignments. These studies have, however, ignored workers’ characteristics, or human factors, suggesting that they cannot be substantiated, which led to only partially realistic results. This paper proposes a conceptual framework for integrating human factors into planning models of OP activities and hypothesises that doing so improves the performance of an OP system and workers’ welfare. The framework is based on a systematic literature review that synthesises findings documented in the OP and human factors literature. The results of the paper may assist researchers and practitioners in designing OP systems by developing planning models that help in enhancing performance and reducing long-term costs caused by work-related inefficiencies.     

Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors

Freeway crash occurrences are highly influenced by geometric characteristics, traffic status, weather conditions and drivers’ behavior. For a mountainous freeway which suffers from adverse weather conditions, it is critical to incorporate real-time weather information and traffic data in the crash frequency study. In this paper, a Bayesian inference method was employed to model one year's crash data on I-70 in the state of Colorado. Real-time weather and traffic variables, along with geometric characteristics variables were evaluated in the models. Two scenarios were considered in this study, one seasonal and one crash type based case. For the methodology part, the Poisson model and two random effect models with a Bayesian inference method were employed and compared in this study. Deviance Information Criterion (DIC) was utilized as a comparison factor. The correlated random effect models outperformed the others. The results indicate that the weather condition variables, especially precipitation, play a key role in the crash occurrence models. The conclusions imply that different active traffic management strategies should be designed based on seasons, and single-vehicle crashes have different crash mechanism compared to multi-vehicle crashes.     

Probabilistic inverse problem and system uncertainties for damage detection in piezoelectrics

This paper provides a probabilistic formulation to design a monitoring setup for damage detection in piezoelectric plates, solving a model-based identification inverse problem (IP). The IP algorithm consists on the minimization of a cost functional defined as the quadratic-difference between experimental and trial measurements simulated by the finite element method. The motivation of this work comes from the necessity for a more rational design criteria applied to damage monitoring of piezoelectric materials. In addition, it is very important for the solving of the inverse problem to take into account the random nature of the system to be solved in order to obtain accurate and reliable solutions. In this direction, two investigations are considered. For the first, the experimental measurements are simulated combining a finite element and a Monte Carlo analysis, both validated with already published results. Then, an uncertainty analysis is used to obtain the statistical distribution of the simulated experimental measurements, while a sensitivity analysis is employed to find out the influence of the uncertainties in the model parameters related to the measurement noise. Upon the study of the measurements, they are used as the input for the damage identification IP which produces the location and extension of a defect inside a piezoelectric plate. For the second investigation, a probabilistic IP approach is developed to determine the statistical distribution and sensitivities of the IP solutions. This novel approach combines the Monte Carlo and the IP algorithm, considering the trial measurements as random. In conclusion, the analysis demonstrates that in order to improve the quality of the damage characterization, only a few material parameters have to be controlled at the experimental stage. It is important to note that this is not an experimental study, however, it can be considered as a first step to design a rational damage identification experimental device, controlling the variables that increase the noise level and decrease the accuracy of the IP solution.     

A new microsecond pulse-heating system to investigate thermophysical properties of solid and liquid metals

A new discharge system for resistive self-heating has been constructed for the measurement of accurate thermophysical properties. A constant-current pulse is used to heat metals over a time interval of 50 to 100 s, reaching temperatures up to the boiling point. New techniques have been developed to obtain sound speeds in the pulse-heated sample, emissivities, and vapor pressure. A new pyrometer allows the extension of the measured temperature range down to the melting point of copper.     

Development of an autofluorescent translocation biosensor system to investigate protein-protein interactions in living cells

Protein-protein interactions are crucial for all cellular events. To analyze protein-protein interactions in live mammalian cells, we developed novel protein translocation biosensors composed of glutathione S-transferase, mutants of GFP, and a rational combination of nuclear import and export signals. Nuclear accumulation of the cytoplasmic biosensors served as the reliable indicator, which was induced by the formation of protein complexes and could easily be detected by fluorescence microscopy. The efficacy of the system was systematically investigated by mapping the p53/mdm2 protein interaction interface. Specificity and general applicability of the biosensors were confirmed by studying additional classes of protein interaction domains (IDs), e.g., the leucine zipper IDs of Jun/Fos and the coiled-coil ID of Bcr-Abl in different cell lines. Importantly, we found that, in comparison to protein complementation assays, our system proved highly efficient and reversible and thus suited for the identification of molecular decoys to prevent specific protein-protein interactions in living cells. Reversibility was demonstrated in competition experiments by overexpressing the specific IDs or by the application of a p53/mdm2 protein interaction inhibitor. Thus, besides the convenient mapping of protein IDs in living cells, the modular translocation system has great potential to be employed in numerous cell-based assays for the identification of small-molecule protein interaction inhibitors as potential novel therapeutics.     

Waveform design in signal-dependent interference and application to target recognition with multiple transmissions

The authors present illumination waveforms matched to stochastic targets in the presence of signaldependent interference. The waveforms are formed by SNR and mutual information (MI) optimisation. We also use these waveforms in cognitive radar (CR) target identification application. In this application, the radar system attempts to identify a deterministic or random target using multiple transmissions. These transmissions are adaptively modified in response to previously received echoes. In addition, the authors present a new multi-band application of the CR platform.     

BEM applied to damage models emphasizing localization and associated regularization techniques

In this work, the implicit BEM formulation, initially developed in the context of a plasticity analysis is extended to incorporate damage mechanics models. The algebraic equations adopted for the formulation are obtained either using displacement or traction equations, for the boundary nodes, and strain equations for the internal nodes. The formulation is modified to incorporate a regularization technique based on a non-local integral formulation. The consistent tangent operator has been obtained for local and non-local formulations. Arc-length strategy developed for BEM formulations is adopted to analyse problems exhibiting the snap-back effects.     

Sensitivity of expected exceedance rate of SDOF-system response to statistical uncertainties of loading and system parameters

The effects of the statistical uncertainties of the loading and system parameters on the mean exceedance rate of a particular threshold are investigated for a linear SDOF-system with viscous damping. For this purpose the structural loading is described by the well-known stationary Kanai-Tajimi-earthquake model. The analysis is simplified by utilizing an approximate solution for the threshold-crossing rate, for which the error with respect to the exact solution is shown to be small. Each of the parameters involved in the expression for the mean exceedance rate of the stationary response of the structure is considered a random variable. The respective effects of the statistical uncertainties of the parameters on the threshold-crossing rate, as expressed by the first- and second moments, are shown explicitely in the numerical examples.     

Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models

This study attempts to explore the viability of dual-state models (i.e., zero-inflated and hurdle models) for traffic analysis zones (TAZs) based pedestrian and bicycle crash frequency analysis. Additionally, spatial spillover effects are explored in the models by employing exogenous variables from neighboring zones. The dual-state models such as zero-inflated negative binomial and hurdle negative binomial models (with and without spatial effects) are compared with the conventional single-state model (i.e., negative binomial). The model comparison for pedestrian and bicycle crashes revealed that the models that considered observed spatial effects perform better than the models that did not consider the observed spatial effects. Across the models with spatial spillover effects, the dual-state models especially zero-inflated negative binomial model offered better performance compared to single-state models. Moreover, the model results clearly highlighted the importance of various traffic, roadway, and sociodemographic characteristics of the TAZ as well as neighboring TAZs on pedestrian and bicycle crash frequency.     

Incorporating reliability in optimal design of water distribution networks—review and new concepts

Much of the effort in optimal design of water distribution networks (WDNs) has focussed so far on minimizing cost alone, with little emphasis on reliability or on investigating the tradeoff between cost and reliability. This is a consequence of the difficulty in defining reliability measures which are meaningful and appropriate, while still of a form which can be incorporated directly into optimization models. This paper will deal with these issues. It contains three parts: (1) conceptual discussion of reliability definitions from different points of view (system versus consumers), (2) a literature survey of existing techniques to incorporate reliability in the optimal design of WDNs, and (3) a new concept for explicitly including reliability in the optimal design of WDNs.     

Arrow’s theorem, multi-criteria decision problems and multi-attribute preferences in engineering design

Arrow’s theorem poses limits to the translation of the different preference orders on a set of options into a single preference order. In this paper, I argue, against opinions to the contrary, that Arrow’s theorem applies fully to multi-criteria decision problems as they occur in engineering design, making solution methods to such problems subject to the theorem’s negative result. Discussing the meaning and consequences for engineering design, I review the solution methods to such problems presented in the engineering design literature in the light of the theorem. It appears that underlying such methods is a mix-up of two fundamentally different problem definitions, as the theory of multi-attribute preferences, which is often presented as an adequate approach for engineering design, in fact fails to address the Arrowian multi-criteria problem. Finally, I suggest ways how engineering design might adopt results from discussions of Arrow’s theorem elsewhere in resolving its multi-criteria decision problems.     

An alternative analysis applied to investigate the ejector performance used in R141b jet-pump refrigeration system

The system performance of R141b jet-pump refrigerator is discussed based on an alternative analysis. It explains the operation of the jet-pump refrigerator operating under actual conditions. A 2 kW cooling of R141b jet-pump refrigerator is designed and constructed to test the ejector performance. With the use of an alternative analysis, point of interest called “critical evaporator temperature” is determined. This point indicates the lowest possible evaporator temperature where the secondary fluid flow is still choked. The critical point varies with the change in operating conditions and with the primary nozzle used. An increase of primary mass flow rate causes the critical point reducing. However, it does not always true. When the primary mass flow rate increases to a certain value, the refrigerator is operated at higher critical evaporator temperature. Thus, the optimum range of the primary mass flow rate for this particular ejector is provided for this present work.