Pricing and recovery planning for demanufacturing operations with multiple used products and multiple reusable components

In this study, we consider a pricing and recovery planning problem encountered in demanufacturing where multiple types of reusable components can be recovered from multiple used product quality groups. The problem is to maximize the net profit of the demanufacturer by characterizing the optimal acquisition prices of the used products and selling prices along with recovery quantities of the reusable components. To address this problem, we develop a mathematical model and analyze the structural properties of an optimal solution to this model. We propose a solution algorithm that exploits these structural properties. Results from a comprehensive computational study shows that the proposed algorithm is very efficient and can be used in practice to address the large-sized instances of the problem. We also present a polynomial time algorithm for a special case of the problem of interest where there is a single used product quality group.     

Accelerating Benders stochastic decomposition for the optimization under uncertainty of the petroleum product supply chain

This paper addresses the solution of a two-stage stochastic programming model for an investment planning problem applied to the petroleum products supply chain. In this context, we present the development of acceleration techniques for the stochastic Benders decomposition that aim to strengthen the cuts generated, as well as to improve the quality of the solutions obtained during the execution of the algorithm. Computational experiments are presented for assessing the efficiency of the proposed framework. We compare the performance of the proposed algorithm with two other acceleration techniques. Results suggest that the proposed approach is able to efficiently solve the problem under consideration, achieving better performance in terms of computational times when compared to other two techniques.     

Time-scale separation redesigns for stabilization and performance recovery of uncertain nonlinear systems

In this paper we propose two different time-scale separation based robust redesign techniques which recover the trajectories of a nominal control design in the presence of uncertain nonlinearities. We first consider additive input uncertainties and design a high-gain filter to estimate the uncertainty. We then employ the fast variables arising from this filter in the feedback control law to cancel the effect of the uncertainties in the plant. We next extend this design to systems with uncertain input nonlinearities in which case we design two sets of high gain filters—the first to estimate the input uncertainty over a fast time-scale, and the second to force this estimate to converge to the nominal input on an intermediate time-scale. Using singular perturbation theory we prove that the trajectories of the respective two-time-scale and three-time scale redesigned systems approach those of the nominal system when the filter gains are increased. We illustrate the redesigns by applying them to various physically motivated examples.     

大型氨厂氨合成回路的模拟与分析

本文在ASPEN PLUS软件上装入用户单元操作模块和子程序,并在ASPEN数据库上修改和添加若干物性参数,建立了氨合成回路的专用计算机模拟软件.该软件既可做如Kellogg型氨厂现行氨合成回路流程的详细物热衡算又可做其它各种不同氨合成回路流程的预设计和多方案的比较计算.文章给出了工程应用实例.计算并分析了不同设计控制条件下各操作点的数值和能耗.     

An integrated supply chain model for new products with imprecise production and supply under scenario dependent fuzzy random demand

In the present day business scenario, instant changes in market demand, different source of materials and manufacturing technologies force many companies to change their supply chain planning in order to tackle the real-world uncertainty. The purpose of this paper is to develop a multi-objective two-stage stochastic programming supply chain model that incorporates imprecise production rate and supplier capacity under scenario dependent fuzzy random demand associated with new product supply chains. The objectives are to maximise the supply chain profit, achieve desired service level and minimise financial risk. The proposed model allows simultaneous determination of optimum supply chain design, procurement and production quantities across the different plants, and trade-offs between inventory and transportation modes for both inbound and outbound logistics. Analogous to chance constraints, we have used the possibility measure to quantify the demand uncertainties and the model is solved using fuzzy linear programming approach. An illustration is presented to demonstrate the effectiveness of the proposed model. Sensitivity analysis is performed for maximisation of the supply chain profit with respect to different confidence level of service, risk and possibility measure. It is found that when one considers the service level and risk as robustness measure the variability in profit reduces.     

An algorithm for the optimization of nonbifurcated flows in computer communication networks

For practical reasons, many computer communications networks currently in operation use single path routing, also called nonbifurcated routing: the traffic of every end to end communication is constrained to follow at most one single path at a time. The determination of optimal nonbifurcated flows is therefore a problem which confronts many network designers. In this paper, two important features of the classical nonbifurcated flow deviation algorithm, originally proposed by Fratta et al. are improved: the path length metric and the starting flow calculation. Tested on different examples, the essential gain is brought by a heuristic which provides a starting flow which is already a reasonable approximation of the global nonbifurcated optimum. Compared with the initial algorithm, this starting flow leads, with less computing time, to solutions which are closer to the optimum.     

Stochastic optimization for the calculation of the time dependency of the physiological demand during exercise and recovery

The stochastic optimization method ALOPEX IV is successfully applied to the problem of estimating the time dependency of the physiological demand in response to exercise. This is a fundamental and unsolved problem in the area of exercise physiology, where the lack of appropriate tools and techniques forces the assumption and the use of a constant demand during exercise. By the use of an appropriate partition of the physiological time series and by means of stochastic optimization, the time dependency of the physiological demand during heavy intensity exercise and its subsequent recovery is, for the first time, revealed.     

Ecological performance of electrical consumer products: the influence of automation and information-based measures

Being concerned with the environmental impact of electrical consumer products, this article examines possibilities of influencing ecological user performance through design features. Furthermore, it looks at the relationship of user characteristics and ecological performance. The impact of level of automation and type of control labelling on ecological user performance was examined in a lab-based experimental scenario with 36 users. In addition to performance indicators, a range of user variables (e.g., self-reported domestic behaviour, environmental knowledge and attitude) was measured to assess their influence on user behaviour. The results showed that low-level automation improved ecological performance whereas no such positive effect was observed for enhanced display-control labelling. Furthermore, the results suggested that the user's mental model of ecological performance was rather limited. No relationship was found between environmental knowledge, attitude and performance. The findings pointed at the strong prevalence of habits in the domestic domain. The implications of the results for designers of consumer products are discussed.     

Synthesis and infeasibility analysis for stochastic models of biochemical systems using statistical model checking and abstraction refinement

The stochastic dynamics of biochemical reaction networks can be modeled using a number of succinct formalisms all of whose semantics are expressed as Continuous Time Markov Chains (CTMC). While some kinetic parameters for such models can be measured experimentally, most are estimated by either fitting to experimental data or by performing ad hoc, and often manual search procedures. We consider an alternative strategy to the problem, and introduce algorithms for automatically synthesizing the set of all kinetic parameters such that the model satisfies a given high-level behavioral specification. Our algorithms, which integrate statistical model checking and abstraction refinement, can also report the infeasibility of the model if no such combination of parameters exists. Behavioral specifications can be given in any finitely monitorable logic for stochastic systems, including the probabilistic and bounded fragments of linear and metric temporal logics. The correctness of our algorithms is established using a novel combination of arguments based on survey sampling and uniform continuity. We prove that the probability of a measurable set of paths is uniformly and jointly continuous with respect to the kinetic parameters. Under a suitable technical condition, we also show that the unbiased statistical estimator for the probability of a measurable set of paths is monotonic in the parameter space. We apply our algorithms to two benchmark models of biochemical signaling, and demonstrate that they can efficiently find parameter regimes satisfying a given high-level behavioral specification. In particular, we show that our algorithms can synthesize up to 6 parameters, simultaneously, which is more than that reported by any other synthesis algorithm for stochastic systems. Moreover, when parameter estimation is desired, as opposed to synthesis, we show that our approach can scale to even higher dimensional spaces, by identifying the single parameter combination that maximizes the probability of the behavior being true in an 11-dimensional system.     

The influence of user interaction and participation in social media on the consumption intention of niche products

Social media may be particularly adept at promoting niche products because of the tendency of consumers to participate in generating reviews and discussing such products, thereby raising interest in them. In this study, we investigate how patterns of user interaction in discussing a niche cultural product may influence participation levels, which in turn enhance consumption intentions. We show that higher levels of participation can indeed enhance consumption intention. Furthermore, interaction patterns with high inclusiveness and betweenness centralization may enhance participation levels, whereas out-degree centralization and core-periphery have a detrimental influence. Implications for research and practice are discussed.     

Development of a fuzzy goal programming model for optimization of lead time and cost in an overlapped product development project using a Gaussian Adaptive Particle Swarm Optimization-based approach

The aim of this paper is to present a model-based methodology to estimate the optimal amount of overlapping and communication policy with a view to minimizing product development lead time and cost. In the first step of methodology, the underlying two factors are considered in order to formulate mathematically a multi-objective function for a complete product development project. To add these objectives, incommensurate in nature, a fuzzy goal programming-based approach is adopted as the second step. In order to attain the optimal solution of formulated objective function, this paper introduces a novel approach, “Gaussian Adaptive Particle Swarm Optimization” (GA-PSO), which is embedded with two beneficial attributes: (1) Gaussian probability distribution, and (2) Time-Varying Acceleration Coefficients strategy. An illustrative hypothetical example of mobile phones is detailed to demonstrate the proposed model-based methodology. Experiments are performed on an underlying example, and computational results are reported to support the efficacy of the proposed model.     

Consideration of illumination effects and optimization of window size for accurate calculation of depth map for 3D shape recovery

Obtaining an accurate and precise depth map is the ultimate goal for 3D shape recovery. For depth map estimation, one of the most vital parts is the initial selection of the focus measure and processing the images with the selected focus measure. Although, many focus measures have been proposed in the literature but not much attention has been paid to the factors affecting those focus measures as well as the manner the images are processed with those focus measures. In this paper, for accurate calculation of depth map, we consider the effects of illumination on the depth map as well as the selection of the window size for application of the focus measures. The resulting depth map can further be used in techniques and algorithms leading to recovery of three-dimensional structure of the object which is required in many high-level vision applications. It is shown that the illumination effects can directly result in incorrect estimation of depth map if proper window size is not selected during focus measure computation. Further, it is shown that the images need some kind of pre-processing to enhance the dark regions and shadows in the image. For this purpose, an adaptive enhancement algorithm is proposed for pre-processing. In this paper, we prove that without such pre-processing for image enhancement and without the use of proper window size for the estimation of depth maps, it is not possible to obtain the accurate depth map.     

Surveying the dimensions and characteristics of Korean ears for the ergonomic design of ear-related products

Six hundred male and female Korean subjects aged 17–89 were selected for this study. Four different points of the outer ear—the lengths of the pinna, the ear connection point, the earhole and the lobule thickness—were measured and analyzed along with demographic data, including age, stature and weight. The results showed that age, sex and different ethnic populations were determinants of ear dimensions as exemplified by the length of the pinna which increases as age increases (r=0.689). The primary objective of this study was to provide product designers with the anthropometric dimensions of Korean ears and recommend appropriate solutions for the ergonomic design of ear-related products. Based on the results of this study, it is recommended that the change in body parts due to aging be considered in the application of anthropometric data when designing such products.

Relevance to Industry

Limited research has been done on the dimensions of ears although an extensive amount of research has been conducted on other parts of the human body. The findings of this study, therefore, can be used as a basis for researching and producing ergonomically designed ear-related products.     

Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA

Lidar provides enhanced abilities to remotely map leaf area index (LAI) with improved accuracies. We aim to further explore the capability of discrete-return lidar for estimating LAI over a pine-dominated forest in East Texas, with a secondary goal to compare the lidar-derived LAI map and the GLOBCARBON moderate-resolution satellite LAI product. Specific problems we addressed include (1) evaluating the effects of analysts and algorithms on in-situ LAI estimates from hemispherical photographs (hemiphoto), (2) examining the effectiveness of various lidar metrics, including laser penetration, canopy height and foliage density metrics, to predict LAI, (3) assessing the utility of integrating Quickbird multispectral imagery with lidar for improving the LAI estimate accuracy, and (4) developing a scheme to co-register the lidar and satellite LAI maps and evaluating the consistency between them. Results show that the use of different analysts or algorithms in analyzing hemiphotos caused an average uncertainty of 0.35 in in-situ LAI, and that several laser penetration metrics in logarithm models were more effective than other lidar metrics, with the best one explaining 84% of the variation in the in-situ LAI (RMSE = 0.29 LAI). The selection of plot size and height threshold in calculating laser penetration metrics greatly affected the effectiveness of these metrics. The combined use of NDVI and lidar metrics did not significantly improve estimation over the use of lidar alone. We also found that mis-registration could induce a large artificial discrepancy into the pixelwise comparison between the coarse-resolution satellite and fine-resolution lidar-derived LAI maps. By compensating for a systematic sub-pixel shift error, the correlation between two maps increased from 0.08 to 0.85 for pines (n = 24 pixels). However, the absolute differences between the two LAI maps still remained large due to the inaccuracy in accounting for clumping effects. Overall, our findings imply that lidar offers a superior tool for mapping LAI at local to regional scales as compared to optical remote sensing, accuracies of lidar-estimate LAI are affected not only by the choice of models but also by the absolute accuracy of in-situ reference LAI used for model calibration, and lidar-derived LAI maps can serve as reliable references for validating moderate-resolution satellite LAI products over large areas.     

Synthesis of ground and remote sensing data for monitoring ecosystem functions in the Colorado River Delta, Mexico

The delta of the Colorado River in Mexico supports a rich mix of estuarine, wetland and riparian ecosystems that provide habitat for over 350 species of birds as well as fish, marine mammals, and other wildlife. An important part of the delta ecosystem is the riparian corridor, which is supported by agricultural return flows and waste spills of water originating in the U.S. and Mexico. These flows may be curtailed in the future due to climate change and changing land use practices (out-of-basin water transfers, increased agricultural efficiency, and more optimal management of dams) in the U.S. and Mexico, and resource managers need to monitor the effects of their water management practices on these ecosystems. We developed ground-validated, remote sensing methods to monitor the vegetation status, habitat value, and water use of wetland and riparian ecosystems using multi-temporal, multi-resolution images. The integrated methodology allowed us to project species composition, leaf area index, fractional cover, habitat value, and evapotranspiration over seasons and years throughout the delta, in response to variable water flows from the U.S. to Mexico. Waste spills of water from the U.S. have regenerated native cottonwood and willow trees in the riparian corridor and created backwater and marsh areas that support birds and other wildlife. However, the main source of water supporting the riparian vegetation is the regional aquifer recharged by underflow from U.S. and Mexico irrigation districts. Native trees have a short half-life in the riparian zone due to human-set fires and harvesting for timber. Active management, monitoring, and restoration programs are needed to maintain the habitat value of this ecosystem for the future.