High Model Complexity Research Articles

The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks

Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO.

Advanced control strategies for the multicolumn countercurrent solvent gradient purification process

The multicolumn countercurrent solvent gradient purification process (MCSGP) is a semicontinuous, chromatographic separation process used in the production of monoclonal antibodies) . The process is characterized by high model complexity and periodicity that challenge the development of control strategies, necessary for feasible and efficient operation and essential toward continuous production. A novel approach for the development of control policies for the MCSGP process, which enables efficient continuous process control is presented. Based on a high fidelity model, the recently presented PAROC framework and software platform that allows seamless design and in‐silico validation of advanced controllers for complex systems are followed. The controller presented in this work is successfully tested against disturbances and is shown to efficiently capture the process periodic nature. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2341–2357, 2016

SimpleBox 4.0: Improving the model while keeping it simple…

Chemical behavior in the environment is often modeled with multimedia fate models. SimpleBox is one often-used multimedia fate model, firstly developed in 1986. Since then, two updated versions were published. Based on recent scientific developments and experience with SimpleBox 3.0, a new version of SimpleBox was developed and is made public here: SimpleBox 4.0. In this new model, eight major changes were implemented: removal of the local scale and vegetation compartments, addition of lake compartments and deep ocean compartments (including the thermohaline circulation), implementation of intermittent rain instead of drizzle and of depth dependent soil concentrations, adjustment of the partitioning behavior for organic acids and bases as well as of the value for enthalpy of vaporization. In this paper, the effects of the model changes in SimpleBox 4.0 on the predicted steady-state concentrations of chemical substances were explored for different substance groups (neutral organic substances, acids, bases, metals) in a standard emission scenario. In general, the largest differences between the predicted concentrations in the new and the old model are caused by the implementation of layered ocean compartments. Undesirable high model complexity caused by vegetation compartments and a local scale were removed to enlarge the simplicity and user friendliness of the model.

Joint analyses model for total cholesterol and triglyceride in human serum with near-infrared spectroscopy

The development of a small, dedicated near-infrared (NIR) spectrometer has promising potential applications, such as for joint analyses of total cholesterol (TC) and triglyceride (TG) in human serum for preventing and treating hyperlipidemia of a large population. The appropriate wavelength selection is a key technology for developing such a spectrometer. For this reason, a novel wavelength selection method, named the equidistant combination partial least squares (EC-PLS), was applied to the wavelength selection for the NIR analyses of TC and TG in human serum. A rigorous process based on the various divisions of calibration and prediction sets was performed to achieve modeling optimization with stability. By applying EC-PLS, a model set was developed, which consists of various models that were equivalent to the optimal model. The joint analyses model of the two indicators was further selected with only 50 wavelengths. The random validation samples excluded from the modeling process were used to validate the selected model. The root-mean-square errors, correlation coefficients and ratio of performance to deviation for the prediction were 0.197mmolL−1, 0.985 and 5.6 for TC, and 0.101mmolL−1, 0.992 and 8.0 for TG, respectively. The sensitivity and specificity for hyperlipidemia were 96.2% and 98.0%. These findings indicate high prediction accuracy and low model complexity. The proposed wavelength selection provided valuable references for the designing of a small, dedicated spectrometer for hyperlipidemia. The methodological framework and optimization algorithm are universal, such that they can be applied to other fields.

Performance measures based optimization of supply chain network resilience: A NSGA-II + Co-Kriging approach

Increased vulnerability of supply chain networks, due to globalization of trade, has solicited attention of researchers and practitioners towards enhanced risk and disaster management. This has resulted in evolution of extant literature and new practices to construct resilient networks. Resilience is the ability of a network to regain its original state post-disaster. In this work, it is measured by the expected value of the fraction of demand that gets satisfied post-disaster. Most studies in literature capture resilience through qualitative dimensions. Even quantitative based researches, compute resilience through structural dimensions which characterize network density, complexity or excess resource availability. This has resulted in inadequate emphasis on two important performance measures of a supply chain network: the percentage of unfulfilled demand and the total transportation cost post-disaster.This work addresses above gap through a Multi-Objective Stochastic Mixed-Integer Programming (MOS-MIP) model with above two performance measures as objective functions. To address high computational complexity of MOS-MIP model, a two stage approach of NSGA-II+Co-Kriging is adopted. NSGA-II generates initial points of Pareto frontier which form input for surrogate modelling through Co-Kriging. As compared to conventional simulation, the proposed approach is computationally cheaper and can handle multi-objective formulation effectively. Co-Kriging quickly performs interpolation to provide enriched Pareto frontier. Additionally, it provides variance plot to define degree of uncertainty or confidence associated with accuracy of prediction of each point of Pareto frontier. Subsequently, managers can make informed choices by evaluating tradeoff between objective functions through enriched Pareto frontier with associated degree of confidence of prediction accuracy.

Nutritional challenges in the Brazilian Unified National Health System for building the interface between health and food and nutritional security

This article discusses the establishment of inter-sector action between health and food and nutritional security in Brazil from 2003 to 2010, when this issue was launched as a priority on the government's agenda. A qualitative study was developed according to constructivist epistemology, using key-informant interviews in the field's nationwide social oversight body. Advances and challenges in this process are addressed as analytical categories. The National Food and Nutrition Policy (PNAN) was mentioned as the link between the two fields, decentralized through a network with activity in the states and municipalities. However, the study found political, institutional, and operational obstacles to the effective implementation of the PNAN in the Brazilian Unified National Health System and consequently to a contribution to the advancement of Health and Food and Nutritional Security in the country. The predominance of the biomedical, curative, and high-complexity model was cited as the principal impediment, while health promotion policies like the PNAN were assigned secondary priority.

Evolving lane merge traffic behaviour simulations via a macroscopic objective function and a machine learning system trained through bootstrapped human judgement

Multi-agent based traffic simulation models have become increasingly important in simulating, studying and analysing traffic behaviours due to their ability to model more sophisticated behaviours of traffic by codifying simple rules into agents. However, such models require selection of appropriate rules and tuning of parameters for the selected rules. This process demands extensive resources if to be done manually. Further, high complexity models (in terms of number of rules and parameters) require a large computational cost to run, imposing scalability problems. In this work, four simple rules are introduced by reformulating existing concepts in the literature in order to simulate the self-organising behaviour of traffic where two lanes form into one and when two types of vehicles (cars and trucks) are present. The optimal rule and parameter combinations are explored via an evolutionary framework to overcome the resource demanding nature of the process. Two forms of objective functions - 1) a macroscopic objective function which focuses on macroscopic properties of traffic 2) a machine learning system trained based on human judgement concerning microscopic interaction of traffic - are studied in order to evolve low complexity and high fidelity traffic simulations. The differences in the rule sets evolved by the two objective functions are discussed highlighting the importance of selecting an appropriate objective function based on the simulation requirements and available resources. Finally, the change of the rule distribution as a function of generation in the evolutionary process is investigated in order to understand the complexity change in the simulations as a function of rule count as simulations are evolving towards high fidelity. This provides an abstract understanding of the relationship between complexity and fidelity in multi-agent based simulations concerning the particular problem of simulation of lane merge traffic.

Thermal-Aware Task Scheduling for 3D-Network-on-Chip: A Bottom to Top Scheme

Three-dimensional network-on-chip (3D-NoC) emerges as a potential multi-core architecture delivering high performance, high energy efficiency and great scalability. However, 3D-NoC suffers from severe thermal problems due to its high power density. To solve this problem, thermal-aware scheduling is an effective solution. However, the high complexity of the thermal model of 3D-NoC becomes a major hurdle for developing efficient thermal-aware scheduling algorithms for 3D-NoC. In this paper, we propose a novel thermal-aware task scheduling scheme named as the Bottom-to-Top (B2T) approach to address this challenge. This heuristic-based method performs task allocation on processing units to efficiently minimize the peak temperature and improve the execution time of the tasks with low complexity. The algorithm is first designed for two-layer 3D-NoC and then extended to 3D-NoC with an arbitrary number of layers. When compared to traditional thermal-aware scheduling algorithms designed for 2D-NoC, our B2T algorithm can achieve significant peak temperature reduction (up to 11.9[Formula: see text]C) and performance improvement (up to 4%) on two-layer 3D-NoC. The improvement becomes more significant as the number of layers in 3D-NoC increases. For four-layer 3D-NoC, the improvement is up to [Formula: see text]C peak temperature reduction.

Integration of Spatial Reuse and Allocation for Downlink Broadcast in LTE-Advanced and WiMAX Relay Networks

Downlink broadcast in LTE-Advanced-(LTE-A) and WiMAX-based relay networks is a crucial service for multimedia delivery. Currently, most research effort goes into devising efficient resource allocation mechanisms to achieve more efficient resource utilization. However, spatial reuse, which is an important technique in improving transmission capacity, has received little attention in the literature. Thus, in this work, we investigate ways of achieving more efficient resource allocation in wireless relay networks via spatial reuse. We first formulate a joint spatial reuse and resource allocation problem as an integer linear programming (ILP) model. We then consider a grouping mechanism in which relay stations (RSs) are grouped together if they do not interfere with each other's transmission signal. RSs in the same group can thus utilize spatial reuse by using the same set of resources to broadcast data. Because of the high computational complexity of the ILP model, we propose a two-phase heuristic solution. In the first phase, the enhanced-resource diminishing principle approach is employed to determine the number of resources required by the base station and a set of selected RSs. In the second phase, a max-coloring algorithm is employed to organize the selected RSs into broadcast groups and then assign the required resources to each group by exploring the maximum advantage of spatial reuse. The simulation results show that the proposed solution improves system performance up to 61%, as compared with two existing mechanisms.

Interpretation of two-phase relative permeability curves through multiple formulations and Model Quality criteria

Abstract We illustrate the way formal model identification criteria can be employed to rank and evaluate a set of alternative models in the context of the interpretation of laboratory scale experiments yielding two-phase relative permeability curves. We consider a set of empirical two-phase relative permeability models (i.e., Corey, Chierici and LET) which are typically employed in industrial applications requiring water/oil relative permeability quantifications. Model uncertainty is quantified through the use of a set of model weights which are rendered by model posterior probabilities conditional on observations. These weights are then employed to (a) rank the models according to their relative skill to interpret the observations and (b) obtain model averaged results which allow accommodating within a unified theoretical framework uncertainties arising from differences amongst model structures. As a test bed for our study, we employ high quality two-phase relative permeability estimates resulting from steady-state imbibition experiments on two diverse porous media, a quartz Sand-pack and a Berea sandstone core, together with additional published datasets. The parameters of each model are estimated within a Maximum Likelihood framework. Our results highlight that in most cases the complexity of the problem appears to justify favoring a model with a high number of uncertain parameters over a simpler model structure. Posterior probabilities reveal that in several cases, most notably for the assessment of oil relative permeabilities, the weights associated with the simplest models is not negligible. This suggests that in these cases uncertainty quantification might benefit from a multi-model analysis, including both low- and high-complexity models. In most of the cases analyzed we find that model averaging leads to interpretations of the available data which are characterized by a higher degree of fidelity than that provided by the most skillful model.

Hierarchical Bayesian random intercept model-based cross-level interaction decomposition for truck driver injury severity investigations

Traffic crashes occurring on rural roadways induce more severe injuries and fatalities than those in urban areas, especially when there are trucks involved. Truck drivers are found to suffer higher potential of crash injuries compared with other occupational labors. Besides, unobserved heterogeneity in crash data analysis is a critical issue that needs to be carefully addressed. In this study, a hierarchical Bayesian random intercept model decomposing cross-level interaction effects as unobserved heterogeneity is developed to examine the posterior probabilities of truck driver injuries in rural truck-involved crashes. The interaction effects contributing to truck driver injury outcomes are investigated based on two-year rural truck-involved crashes in New Mexico from 2010 to 2011. The analysis results indicate that the cross-level interaction effects play an important role in predicting truck driver injury severities, and the proposed model produces comparable performance with the traditional random intercept model and the mixed logit model even after penalization by high model complexity. It is revealed that factors including road grade, number of vehicles involved in a crash, maximum vehicle damage in a crash, vehicle actions, driver age, seatbelt use, and driver under alcohol or drug influence, as well as a portion of their cross-level interaction effects with other variables are significantly associated with truck driver incapacitating injuries and fatalities. These findings are helpful to understand the respective or joint impacts of these attributes on truck driver injury patterns in rural truck-involved crashes.

Evaluation and comparison of data-driven and knowledge-supported Bayesian Belief Networks to assess the habitat suitability for alien macroinvertebrates

Defining habitats vulnerable to invasion is important to support the management of invasive alien species (IAS). We developed and applied data-driven and knowledge-supported data-driven Bayesian Belief Networks (BBNs) to assess the habitat suitability for alien gammarids. Data-driven model development using a Naive Bayes classifier and equal width discretization resulted in a habitat suitability model with a moderate technical performance (CCI = 68% K = 0.33). Although the structure of the knowledge-supported model yielded important ecological insight between environmental and biotic variables and the occurrence of alien gammarids, the performance was lower (CCI = 60% K = 0.19) compared to the purely data-driven model. The lower predictive performance of the knowledge-supported model may be attributed to its higher model complexity. Our study shows that BBNs can support the management of IAS as they are visually appealing, transparent models that facilitate integration of monitoring data and expert knowledge.

Oral Breakdown of Texturally Complex Gel‐Based Model Food

AbstractUntil now, the textural complexity of foods and subsequent influence on oral breakdown pathways has not been analyzed. This study evaluated the effect of textural complexity on the oral breakdown of foods by using gel‐based model foods with “built‐in” levels of textural complexity. Twenty subjects chewed and expectorated the model foods at different stages during mastication (chewing cycle). Textural complexity did not significantly impact oral transit time, number of chews or chewing rate. The high complexity (HC) model food followed a significantly different oral breakdown pathway to low complexity and medium complexity samples. During the initial stages of chewing, HC samples broke down into a significantly greater number (P < 0.05) of significantly smaller particles (P < 0.05). The surrounding matrices controlled the release of embedded inclusions, ensuring the HC samples followed a dynamic oral breakdown pathway with changes occurring in particle size and number throughout the whole chewing cycle.Practical ApplicationsThe textural complexity of food is associated with a range of differing texture attributes that from the first bite through to the point of swallowing stimulate the senses, creating a variety of texture perceptions. Little is known on how textural complexity can influence oral breakdown pathways. This study has shown how a high complexity model food follows a dynamic oral breakdown pathway where changes in the bolus occur throughout the whole chewing cycle. These results lend themselves to further study on how these constant changes influence perceivable texture and stimulate the senses during eating. It is also hypothesized by the authors that in foods that are more texturally complex, these perceived textures and potential stimulation might positively affect the satiation response.

The Privacy Analysis of Battery Control Mechanisms in Demand Response: Revealing State Approach and Rate Distortion Bounds

Demand response systems in the electricity grid, which rely on two way communication between the consumers and utility, require the transmission of instantaneous energy consumption to utilities. Perfect knowledge of a user’s power consumption profile by a utility is a violation of privacy and can be detrimental to the successful implementation of demand response systems. It has been shown that an in-home energy storage system (such as a battery/inverter) that provides a viable means to achieve the cost savings of instantaneous electricity pricing without inconvenience can also be used to hide a user’s power usage pattern. A fundamental tradeoff exists between the costs saved and the degree of privacy achievable, and in this paper, the tradeoff achievable by a finite capacity battery assuming a zero tolerance for activity delay is studied using a Markov process model for user’s demands and instantaneous electricity prices. Due to high computational complexity (continuous state-action space) of the stochastic control model, inner and upper bounds are presented on the optimal tradeoff. In particular, a class of battery charging policies based on minimizing revealing states is proposed to derive achievable privacy-cost savings tradeoff. The performance of this algorithm is compared with lower bounds derived using a greedy heuristic and upper bounds derived using an information theoretic rate distortion approach. The framework proposed is shown to be applicable even when users only desire partial information protection, such as presence/absence of activity or specific appliances they wish to hide. Numerical results based on real electricity and pricing data show that the proposed algorithm performs close to the upper bound demonstrating its efficacy.

Ride comfort optimization of a multi-axle heavy motorized wheel dump truck based on virtual and real prototype experiment integrated Kriging model

The optimization of hydro-pneumatic suspension parameters of a multi-axle heavy motorized wheel dump truck is carried out based on virtual and real prototype experiment integrated Kriging model in this article. The root mean square of vertical vibration acceleration, in the center of sprung mass, is assigned as the optimization objective. The constraints are the natural frequency, the working stroke, and the dynamic load of wheels. The suspension structure for the truck is the adjustable hydro-pneumatic suspension with ideal vehicle nonlinear characteristics, integrated with elastic and damping elements. Also, the hydraulic systems of two adjacent hydro-pneumatic suspension are interconnected. Considering the high complexity of the engineering model, a novel kind of meta-model called virtual and real prototype experiment integrated Kriging is proposed in this article. The interpolation principle and the construction of virtual and real prototype experiment integrated Kriging model were elucidated. Being different from traditional Kriging, virtual and real prototype experiment integrated Kriging combines the respective advantages of actual test and Computer Aided Engineering simulation. Based on the virtual and real prototype experiment integrated Kriging model, the optimization results, obtained by experimental verification, showed significant improvement in the ride comfort by 12.48% for front suspension and 11.79% for rear suspension. Compared with traditional Kriging, the optimization effect was improved by 3.05% and 3.38% respectively. Virtual and real prototype experiment integrated Kriging provides an effective way to approach the optimal solution for the optimization of high-complexity engineering problems.

PopPlanner: visually constructing demographic models for simulation.

Currently there are a number of coalescent simulation programs that support a wide range of features, such as arbitrary demographic models, migration, and sub structure. Defining the model is done typically with either text files or command line switches. Although this has proven to be a powerful method of defining models of high complexity, it is often error prone and difficult to read without familiarity both with command lines and the program in question. A intuitive GUI based population structure program that can both read and write applicable command lines would dramatically simplify the construction, modification, and error checking of such models by a wider user base.Results: PopPlanner is a tool to both construct and inspect complicated demographic models visually with a GUI where the user's primary interaction is through mouse gestures. Because of their popularity, we focus on ms and by extension msms, command line coalescent simulation programs. Our program can be used to find errors with existing command lines, or to build original command lines. Furthermore, the graphical output supports a number of editing and output features including export of publication quality figures.

Quantifying Performance of a Connected Vehicle by Optimal Control

In this paper we exploit trajectory optimization techniques to quantify acceleration performance improvements in an advanced powertrain that are achievable if a preview of a launch event is available about 1 sec in advance through vehicle to traffic light communications. The vehicle is at idle immediately before acceleration. Based on the preview information, the idle speed is increased immediately before launch. A dynamic optimization approach is employed to optimize the idle speed set-point and trajectories of clutch and engine actuators after start of the acceleration. The optimization is applied to a high complexity model of powertrain. To perform the optimization, the trajectories being optimized are first transcribed using Gaussian kernel functions and then a mesh adaptive direct search (MADS) algorithm is applied. The achievable average acceleration with and without preview (i.e., without raising pre-launch engine speed) and for different sets of actuator trajectories being optimized under drivability constraints are compared thereby quantifying the benefits of vehicle-to-infrastructure connectivity.

An Alternative Way to Model Population Ability Distributions in Large-Scale Educational Surveys.

In large-scale educational surveys, a latent regression model is used to compensate for the shortage of cognitive information. Conventionally, the covariates in the latent regression model are principal components extracted from background data. This operational method has several important disadvantages, such as the handling of missing data and the high model complexity. The approach introduced here to identify multiple groups that can account for the variation among students is to conduct a latent class analysis (LCA). In the LCA, one or more latent nominal variables are identified that can be used to classify respondents with respect to their background characteristics. These classifications are then introduced as predictors in the latent regression. The primary goal of this study was to explore whether this approach yields similar estimates of group means and standard deviations compared with the operational procedure. The alternative approaches based on LCA differed regarding the number of classes, the items used for the LCA, and whether manifest class membership information or class membership probabilities were used as independent variables in the latent regression. Overall, recovery of the operational approach's group means and standard deviations was very satisfactory for all LCA approaches. Furthermore, the posterior means and standard deviations used to generate plausible values derived from the operational approach and the LCA approaches correlated highly. Thus, incorporating independent variables based on an LCA of background data into the latent regression model appears to be a viable alternative to the operational approach.

Real time QRS detection based on M-ary likelihood ratio test on the DFT coefficients.

This paper shows an adaptive statistical test for QRS detection of electrocardiography (ECG) signals. The method is based on a M-ary generalized likelihood ratio test (LRT) defined over a multiple observation window in the Fourier domain. The motivations for proposing another detection algorithm based on maximum a posteriori (MAP) estimation are found in the high complexity of the signal model proposed in previous approaches which i) makes them computationally unfeasible or not intended for real time applications such as intensive care monitoring and (ii) in which the parameter selection conditions the overall performance. In this sense, we propose an alternative model based on the independent Gaussian properties of the Discrete Fourier Transform (DFT) coefficients, which allows to define a simplified MAP probability function. In addition, the proposed approach defines an adaptive MAP statistical test in which a global hypothesis is defined on particular hypotheses of the multiple observation window. In this sense, the observation interval is modeled as a discontinuous transmission discrete-time stochastic process avoiding the inclusion of parameters that constraint the morphology of the QRS complexes.

Control-oriented residual gas mass prediction for spark ignition engines

Trapped residual gas mass is an important physical factor that influences combustion phasing and variation, fuel consumption, and air mass prediction for fuel control. There are currently no mass-production sensors available to directly measure in-cylinder residual gas mass, so prediction models must be utilized for control. Residual gas content of the cylinder can be difficult to directly model for control purposes because it involves complex flows during gas exchange that are driven by fluctuating pressures in the intake and exhaust systems. Capturing these effects in an accurate manner generally requires high model complexity and computational effort outside of the capability of most production intent engine controllers. This paper presents a semi-physics-based control oriented residual gas mass (RGM) prediction method. The RGM model is based on Bernoulli’s principle and considers engine operating conditions, valve timing and geometry, and piston motion effects. Moreover, to more accurately estimate the burned gas back flow, this model captures gas wave dynamic effects in intake and exhaust manifold pressures. The model is described in detail and its prediction accuracy is compared to that of a high fidelity simulation that utilizes experimentally measured crank angle resolved intake, exhaust, and cylinder pressures as boundary conditions. The new model is incorporated into a rapid-prototype control system for real-time operation during transient and steady-state engine operation. The results show that the proposed RGM model provides real-time predictions within 1.9-2.3% RGF, creating relative estimation errors in the range of 10-24%, and is capable of running real-time for engine control.