Set Of Optimization Models Research Articles

Design and Application of Global Energy Trade Cross Border E-commerce Optimization Model

INTRODUCTION: With the continuous advancement of global economic integration, cross-border energy trade activities internationally are becoming increasingly frequent.
 OBJECTIVES: To improve the efficiency of energy trade, this study is devoted to exploring the optimization model of cross-border e-commerce in global energy trade. Introducing advanced information technology and e-commerce platforms aims to facilitate the digital transformation of energy trade, improve transaction efficiency, reduce costs, and promote sustainable energy development.
 METHODS: This study adopts a comprehensive methodology, including a literature review, case analysis, and model construction. First, relevant literature on global energy trade and cross-border e-commerce was thoroughly studied to understand the current development status and existing problems. Second, the successful experiences and challenges of cross-border e-commerce in enhancing the efficiency of energy trade are summarized through the analysis of several international cases. Finally, a set of optimization models that comprehensively consider market demand, technical conditions, and policy environment are constructed to guide the development of cross-border e-commerce in global energy trade. RESULTS: The empirical analysis of the optimization model reveals that cross-border e-commerce has significant potential in international energy trade. The model can effectively improve transaction efficiency, reduce the risk of information asymmetry, and promote the balanced development of the global energy market. It is also observed that digital transformation significantly affects the promotion of sustainable energy, providing a more sustainable path for global energy transition.
 CONCLUSION: By establishing a cross-border e-commerce optimization model for international energy trade, this study provides substantial theoretical and empirical support for promoting the digital transformation of energy trade. In the future, governments, enterprises, and international organizations can draw on the conclusions of this study to formulate relevant policies and strategies to promote global energy trade jointly toward a more efficient and sustainable development path.

Resource-Aware Federated Neural Architecture Search over Heterogeneous Mobile Devices

Federated learning has been recently proposed for many clients to collaboratively train a machine learning model in a privacy-preserving manner. However, it also amplifies the difficulty in designing good neural network architecture, especially considering heterogeneous mobile devices. To this end, we propose a novel neural architecture search algorithm, namely FedNAS, which can automatically generate a set of optimal models under federated settings. The main idea is to decouple the two primary steps of the NAS process, i.e, model search and model training, and separately distribute them on the cloud and devices. It also tackles the primary challenge of limited on-device computational and communication resources through its novel designs: FedNAS fully exploits the key opportunity of insufficient model candidate re-training during the architecture search process and incorporates three key optimizations: parallel candidate training on partial clients, early dropping candidates with inferior performance, and dynamic round numbers. Evaluated on typical CNN architectures and large-scale datasets, FedNAS is able to achieve comparable model accuracy as a state-of-the-art NAS algorithm that trains models with centralized data, and also reduces the client cost by up to 200_ or more compared to a straightforward design of federated NAS.

Mathematical model of unsteady flow in a natural circulation helium loop

This article presents a one-dimensional transient mathematical model of a helium thermosyphon, which was validated experimentally and shows agreement with measurement. The novelty stems mainly from its complexity, ability to cover real-world scenarios and a large variety of (not only) large-scale thermosyphon devices. The model uses scenario-driven boundary conditions that replicate the operation of the physical model, including the accumulation of heat energy in the solid parts of the heat exchangers and changes in mass of the flowing medium. The model is implemented using both the Eulerian fixed control volume approach and the Lagrangian particle moving approach, along with the pressure correction algorithm, which is innovative too. This paper contains a comprehensive description of the model algorithm, testing of numerical properties, such as sensibility to time step value and grid density; and the heat accumulation effect. The optimal model settings are presented. In addition, a comparison with experimental data is provided. The best agreement between model and measurement was obtained for pressure values (97% of data samples were below 5% of relative error), followed by temperature values (71% of data samples were below 5% of relative error). The model and measurement exhibited the poorest agreement in velocity values, with only 33% of data samples falling below the 5% relative error threshold.

Quantitative Stability of Optimization Problems with Stochastic Constraints

In this paper, we consider optimization problems with stochastic constraints. We derive quantitative stability results for the optimal value function, the optimal solution set and the feasible solution set of optimization models in which the underlying stochastic constraints involve the mathematical expectation of random single-valued and set-valued mappings, respectively. New primal sufficient conditions are developed for the uniform error bound property of the stochastic constraint system for the single-valued case.

Species distribution modelling of Monotheca buxifolia (Falc.) A. DC.: Present distribution and impacts of potential climate change

Species distribution modelling (SDM) is an important tool to examine the possible change in the population range and/or niche-shift under current environment and predicted climate change. Monotheca buxifolia is an economically and ecologically important tree species inhabiting Pakistan and Afghanistan in dense patches, and species range is contracting rapidly. This study hypothesize that predicted climate change might remarkably influence the existing distribution pattern of M. buxifolia in the study area. A total of 75 occurrence locations were identified comprising M. buxifolia as a dominant tree species. The Maximum Entropy (MaxEnt) algorithm was utilized to perform the SDM under current (the 1970s–2000s) and two future climate change scenarios (shared socioeconomic pathways: SSPs 245 and 585) of two time periods (the 2050s and 2070s). The optimal model settings were assessed, and simulation precision was assessed by examining the partial area under the receiver operating characteristic curve (pAUC-ROC). The results showed that out of 39 considered bio-climatic, topographic, edaphic, and remote sensing variables which were utilized in the preliminary model, 6 variables including precipitation of warmest quarter, topographic diversity, global human modification of terrestrial land, normalized difference vegetation index, isothermality, and elevation (in order) were the most influential drivers, and utilized in all reduced SDMs. A high predictive performance (pAUC-ROC; >0.9) of all the considered SDMs was recorded. A total of about 67,684 km2 of geographical area was predicted as suitable habitat (p > 0.8) for M. buxifolia, and Pakistan is the leading country (with about 54,975 km2 of suitable land area) under the current climate scenario. Overall, the existing distribution of the tree species in the study area might face considerable loss (i.e. rate of change %; −27 to −107) in future, and simultaneously a northward (high elevation) niche shift is predicted for all the considered future climate change scenarios. Hence, development and implementation of a coordinated conservation program is required on priority basis to save the tree species in its native geographic range.

Production System Efficiency Optimization Using Sensor Data, Machine Learning-based Simulation and Genetic Algorithms

In modern industries, there is a significant repository of sensor data, which contains a large amount of information. Unfortunately, this rich source of information is undervalued and underutilized, and its full potential is not fully exploited by modern day manufacturers. In the Industry 4.0 era, exploiting these powerful datasets is becoming critical for manufacturers’ survival and competitiveness in the age of artificial intelligence. Cooperative and mutual efforts between academia and the industrial sector to take advantage of these rich datasets have the potential to reap extraordinary benefits for business, the economy and society. Applying the latest artificial intelligence methods could increase production efficiencies and reduce environmental impacts. In view of the availability of large amounts of sensor data and its lack of full utilization, this research proposes an artificial intelligence solution that combines data envelopment analysis (DEA), machine learning-based simulation and genetic algorithms to optimize the efficiency of production systems through recommendations of the optimal model settings. First, DEA is used to identify the efficient and inefficient states of a production system, this information is input to the second step to build a machine learning model that makes predictions through simulations and production efficiency scenarios. Then, a genetic algorithm proposes an optimal scenario with the corresponding settings. The main research contribution of this proposed solution is its unique combination of DEA with machine learning models and genetic algorithms.

Performance evaluation of spectral wave model caused by ERA-Interim, ERA5, and CFSR wind fields in the Black Sea

The main objective of the present study is to evaluate the performance of the MIKE 21 SW (Spectral Wave) in a semi-closed basin (Black Sea). Wind data were obtained from ECMWF ERA-Interim, ECMWF ERA5, and NCEP CFSR. The wave model was calibrated and validated with wave measurements recorded at seven different stations along the Black Sea coastlines. In the calibration, several different physical parameters were tested to determine the optimal model settings, and the whitecapping parameter (Cds) was more influential than the bottom friction parameter (kn), wave breaking parameter (γ), and nonlinear wave-wave interactions in the prediction of the Black Sea wave properties. The wave results modeled using ERA-Interim showed less agreement with wave measurements than those obtained with ERA-5 and CFSR wind fields. Although the significant wave height and wave period modeled using ERA5 and CFSR wind fields were reasonably well matched at all measurement stations, ERA5 wind fields provided slightly better performance owing to having the largest correlation coefficient (R) and lowest statistical error measures (bias, RMSE, SI) in the Black Sea.

A micro gas turbine one-dimensional model: Approach description, calibration with a vector optimization methodology and validation

Based on recent studies, it is mandatory to match energy supply with user demand with the aim of achieving a significant primary energy saving. In order to ensure an efficient matching, the power plant must be capable to follow the energy demand fluctuation of specific users. The paper deals with the one-dimensional modeling, calibration and validation of a micro gas turbine plant which performs a regenerative Brayton cycle. Compressor and turbine have been outlined with performance maps, the combustor behavior is simulated by a 0D block and the recuperator is defined by a steady-state block; seven pipes have been modeled as 1D unsteady flow, and they connect the main components of the power plant. Although a micro gas turbine is substantially a steady plant, the application of a one-dimensional approach allows the accurate investigation of transient and stable part-load working conditions. The model has been calibrated using a multi-variable multi-objective methodology in which a genetic algorithm was coupled with the model and was capable to find out the optimal model settings. The model has been validated through the comparison between calculated and experimental data at part-load working conditions. The novelty of the paper is the modeling approach and its validation methodology.

Integrated energy system security region: Concepts, methods, and implementations

The interconnection and coupling of integrated energy systems (IES) including electricity system, natural gas system and district heating system become increasingly tight. It brings opportunities for improving energy consumption efficiency as well as challenges on security interactions. Thus, the concepts of the IES security region (SR), which considers all the essential operational constraints of IES (including voltage magnitude constraint, thermal constraint, gas pressure constraint, gas transmission constraint, and temperature constraint), are discussed in this paper. These concepts stem from the ideas of power system security regions and are expanded to the multi-dimensional IES involving the limits and parameters of electricity, natural gas, and district heating networks. A set of optimization models are developed to explore the SR boundaries (SRBs) in the IES. Also, a piecewise approximation is proposed to represent the SRB of IES in a multi-dimensional space with a minimum set of hyperplanes. After that, the potential implementations of SR for security assessment and optimal control in the IES are presented and analyzed. Numerical results from the IES 30-20-2×6 system demonstrates the feasibility and effectiveness of the proposed method and verifies that it can significantly enhance the situation awareness ability of the IES.

Generalized disjunctive programming models for the truck loading problem: A case study from the non-alcoholic beverages industry

A real-world truck loading problem is considered for the major non-alcoholic beverage bottler in Argentina. The previous manual procedure to define the truck loading plan is improved by automatizing the process through a set of optimization models applying Generalized Disjunctive Programming. Most of the operational practices and restrictions are taken into account. Given that there is flexibility to load products in the trucks, a better use of their capacities is obtained, and the balance of forces addressed avoids expensive penalties fees. By a set of examples, different metrics are tested, and good quality results are obtained for an every-day practice.

WH-MOEA: A Multi-Objective Evolutionary Algorithm for Wiener-Hammerstein System Identification. A Novel Approach for Trade-Off Analysis Between Complexity and Accuracy

Several approaches have been presented to identify Wiener-Hammerstein models, most of them starting from a linear dynamic model whose poles and zeros are distributed around the static non-linearity. To achieve good precision in the estimation, the Best Linear Approximation (BLA) has usually been used to represent the linear dynamics, while static non-linearity has been arbitrarily parameterised without considering model complexity. In this paper, identification of Wiener, Hammerstein or Wiener-Hammerstein models is stated as a multiobjective optimisation problem (MOP), with a trade-off between accuracy and model complexity. Precision is quantified with the Mean-Absolute-Error (MAE) between the real and estimated output, while complexity is based on the number of poles, zeros and points of the static non-linearity. To solve the MOP, WH-MOEA, a new multiobjective evolutionary algorithm (MOEA) is proposed. From a linear structure, WH-MOEA will generate a set of optimal models considering a static non-linearity with a variable number of points. Using WH-MOEA, a procedure is also proposed to analyse various linear structures with different numbers of poles and zeros (known as design concepts). A comparison of the Pareto fronts of each design concept allows a more in-depth analysis to select the most appropriate model according to the user’s needs. Finally, a complex numerical example and a real thermal process based on a Peltier cell are identified, showing the procedure’s goodness. The results show that it can be useful to consider the simultaneously precision and complexity of a block-oriented model (Wiener, Hammerstein or Wiener-Hammerstein) in a non-linear process identification.

Reliability assessment for systems suffering competing degradation and random shocks under fuzzy environment.

Reliability assessment of multi-component systems under competing degradation and random shocks has been intensively investigated in recent years. In most cases, the parameters associated with competing degradation and random shocks are represented by crisp values. However, due to insufficient data and vague judgments from experts, it may produce epistemic uncertainty with those parameters and they are befitting to be described as fuzzy numbers. In this article, the internal degradation is treated as a continuous monotonically increasing random process with respect to operating time, whereas the amount of cumulative damage produced by each external random shock is modeled by a geometric process. As components in a system suffer the same environmental condition, an external random shock will produce different amounts of cumulative damage to each component simultaneously. Each component fails when either the internal degradation or cumulative damage from the random shocks, whichever comes first, exceeds its corresponding random thresholds. Moreover, the parameters associated with the internal degradation and the random shocks are represented by triangular fuzzy numbers. The fuzzy reliability functions of components and the entire system are evaluated by a set of optimization models. A multi-component system, together with some comparative results, is presented to illustrate the implementation of the proposed method.

A Static Equivalent Model of Natural Gas Network for Electricity–Gas Co-Optimization

For the privacy concern, the data exchange between the electricity network and the natural gas network is not always possible. It brings challenges for the co-operation of electricity-gas systems. This paper proposes a static equivalent model that replaces the natural gas network, which lays the foundation for electricity-gas co-operation between separate electricity and gas companies. The influence of the natural gas network on the electricity network is represented in this equivalent model, which consists of a constraint equivalent model and a loss equivalent model. In the constraint equivalent model, a set of optimization models is formulated to approximate the feasible region of natural gas-fired units (NGUs) imposed by the operational constraints of the natural gas network. In the loss equivalent model, the natural gas consumption of compressors contributed by NGUs is considered based on the “shortest path principle” using the gas loss as the weight. The proposed static equivalent model is applied in the optimal power flow of electricity-gas systems. Case studies demonstrate the desired accuracy of the proposed method.

Function and energy consumption constrain neuronal biophysics in a canonical computation: Coincidence detection.

Neural morphology and membrane properties vary greatly between cell types in the nervous system. The computations and local circuit connectivity that neurons support are thought to be the key factors constraining the cells’ biophysical properties. Nevertheless, additional constraints can be expected to further shape neuronal design. Here, we focus on a particularly energy-intense system (as indicated by metabolic markers): principal neurons in the medial superior olive (MSO) nucleus of the auditory brainstem. Based on a modeling approach, we show that a trade-off between the level of performance of a functionally relevant computation and energy consumption predicts optimal ranges for cell morphology and membrane properties. The biophysical parameters appear most strongly constrained by functional needs, while energy use is minimized as long as function can be maintained. The key factors that determine model performance and energy consumption are 1) the saturation of the synaptic conductance input and 2) the temporal resolution of the postsynaptic signals as they reach the soma, which is largely determined by active membrane properties. MSO cells seem to operate close to pareto optimality, i.e., the trade-off boundary between performance and energy consumption that is formed by the set of optimal models. Good performance for drastically lower costs could in theory be achieved by small neurons without dendrites, as seen in the avian auditory system, pointing to additional constraints for mammalian MSO cells, including their circuit connectivity.

Evaluation of numerical wave model for typhoon wave simulation in South China Sea

The simulating waves nearshore (SWAN) model has typically been designed for wave simulations in near-shore regions. In this study, the model's applicability to the simulation of typhoon waves in the South China Sea (SCS) was evaluated. A blended wind field, consisting of an interior domain based on Fujita's model and an exterior domain based on Takahashi's model, was used as the driving wind field. The waves driven by Typhoon Kai-tak over the SCS that occurred in 2012 were selected for the numerical simulation research. Sensitivity analyses of time step, grid resolution, and angle resolution were performed in order to obtain optimal model settings. Through sensitivity analyses, it can be found that the time step has a large influence on the results, while grid resolution and angle resolution have a little effect on the results.

Increasing the Depth of Current Understanding: Sensitivity Testing of Deep-Sea Larval Dispersal Models for Ecologists.

Larval dispersal is an important ecological process of great interest to conservation and the establishment of marine protected areas. Increasing numbers of studies are turning to biophysical models to simulate dispersal patterns, including in the deep-sea, but for many ecologists unassisted by a physical oceanographer, a model can present as a black box. Sensitivity testing offers a means to test the models’ abilities and limitations and is a starting point for all modelling efforts. The aim of this study is to illustrate a sensitivity testing process for the unassisted ecologist, through a deep-sea case study example, and demonstrate how sensitivity testing can be used to determine optimal model settings, assess model adequacy, and inform ecological interpretation of model outputs. Five input parameters are tested (timestep of particle simulator (TS), horizontal (HS) and vertical separation (VS) of release points, release frequency (RF), and temporal range (TR) of simulations) using a commonly employed pairing of models. The procedures used are relevant to all marine larval dispersal models. It is shown how the results of these tests can inform the future set up and interpretation of ecological studies in this area. For example, an optimal arrangement of release locations spanning a release area could be deduced; the increased depth range spanned in deep-sea studies may necessitate the stratification of dispersal simulations with different numbers of release locations at different depths; no fewer than 52 releases per year should be used unless biologically informed; three years of simulations chosen based on climatic extremes may provide results with 90% similarity to five years of simulation; and this model setup is not appropriate for simulating rare dispersal events. A step-by-step process, summarising advice on the sensitivity testing procedure, is provided to inform all future unassisted ecologists looking to run a larval dispersal simulation.

ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models

Summary Recent studies have demonstrated a need for increased rigour in building and evaluating ecological niche models (ENMs) based on presence‐only occurrence data. Two major goals are to balance goodness‐of‐fit with model complexity (e.g. by ‘tuning’ model settings) and to evaluate models with spatially independent data. These issues are especially critical for data sets suffering from sampling bias, and for studies that require transferring models across space or time (e.g. responses to climate change or spread of invasive species). Efficient implementation of procedures to accomplish these goals, however, requires automation. We developed ENMeval, an R package that: (i) creates data sets for k‐fold cross‐validation using one of several methods for partitioning occurrence data (including options for spatially independent partitions), (ii) builds a series of candidate models using Maxent with a variety of user‐defined settings and (iii) provides multiple evaluation metrics to aid in selecting optimal model settings. The six methods for partitioning data are n−1 jackknife, random k‐folds ( = bins), user‐specified folds and three methods of masked geographically structured folds. ENMeval quantifies six evaluation metrics: the area under the curve of the receiver‐operating characteristic plot for test localities (AUCTEST), the difference between training and testing AUC (AUCDIFF), two different threshold‐based omission rates for test localities and the Akaike information criterion corrected for small sample sizes (AICc). We demonstrate ENMeval by tuning model settings for eight tree species of the genus Coccoloba in Puerto Rico based on AICc. Evaluation metrics varied substantially across model settings, and models selected with AICc differed from default ones. In summary, ENMeval facilitates the production of better ENMs and should promote future methodological research on many outstanding issues.

A Hybrid Human Dynamics Model on Analyzing Hotspots in Social Networks

The increasing development of social networks provides a unique source for analyzing human dynamics in the modern age. In this paper, we analyze the top‐one Internet forum in China (“Tianya Club”) and identify the statistical properties of hotspots, which can promptly reflect the crowd events in people′s real‐life. Empirical observations indicate that the interhotspot distribution follows a power law. To further understand the mechanism of such dynamic phenomena, we propose a hybrid human dynamic model that combines “memory” of individual and “interaction” among people. To build a rich simulation and evaluate this hybrid model, we apply three different network datasets (i.e., WS network, BA network, and Karate‐Club). Our simulation results are consistent with the empirical studies, which indicate that the model can provide a good understanding of the dynamic mechanism of crowd events using such social networking data. We additionally analyze the sensitivity of model parameters and find the optimal model settings.

Calibration and resolution effects on model performance for predicting shallow landslide locations in Taiwan

In this paper we optimise the spatially explicit prediction of landslide hazard, landslide triggering and subsequent movement downslope of materials for a mountainous catchment in Taiwan. The location prediction is optimised by subsequently adding three location parameters: rainfall distribution, land-use classes and DEM derived slopes. Then the three most important model parameters are calibrated to find the best prediction for both stable and unstable areas. The landslides predicted by the LAPSUS-LS model are compared with a landslide inventory to validate the output. The optimal model settings for the calibration area are then applied to a validation area. Results show that model performance can be improved by adding the spatial distribution of rainfall and by stratifying according to land-use classes. Landslide prediction is better with fine resolution DEMs, mainly because the local topography is smoothed in coarser resolutions. Although in general the amount of landslides is over-predicted, the overall performance indicates that the model is able to capture the important factors determining landslide location. Additional spatially distributed data such as regolith or soil depth and regeneration rates of the legacy effect can further enhance the model's prediction.

Optimization Models for the Location of Motorway Interchanges: Concessionaires’ Perspective

The location of motorway interchanges strongly affects the traffic motorways can capture and therefore the revenues for the concessionaires. This paper presents a set of optimization models aimed at assisting toll-motorway concessionaires when they set up the location of interchanges for a new motorway. The decisions are assumed to be made with the objective of maximizing profit. Road users select their itineraries according to the attractiveness of the routes, which is measured by travel costs (vehicle operating costs, accident costs, user time costs, and tolling costs). The choice is between the existing road network and a combination of existing road segments with new motorway segments. A deterministic model and two stochastic models are proposed. The latter models take into account the risks involved in motorway investment decisions. The usefulness of the models is illustrated for a real case study.