Analysis of selected optimization methods in road transport

2018 ◽  
Vol 120 ◽  
pp. 447-458
Author(s):  
Waldemar Woźniak ◽  
Roman Stryjski ◽  
Janusz Mielniczuk ◽  
Tomasz Wojnarowski

The article presents practical solutions to the problem of allocation of transport orders to available transport means in road transport. For this purpose, a comparative analysis of the selected optimization method used in the TSL industry with the own solution was made. The proposed algorithm for effective allocation of selected transport orders was developed based on heuristic methods. It was proposed to use the algorithm as a tool to support the work of shippers to obtain cost-effective transport orders from electronic transport exchanges.

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
B. F. Silva ◽  
P. S. Sausen ◽  
A. Sausen

This paper presents a proposition of a new optimization method called Search in Improved Network, which is an extension of the method Search in Modified Network, to calculate the empirical parameters of the Rakhmatov and Vrudhula model for predicting batteries lifetime used in mobile devices. The new method is evaluated according to the following methodology. At first empirical parameters are computed considering the optimization methods Search in Improved Network, Search in Modified Network, and Least Squares, as well as the experimental data obtained from a testbed, considering a Lhition-Ion battery, model BL5F, used in the Nokia N95 cell phone. In a second moment, the Rakhmatov and Vrudhula model is validated for each set of parameters obtained, and the simulated data from the model are compared with a set of experimental data. From simulations results a comparative analysis is performed and it is found that by the application of the method Search in Improved Network in the parameters estimation of the Rakhmatov and Vrudhula model it is possible to obtain an easy and intuitive implementation, improving the results obtained in the model accuracy, as well as preserving the runtime.


Author(s):  
Nikolas Antonakis

An optimization process is employed to improve the performance of an industrial radial flow pump impeller. A hybrid optimization scheme is coupled to a cost effective potential flow solver that computes the flow through the blade channel. The impeller geometry is parameterized to reproduce variations over a wide design space and the objective function is evaluated at each iteration to account for the performance of each candidate blade. The concept of the hybrid approach is to employ a global stochastic optimization method for the diversification of the design space and a deterministic local method for efficiently intensifying the search towards the optimum. The trend of computer industry to multi-core processors is a promising platform for population based optimization methods and even on a quad core the timescales needed to solve the problem with the proposed methodology are reasonable. Results present an optimised impeller with improved performance but also a clear trade-off among contradictory design objectives.


Author(s):  
A.I. Glushchenko ◽  
M.Yu. Serov

В статье рассматривается вопрос совершенствования системы управления параллельно-работающими насосными агрегатами с целью повышения энергоэффективности их работы. Проведено сравнение и выявление недостатков существующих методов решения рассматриваемой проблемы. Предложена идея нового подхода на базе онлайн оптимизации. The problem under consideration is improvement of the energy efficiency of a control system of parallel-running pump units. Known methods used to solve this problem are considered. Their advantages and disadvantages are shown. Finally, the idea of a new approach, which is based on online optimization, is proposed.


Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.


2021 ◽  
Vol 11 (15) ◽  
pp. 7168
Author(s):  
Fatemeh Shahbazi ◽  
Mohammad Souri ◽  
Masoud Jabbari ◽  
Amir Keshmiri

Biosensors are favored devices for the fast and cost-effective detection of biological species without the need for laboratories. Microfluidic integration with biosensors has advanced their capabilities in selectivity, sensitivity, controllability, and conducting multiple binding assays simultaneously. Despite all the improvements, their design and fabrication are still challenging and time-consuming. The current study aims to enhance microfluidic-integrated biosensors’ performance. Three different functional designs are presented with both active (with the help of electroosmotic flow) and passive (geometry optimization) methods. For validation and further studies, these solutions are applied to an experimental setup for DNA hybridization. The numerical results for the original case have been validated with the experimental data from previous literature. Convection, diffusion, migration, and hybridization of DNA strands during the hybridization process have been simulated with finite element method (FEM) in 3D. Based on the results, increasing the velocity on top of the functionalized surface, by reducing the thickness of the microchamber in that area, would increase the speed of surface coverage by up to 62%. An active flow control with the help of electric field would increase this speed by 32%. In addition, other essential parameters in the fabrication of the microchamber, such as changes in pressure and bulk concentration, have been studied. The suggested designs are simple, applicable and cost-effective, and would not add extra challenges to the fabrication process. Overall, the effect of the geometry of the microchamber on the time and effectiveness of biosensors is inevitable. More studies on the geometry optimization of the microchamber and position of the electrodes using machine learning methods would be beneficial in future works.


2015 ◽  
Vol 807 ◽  
pp. 247-256 ◽  
Author(s):  
Lena C. Altherr ◽  
Thorsten Ederer ◽  
Philipp Pöttgen ◽  
Ulf Lorenz ◽  
Peter F. Pelz

Cheap does not imply cost-effective -- this is rule number one of zeitgeisty system design. The initial investment accounts only for a small portion of the lifecycle costs of a technical system. In fluid systems, about ninety percent of the total costs are caused by other factors like power consumption and maintenance. With modern optimization methods, it is already possible to plan an optimal technical system considering multiple objectives. In this paper, we focus on an often neglected contribution to the lifecycle costs: downtime costs due to spontaneous failures. Consequently, availability becomes an issue.


Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2963
Author(s):  
Melinda Timea Fülöp ◽  
Miklós Gubán ◽  
György Kovács ◽  
Mihály Avornicului

Due to globalization and increased market competition, forwarding companies must focus on the optimization of their international transport activities and on cost reduction. The minimization of the amount and cost of fuel results in increased competition and profitability of the companies as well as the reduction of environmental damage. Nowadays, these aspects are particularly important. This research aims to develop a new optimization method for road freight transport costs in order to reduce the fuel costs and determine optimal fueling stations and to calculate the optimal quantity of fuel to refill. The mathematical method developed in this research has two phases. In the first phase the optimal, most cost-effective fuel station is determined based on the potential fuel stations. The specific fuel prices differ per fuel station, and the stations are located at different distances from the main transport way. The method developed in this study supports drivers’ decision-making regarding whether to refuel at a farther but cheaper fuel station or at a nearer but more expensive fuel station based on the more economical choice. Thereafter, it is necessary to determine the optimal fuel volume, i.e., the exact volume required including a safe amount to cover stochastic incidents (e.g., road closures). This aspect of the optimization method supports drivers’ optimal decision-making regarding optimal fuel stations and how much fuel to obtain in order to reduce the fuel cost. Therefore, the application of this new method instead of the recently applied ad-hoc individual decision-making of the drivers results in significant fuel cost savings. A case study confirmed the efficiency of the proposed method.


2021 ◽  
Vol 13 (4) ◽  
pp. 707
Author(s):  
Yu’e Shao ◽  
Hui Ma ◽  
Shenghua Zhou ◽  
Xue Wang ◽  
Michail Antoniou ◽  
...  

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.


2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.


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