scholarly journals Multi-objective optimization of wave break forest design through machine learning

2019 ◽  
Vol 21 (2) ◽  
pp. 295-307
Author(s):  
Jie Ren ◽  
Zengchuan Dong ◽  
Wei Xu ◽  
Qilai Zhang ◽  
Rensheng Shi ◽  
...  
Keyword(s):  
Wave Attenuation ◽  
Cluster Structure ◽  
Field Research ◽  
Physical Experiment ◽  
Feature Weighting ◽  
Attenuation Effect ◽  
Multi Objective ◽  
Physical Experiments ◽  
Proposed Model ◽  
Wave Break

Abstract Planting trees on a floodplain along a river is a practical and ecological method for embankment protection. Optimization of wave break forest is also a new concept on wave attenuation studies. In this study, we carried out physical experiments to obtain fundamental data and proposed the Cluster Structure Preserving Based on Dictionary Pair for Unsupervised Feature Weighting model (CDUFW) for multi-objective wave break forest design. Physical experiments were designed with considering the effects of different planting configurations on wave attenuation in three scenarios: (1) the equilateral triangle arrangement with different row spacings; (2) different arrangements with the same density; (3) different tree shapes with the same row spacing. The physical experiment condition was typically defined according to the field research of the study area. Then, a multi-objective weighting model for wave break forest design optimization was based on the scheme set of physical experiment outputs using the proposed CDUFW model. Physical experiments showed that different arrangement modes take advantage of the wave attenuation effect of different forest widths. The CDUFW model performed well in finding the effective, economic and reasonable scheme. The proposed model is excellent in data mining and classification, and can be applied to many decision-making and evaluation fields.

scholarly journals Wave-attenuation characteristics of combined-vegetation wave break forests for big rivers with large flood water level changes

2021 ◽  
Vol 83 (4) ◽  
pp. 831-840
Author(s):  
Jie Ren ◽  
Zengchuan Dong ◽  
Dawei Jin ◽  
Yue Zhou ◽  
Wei Xu ◽  
...  
Keyword(s):  
Water Level ◽  
Water Depth ◽  
Water Surface ◽  
Wave Attenuation ◽  
Water Levels ◽  
Large Rivers ◽  
Attenuation Effect ◽  
Practical Applications ◽  
Different Types ◽  
Wave Break

Abstract For large rivers with a compound cross section, the downstream channel has a very wide water surface during the flood season. A wide water surface, high water level, and larger wind speed will cause higher waves, increasing the threat of flooding to the dike. The design of a combined-vegetation wave break forest was put forward to achieve better wave attenuation effect. The main idea of this concept is to plant different types of vegetation at different locations in front of the dike. Three single-vegetation and four combined-vegetation forest schemes were tested under seven different water depth conditions. Both physical experiments and wave numerical simulations were carried out for each scheme to study the wave attenuation effect. The results showed that the wave attenuation effect of the single-vegetation wave break forest was significantly different under different water depth conditions, and the overall effect of the combined-vegetation of wave forest was better. Combined-vegetation wave break forests combine the advantages of different types of vegetation in different water levels, which makes it more economical and reasonable to plant by rivers with large water level variation. The proposed design ideas and methods could provide theoretical support for ecological revetment engineering of large rivers and insights for practical applications.

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

Towards a Multi Objective Path Optimisation for Car Navigation

2011 ◽  
Vol 65 (1) ◽  
pp. 125-144 ◽  
Author(s):  
Ching-Sheng Chiu ◽  
Chris Rizos
Keyword(s):  
Selection Criteria ◽  
Empirical Studies ◽  
Route Selection ◽  
Multi Objective ◽  
Optimal Paths ◽  
Proposed Model ◽  
Shortest Distance ◽  
Shortest Path Algorithms ◽  
Car Navigation System ◽  
Car Navigation

In a car navigation system the conventional information used to guide drivers in selecting their driving routes typically considers only one criterion, usually the Shortest Distance Path (SDP). However, drivers may apply multiple criteria to decide their driving routes. In this paper, possible route selection criteria together with a Multi Objective Path Optimisation (MOPO) model and algorithms for solving the MOPO problem are proposed. Three types of decision criteria were used to present the characteristics of the proposed model. They relate to the cumulative SDP, passed intersections (Least Node Path – LNP) and number of turns (Minimum Turn Path – MTP). A two-step technique which incorporates shortest path algorithms for solving the MOPO problem was tested. To demonstrate the advantage that the MOPO model provides drivers to assist in route selection, several empirical studies were conducted using two real road networks with different roadway types. With the aid of a Geographic Information System (GIS), drivers can easily and quickly obtain the optimal paths of the MOPO problem, despite the fact that these paths are highly complex and difficult to solve manually.

A multi-objective supplier selection model for green supply chain network under uncertainty

2018 ◽  
Vol 13 (3) ◽  
pp. 605-625 ◽  
Author(s):  
Mohammad Khalilzadeh ◽  
Hadis Derikvand
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Objective Function Value ◽  
Selection Problem ◽  
Green Supply Chain ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Objective Model ◽  
Supplier Selection Problem

Purpose Globalization of markets and pace of technological change have caused the growing importance of paying attention to supplier selection problem. Therefore, this study aims to choose the best suppliers by providing a mathematical model for the supplier selection problem considering the green factors and stochastic parameters. This paper aims to propose a multi-objective model to identify optimal suppliers for a green supply chain network under uncertainty. Design/methodology/approach The objective of this model is to select suppliers considering total cost, total quality parts and total greenhouse gas emissions. Also, uncertainty is tackled by stochastic programming, and the multi-objective model is solved as a single-objective model by the LP-metric method. Findings Twelve numerical examples are provided, and a sensitivity analysis is conducted to demonstrate the effectiveness of the developed mathematical model. Results indicate that with increasing market numbers and final product numbers, the total objective function value and run time increase. In case that decision-makers are willing to deal with uncertainty with higher reliability, they should consider whole environmental conditions as input parameters. Therefore, when the number of scenarios increases, the total objective function value increases. Besides, the trade-off between cost function and other objective functions is studied. Also, the benefit of the stochastic programming approach is proved. To show the applicability of the proposed model, different modes are defined and compared with the proposed model, and the results demonstrate that the increasing use of recyclable parts and application of the recycling strategy yield more economic savings and less costs. Originality/value This paper aims to present a more comprehensive model based on real-world conditions for the supplier selection problem in green supply chain under uncertainty. In addition to economic issue, environmental issue is considered from different aspects such as selecting the environment-friendly suppliers, purchasing from them and taking the probability of defective finished products and goods from suppliers into account.

scholarly journals A multi-objective approach under uncertainty for designing a green meat supply chain network

2021 ◽  
Author(s):  
Fatemeh Mohebalizadehgashti
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Mixed Integer ◽  
Environmental Concerns ◽  
Supply Chain Network ◽  
Food Supply Chain ◽  
Multi Objective ◽  
Proposed Model

Traditional logistics management has not focused on environmental concerns when designing and optimizing food supply chain networks. However, the protection of the environment is one of the main factors that should be considered based on environmental protection regulations of countries. In this thesis, environmental concerns with a mathematical model are investigated to design and configure a multi-period, multi-product, multi-echelon green meat supply chain network. A multi-objective mixed-integer linear programming formulation is developed to optimize three objectives simultaneously: minimization of the total cost, minimization of the total CO2 emissions released from transportation, and maximization of the total capacity utilization. To demonstrate the efficiency of the proposed optimization model, a green meat supply chain network for Southern Ontario, Canada is designed. A solution approach based on augmented εε-constraint method is developed for solving the proposed model. As a result, a set of Pareto-optimal solutions is obtained. Finally, the impacts of uncertainty on the proposed model are investigated using several decision trees. Optimization of a food supply chain, particularly a meat supply chain, based on multiple objectives under uncertainty using decision trees is a new approach in the literature. Keywords: Meat supply chain; Decision tree; Multi-objective programming; Mixed-integer linear programming; Augmented εε-constraint.

scholarly journals USE OF MOBILE DEVICE SENSORS TO CONDUCT THE PHYSICAL EXPERIMENT

2019 ◽  
pp. 345-354
Author(s):  
Sergii Tereschuk ◽  
Vira Kolmakova
Keyword(s):  
Stem Education ◽  
Mobile Device ◽  
Mobile Application ◽  
Positive Impact ◽  
Physical Experiment ◽  
Science Journal ◽  
Conservation Of Energy ◽  
Physical Experiments ◽  
Physical Laws ◽  
Physical Phenomena

The concept of "sensor" in the system of physical experiment at school is considered in the article. The possibility of using sensors in physics lessons is substantiated: transformation of an input signal into an output is accompanied by transformation of one type of energy into another (according to the law of conservation of energy), and the functioning of the sensors are based on physical phenomena (physical effects or principles), which are described by the relevant physical laws. The article deals with the methodical aspects of using the Google Science Journal mobile application in physics lessons. This application allows you to use the sensors of your mobile device for a physical experiment. As an example we consider the frontal laboratory work "Determination of the period of oscillation of the mathematical pendulum". The method of its carrying out is offered in two approaches: the first one involves the traditional technique of conducting the experiment, and the second approach is using the mobile application Google Science Journal. The article shows that the use of smartphone sensors in physics lessons has perspectives in the context of STEM education. Thus, the use of the considered application is of current importance and requires further scientific and methodological research on its use in the high school physical experimentation system. The Science Journal mobile application can be used to connect external sensors, which will have a positive impact on the introduction of STEM education, and to use Arduino in the demonstration of physical experiments by a physics teacher. Connecting sensors using an Arduino microcontroller is particularly promising in creative lab work on physics.

scholarly journals Vehicle Interaction Behavior Prediction with Self-Attention

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 429
Author(s):  
Linhui Li ◽  
Xin Sui ◽  
Jing Lian ◽  
Fengning Yu ◽  
Yafu Zhou
Keyword(s):  
Cluster Structure ◽  
Class Imbalance ◽  
Behavior Prediction ◽  
Time To Event ◽  
Future Behavior ◽  
Interaction Behavior ◽  
Proposed Model ◽  
High Interaction ◽  
Fully Connected ◽  
High Uncertainty

The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lane gap. In addition, the proposed model utilizes the sliding window algorithm to extract VC behavior information. Then the temporal characteristics of the three interactive features mentioned above will be caught by two layers of self-attention encoder with six heads respectively. Finally, target vehicle’s future behavior will be predicted by a sub-network consists of a fully connected layer and SoftMax module. The experimental results show that this method has achieved accuracy, precision, recall, and F1 score of more than 92% and time to event of 2.9 s on a Next Generation Simulation (NGSIM) dataset. It accurately predicts the interactive behaviors in class-imbalance prediction and adapts to various driving scenarios.

scholarly journals Portfolio Optimization with a Mean–Absolute Deviation–Entropy Multi-Objective Model

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1266
Author(s):  
Weng Siew Lam ◽  
Weng Hoe Lam ◽  
Saiful Hafizah Jaaman
Keyword(s):  
Portfolio Optimization ◽  
Performance Ratio ◽  
Programming Approach ◽  
Mean Absolute Deviation ◽  
Diversification Strategy ◽  
Absolute Deviation ◽  
Multi Objective ◽  
Proposed Model ◽  
The Mean ◽  
Naïve Diversification

Investors wish to obtain the best trade-off between the return and risk. In portfolio optimization, the mean-absolute deviation model has been used to achieve the target rate of return and minimize the risk. However, the maximization of entropy is not considered in the mean-absolute deviation model according to past studies. In fact, higher entropy values give higher portfolio diversifications, which can reduce portfolio risk. Therefore, this paper aims to propose a multi-objective optimization model, namely a mean-absolute deviation-entropy model for portfolio optimization by incorporating the maximization of entropy. In addition, the proposed model incorporates the optimal value of each objective function using a goal-programming approach. The objective functions of the proposed model are to maximize the mean return, minimize the absolute deviation and maximize the entropy of the portfolio. The proposed model is illustrated using returns of stocks of the Dow Jones Industrial Average that are listed in the New York Stock Exchange. This study will be of significant impact to investors because the results show that the proposed model outperforms the mean-absolute deviation model and the naive diversification strategy by giving higher a performance ratio. Furthermore, the proposed model generates higher portfolio mean returns than the MAD model and the naive diversification strategy. Investors will be able to generate a well-diversified portfolio in order to minimize unsystematic risk with the proposed model.

A multi-objective optimization model for logistic planning in the crisis response phase

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Mahdi Ershadi ◽  
Hossein Shams Shemirani
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Crisis Response ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Different Types ◽  
Response Phase ◽  
Logistic Planning

PurposeProper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.Design/methodology/approachThe main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.FindingsThe performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.Practical implicationsThe proposed methodology can be applied to find the best response plan for all crises.Originality/valueIn this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.

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