Multi-Step Heuristic Method for Bus Terminal Location Problem

2019 ◽  
Vol 2673 (10) ◽  
pp. 361-369
Author(s):  
Mohammad Hossein Zamanian ◽  
Farideddin Peiravian
Keyword(s):  
Land Use ◽  
Location Problem ◽  
Transportation Network ◽  
Heuristic Method ◽  
Programming Approach ◽  
Traffic Patterns ◽  
Construction Costs ◽  
Proposed Model ◽  
Node Connectivity ◽  
Increasing Demand

Bus terminals are one of the main facilities that have a key role in the collection and distribution of passengers within an urban transportation network. The location of a bus terminal strongly affects its performance. Owing to the increasing demand, it is sometimes necessary to add a new bus terminal to the existing urban bus network. Finding a proper location, however, is a challenge that is influenced by different transportation and socio-economic considerations, which in turn affects the surrounding land-use and traffic patterns. In this paper, a new multi-step heuristic method is proposed for the bus terminal location problem to identify a new bus terminal location based on the existing network as well as other transportation considerations and constraints. This is achieved by identifying the existing bus stops that have the greatest potential to be turned into a bus terminal. Other factors taken into consideration are the locations of the existing bus terminals, adjacent land-use, construction costs, node connectivity, and system accessibility. Owing to the multi-objective nature of the problem, a goal programming approach is used to formulate the objective function. To evaluate the proposed model, it was applied to the city of Shiraz in Iran. The results show that the model can provide acceptable and reliable outcomes.

scholarly journals A New Heuristic Method for Transportation Network and Land Use Problem

2016 ◽  
Vol 8 (3) ◽  
pp. 94 ◽  
Author(s):  
Mouhamadou A.M.T. Bald ◽  
Babacar M. Ndiaye
Keyword(s):  
Land Use ◽  
Transportation Network ◽  
Heuristic Method ◽  
Optimization Techniques ◽  
Geographical Information ◽  
Mixed Integer ◽  
Individual Agent ◽  
The Road ◽  
Agent Simulation ◽  
Network Costs

Our paper deals with the Transportation Network and Land Use (TNLU) problem.  It consists in finding, simultaneously, the best location of urban area activities, as well as of the road network design that may minimize the moving cost in the network, and the network costs. We propose a new mixed integer programming formulation of the problem, and a new heuristic method for the resolution of TNLU. Then, we give a methodology to find locations or relocations of some Dakar region amenities (home, shop, work and leisure places), that may reduce travel time or travel distance. The proposed methodology mixes multi-agent simulation with combinatorial optimization techniques; that is individual agent strategies versus global optimization using Geographical Information System. Numerical results which show the effectiveness of the method,  and simulations based on the scenario of Dakar city are given.

Fusing Dynamic Images and Depth Motion Maps for Action Recognition in Surveillance Systems

2019 ◽  
Vol 09 ◽  
Author(s):  
Rajat Khurana ◽  
Alok Kumar Singh Kushwaha
Keyword(s):  
Action Recognition ◽  
Depth Map ◽  
Good Choice ◽  
Surveillance Systems ◽  
Activity Detection ◽  
Learning Capability ◽  
Proposed Model ◽  
Care Activity ◽  
Increasing Demand ◽  
Depth Motion Maps

Background & Objective: Identification of human actions from video has gathered much attention in past few years. Most of the computer vision tasks such as Health Care Activity Detection, Suspicious Activity detection, Human Computer Interactions etc. are based on the principle of activity detection. Automatic labelling of activity from videos frames is known as activity detection. Motivation of this work is to use most out of the data generated from sensors and use them for recognition of classes. Recognition of actions from videos sequences is a growing field with the upcoming trends of deep neural networks. Automatic learning capability of Convolutional Neural Network (CNN) make them good choice as compared to traditional handcrafted based approaches. With the increasing demand of RGB-D sensors combination of RGB and depth data is in great demand. This work comprises of the use of dynamic images generated from RGB combined with depth map for action recognition purpose. We have experimented our approach on pre trained VGG-F model using MSR Daily activity dataset and UTD MHAD Dataset. We achieve state of the art results. To support our research, we have calculated different parameters apart from accuracy such as precision, F score, recall. Conclusion: Accordingly, the investigation confirms improvement in term of accuracy, precision, F-Score and Recall. The proposed model is 4 Stream model is prone to occlusion, used in real time and also the data from the RGB-D sensor is fully utilized.

Designing a sustainable supply chain network for producing high-value products from waste glass

2021 ◽  
pp. 0734242X2199466
Author(s):  
Naeme Zarrinpoor
Keyword(s):  
Supply Chain ◽  
Greenhouse Gas Emission ◽  
Fuzzy Programming ◽  
Solution Procedure ◽  
Waste Glass ◽  
Worker Safety ◽  
Programming Approach ◽  
Supply Chain Network ◽  
Sustainable Supply Chain ◽  
Proposed Model

This paper aims to design a supply chain network for producing double glazed glass from the recycling of waste glass. All three pillars of sustainability are taken into consideration. The economic objective tries to maximize total profits. The environmental objective considers the energy consumption, the generated waste, the greenhouse gas emission, the water consumption, and the fuel consumption of vehicles. The social objective addresses created job opportunities, the worker safety, the regional development, the worker benefit, and training hours. To solve the model, a two-stage framework based on the group best-worst method and an interactive fuzzy programming approach is developed. The proposed model is validated through a real case study based on waste glass management in the city of Shiraz. It is revealed that when sustainable development goals are approached, a great degree of improvement will be attained in environmental and social aspects without a significant decrease in the economic sustainability. The results also demonstrate that the locations of glass recycling centres are different under economic, environmental, and social pillars, and the proposed model yields an optimal system configuration with a proper satisfaction degree of all objectives. Moreover, applying the proposed solution procedure enables system designers to obtain the most desirable trade-off between different aspects of sustainability.

scholarly journals Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

2021 ◽  
Vol 13 (11) ◽  
pp. 2166
Author(s):  
Xin Yang ◽  
Rui Liu ◽  
Mei Yang ◽  
Jingjue Chen ◽  
Tianqiang Liu ◽  
...  
Keyword(s):  
Land Use ◽  
Hybrid Model ◽  
Landslide Susceptibility ◽  
Spatial Information ◽  
Landslide Susceptibility Mapping ◽  
Ratio Method ◽  
Support Vector ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Proposed Model

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

scholarly journals STUDY AND APPLY ACO ALGORITHM IN TIME-COST OPTIMIZATION OF CONSTRUCTION PROJECT

2010 ◽  
Vol 13 (1) ◽  
pp. 17-30
Author(s):  
Luan Hong Pham ◽  
Nhan Thanh Duong
Keyword(s):  
Cost Optimization ◽  
Construction Project ◽  
Heuristic Method ◽  
Ant Colony ◽  
Ant Colony System ◽  
Time Cost ◽  
Project Duration ◽  
Proposed Model ◽  
Feasible Solutions ◽  
Computational Resources

Time-cost optimization problem is one of the most important aspects of construction project management. In order to maximize the return, construction planners would strive to optimize the project duration and cost concurrently. Over the years, many researches have been conducted to model the time-cost relationships; the modeling techniques range from the heuristic method and mathematical approach to genetic algorithm. In this paper, an evolutionary-based optimization algorithm known as ant colony optimization (ACO) is applied to solve the multi-objective time-cost problem. By incorporating with the modified adaptive weight approach (MAWA), the proposed model will find out the most feasible solutions. The concept of the ACO-TCO model is developed by a computer program in the Visual Basic platforms. An example was analyzed to illustrate the capabilities of the proposed model and to compare against GA-based TCO model. The results indicate that ant colony system approach is able to generate better solutions without making the most of computational resources which can provide a useful means to support construction planners and managers in efficiently making better time-cost decisions.

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.

scholarly journals KGPMH: A hybrid approach for solving a novel facility location problem

2021 ◽  
Author(s):  
Maryam DehghanChenary ◽  
Arman Ferdowsi ◽  
Fariborz Jolai ◽  
Reza Tavakkoli-Moghaddam
Keyword(s):  
Mathematical Model ◽  
Location Problem ◽  
Hybrid Approach ◽  
Facility Location Problem ◽  
Evaluation Function ◽  
Genetic Approach ◽  
Hub Location ◽  
Hub Location Problem ◽  
Proposed Model ◽  
The Impact

<pre>The focus of this paper is to propose a bi-objective mathematical model for a new extension of a multi-period p-mobile hub location problem and then to devise an algorithm for solving it. The developed model considers the impact of the time spent traveling at the hubs' network, the time spent at hubs for processing the flows, and the delay caused by congestion at hubs with specific capacities. Following the unveiled model, a hybrid meta-heuristic algorithm will be devised that simultaneously takes advantage of a novel evaluation function, a clustering technique, and a genetic approach for solving the proposed model.</pre>

scholarly journals Quantifying the Environmental and Economic Performance of Remote Communities

2019 ◽  
Vol 8 (4) ◽  
pp. 176 ◽  
Author(s):  
Jamie Filer ◽  
Steven Schuldt
Keyword(s):  
Management Practices ◽  
Sustainability Assessment ◽  
Assessment Model ◽  
Remote Site ◽  
Construction Costs ◽  
Remote Communities ◽  
Renewable Energy Systems ◽  
Proposed Model ◽  
Waste Management Practices ◽  
Post Disaster

Remote communities such as oil production sites, post-disaster housing camps, and military forwardoperating bases (FOB) are often detached from established infrastructure grids, requiring a constantresupply of resources. In one instance, a 600-person FOB required 22 trucks per day to delivernecessary fuel and water and remove generated wastes. This logistical burden produces negativeenvironmental impacts and increases operational costs. To minimize these consequences,construction planners can implement sustainability measures such as renewable energy systems,improved waste management practices, and energy-efficient equipment. However, integration ofsuch upgrades can increase construction costs, presenting the need for a tool that identifies tradeoffsamong conflicting criteria. To assist planners in these efforts, this paper presents the development ofa novel remote site sustainability assessment model capable of quantifying the environmental andeconomic performance of a set of infrastructure alternatives. Through field data and literatureestimates, a hypothetical FOB is designed and evaluated to demonstrate the model’s distinctivecapability to accurately and efficiently assess construction alternatives. The proposed model willenable construction planners to maximize the sustainability of remote communities, creating sitesthat are more self-sufficient with reduced environmental impacts.Keywords: Sustainability, infrastructure, remote communities

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