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Sustainable Futures (26661888)
The Vehicle Routing Problem (VRP) model optimizes the routing of a fleet of vehicles to serve customers and has evolved over time. VRP studies have experienced an 11% growth over the past 5 years. In recent years, researchers have been working on a stable VRP model that optimizes routes based on environmental requirements and clean energy needs. Accordingly, we outline the VRP growth trend and present a systematic classification of features based on economic, social, and environmental issues to achieve a sustainable VRP model. The proposed model integrates issues related to passenger and goods transport, Private Vehicles, and Electric Vehicles. © 2024 The Author(s)
PLoS ONE (19326203)(6 June)
Moving toward sustainable transportation is one of the essential issues in cities. Bicycles, as active transportation, are considered an important part of sustainable transportation. However, cyclists engage in more physical activity and air intake, making the quality of air that they inhale important in the programs that aim to improve the share of this mode. This paper develops a multi-modal transportation network design problem (MMNDP) to select links and routes for cycling, cars, and buses to decrease the exposure of cyclists to traffic-generated air pollution. The objective functions of the model include demand coverage, travel time, and exposure. The study also examined the effect of having exclusive lanes for bicycles and buses on the network. In the present study, the non-dominated storing genetic algorithm (NSGA-II) solves the upper-level and a method of successive average (MSA) unravels the lower level of the model. A numerical example and four scenarios evaluate the trade-off between different objective functions of the proposed model. The results reveal that considering exposure to air pollution in our model results in a slight increase in travel time (4%) while the exposure to traffic-generated air pollution for cyclists was reduced significantly (47%). Exclusive lanes also result in exposure reduction in the network (60%). In addition, the demand coverage objective function performs well in increasing the total demand in the network by 47%. However, more demand coverage leads to a rise in travel time by 28% and exposure by 58%. The model also showed an acceptable result in terms of exposure to traffic- generated air pollution compared to the model in the literature. Copyright © 2023 Mortazavi Moghaddam et al.
Urban Rail Transit (21996687)(3)
Speed 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. © 2021, The Author(s).
Applied Soft Computing (15684946)
Train scheduling plays a significant role in the optimal use of the railway resources and satisfaction of customers. This paper presents a novel innovation, which is the elasticity of train length, or the possibility of compression and stretching of the train length in accordance with rail route conditions. The proposed sustainable model provides optimal train scheduling with minimum travel time as well as optimal revenue and cost by developing a balance between length, speed, traction power, and energy consumption of trains while there is the possibility of carrying homogeneous and heterogeneous commodities. Thus, the new model is referred to as the sustainable elastic train scheduling model. Implementing the model in a real case using an introduced genetic algorithm proves its success and increases railway revenues by at least 48 percent with the saving of 25 percent in time. Sensitivity analysis of the model reveals the model is more sensitive to changes in the objective function of travel time. In addition to 50% improvement in using railroad capacity, it is possible to achieve the minimum increase of 71% in revenue. © 2021 Elsevier B.V.
International Journal of Islamic and Middle Eastern Finance and Management (17538394)(3)
Purpose: The financial resources limitation, the difficult conditions for entry into the market and the lack of sufficient funds are the most important problems facing Iranian small and medium enterprises (SMEs). For these reasons, this paper aims to propose an appropriate methodology for formulating the most influential Iranian SMEs development strategies to make it possible to grow and make more income. Then, a framework is developed to precisely determine the target market for Iranian SMEs. Design/methodology/approach: The paper uses strengths, weaknesses, opportunities and threats (SWOT) analysis; Pareto principle and analysis of the market conditions to propose the development strategies and uses a methodology based on multicriteria decision-making (MCDM) method to determine the target market. Findings: According to the research results, it is necessary for the Iranian SMEs to follow the brand strengthening, product and market development, enhancing product quality and creating research and development units strategies focusing on the domestic market. The results obtained from the empirical study also indicated that the customer acquisition rate improved from 0.06 to 0.13 per month, and the company's income has a 64% growth in 2016 than the year 2015 through the selection of some public customers as the target market. Originality/value: Very few studies have been done so far on the formulation methodology of a market entry strategy for SMEs. Studies by researchers imply that no studies have been conducted in Iran in this regard. International studies also mainly focus on the impact of some marketing activities. © 2020, Emerald Publishing Limited.
Wireless Personal Communications (09296212)(4)
Wireless Personal Communications (09296212)(2)
Wireless Networks (10220038)(8)
A vehicular ad hoc network (VANET) is a network in which vehicles acting as dynamic nodes communicate with each other. A VANET is a suitable piece of infrastructure for developing intelligent transportation systems. Stable communication within a VANET leads to enhanced driver safety and better traffic management. The clustering technique, which organizes similar vehicles into similar groups, is a possible method for improving the stability of connectivity within a VANET. In this paper, two new clustering algorithms suited to the dynamic environment of a VANET are proposed. The multi-objective data envelopment analysis clustering algorithm as a mathematical clustering model and the ant system-based clustering algorithm as a meta-heuristic clustering model are introduced as algorithms for VANETs. A comparative simulation study in a highway environment is presented as well to evaluate the introduced methods and compare them with the most commonly used VANET clustering algorithms. The results show that the proposed algorithms offer improved stability and runtime along with relatively better performance than existing algorithms. Furthermore, the results show that in the VANET environment, the mathematical clustering model proposed herein yields better results than the meta-heuristic algorithm. © 2015, Springer Science+Business Media New York.
Wireless Personal Communications (09296212)(1)
In vehicular ad-hoc network (VANET), vehicles are dynamic nodes communicating with each other by wireless technology in their own transmission range. Consequently, with regard to larger communication due to the greater number of vehicles and high mobility of nodes, communication management and creation of a stable network in VANET are most challenging subjects. Hence, clustering as a possible solution to address this challenge, should take into consideration to produce stable clustering structure. Clustering technique is for organizing nodes into groups, making the network more robust and scalable. This paper introduces two new Improved Ant System-based Clustering algorithm (IASC1 and IASC2) suitable for dynamic environment of the VANET. Simulation is run to evaluate the introduced methods and compare them with the most commonly VANET clustering algorithms as found in the literature review. Results reveal the proposed algorithms have improved the stability and the runtime of VANET clustering algorithm and have a relatively good performance compared with other algorithms. © 2015, Springer Science+Business Media New York.
International Journal of Advanced Manufacturing Technology (14333015)(5-8)
Data envelopment analysis (DEA) is an important managerial tool for evaluating and improving the performance of decision making units. The existing DEA models are mostly limited to static environment using crisp data and are time-consuming and also have weak discriminating power. The aim of this work is to introduce a new fuzzy dynamic DEA model with missing values, which benefits from strengths of multi-objective modeling to overcome weakness and drawbacks of the classic DEA models. To check for quality and accuracy of the proposed model, this paper offers a comparative study to compare the discriminating power and computational efforts of the model with two problems in the literature taken as benchmarks. Also, this paper presents a real application of the fuzzy dynamic DEA model for assessing and ranking the level of performance for 56 railways around the globe using real data gathered from credible sources. The numerical case illustrates the model and the result may be used by railways to improve their performance efficiency compared to the best in the sample. Results for the comparative study and the real case reveal significant improvement in computational time and discriminating power. © Springer-Verlag London Limited 2011.
Expert Systems with Applications (09574174)(1)
Data Envelopment Analysis (DEA) is a managerial powerful tool to evaluate the relative efficiency of each decision making unit (DMU). Nowadays, multi-objective DEA models in static environment are an attractive technique for evaluation quantity and quality aspects of performance analysis because there is some weakness in single objective DEA such as one-dimensional performance analysis and also it is important to consider the decision maker(s) preference over the potential adjustments of various inputs and outputs when DEA is employed. In this paper, a fuzzy dynamic multi-objective DEA model is presented in which data are changing sequentially. This paper assesses the performance of the railways using presented model as a numerical example to evaluate the results of the model. Results indicate that the multiple objective program model improves discriminating power of classical DEA models with just one time calculation of the efficiency achievement for all DUMs. © 2010 Elsevier Ltd. All rights reserved.
