Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China

A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO.

/pdf/caconet-ant-colony-optimization-aco-based-clustering-1ccif6tn92.pdf

CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET

Over the last two decades, application of Data envelopment analysis (DEA) in transportation problems have gained considerable research attention. This paper presents a literature review and classification of the applications of DEA in transportation systems (TSs). First by classifying 40 papers from 2007 to 2018, the origins of DEA in transportation problems have been reviewed. Then the development and an overall view of DEA applications in TSs have been presented. We have classified the applications of DEA into six different contexts. In each context, published papers have deeply been analyzed. Content of analysis includes “Number of published papers during the time”, “target journals”, “countries”, “keyword frequency”, “most cited papers”, “map of most co-cited publications”. More important, we reported the “inputs and outputs variables” used in each paper. Further “a review of the selected papers” and “gaps/future research directions” have been given within each cluster. The results show that DEA is one of the most useful approach in evaluating TSs for policy makers. On the other hand, DEA can help the decision makers in transportation especially regarding environmental factors, sustainable development and eco-design. Finally, we proposed subjects for future researches including guidance for new studies in the field of DEA applications in TSs.

/pdf/the-origins-development-and-future-directions-of-data-47yahk0lwq.pdf

The origins, development and future directions of data envelopment analysis approach in transportation systems

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. This chapter provides a taxonomy and review of the fuzzy DEA (FDEA) methods. We present a classification scheme with six categories, namely, the tolerance approach, the α-level based approach, the fuzzy ranking approach, the possibility approach, the fuzzy arithmetic, and the fuzzy random/type-2 fuzzy set. We discuss each classification scheme and group the FDEA papers published in the literature over the past 30 years.

The State of the Art in Fuzzy Data Envelopment Analysis

Since Vehicular ad hoc networks (VANETs) are vulnerable to various kinds of attacks, there is a need to fulfill the security requirements like message privacy, integrity, and authentication. The authentication technique is said to be efficient if it detects compromised nodes accurately with less complexity, reduced authentication delay, and keying overhead. In this paper, a trust-based authentication scheme for cluster-based VANETs is proposed. The vehicles are clustered, and the trust degree of each node is estimated. The trust degree is a combination of direct trust degree and indirect trust degree. Based on this estimated trust degree, cluster heads are selected. Then, each vehicle is monitored by a set of verifiers, and the messages are digitally signed by the sender and encrypted using a public/ private key as distributed by a trusted authority and decrypted by the destination. This verifies the identity of sender as well as receiver thus providing authentication to the scheme. By simulation results, we prove that the proposed technique provides high security with less overhead and delay.

Trust based authentication technique for cluster based vehicular ad hoc networks (VANET)

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.

Fuzzy dynamic multi-objective Data Envelopment Analysis model

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.

New clustering algorithms for vehicular ad-hoc network in a highway communication environment

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.

Multi-objective data envelopment analysis model in fuzzy dynamic environment with missing values.

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.

Two New Clustering Algorithms for Vehicular Ad-Hoc Network Based on Ant Colony System

In the railway industry, making a time management schedule for freight and passenger train plays a vital role in optimal use of railway resources such as tracks, fleet and human resources. In the last few years, there has been a growing interest in using non-periodic mathematical models for scheduling of train movements between researches. In the main mathematical model of train scheduling, which is used in a variety of single-objective and multi-objective models, there are some constraints that prevent the collision of trains with each other. The present study tries to take a closer look at the models and reduce these constraints. To this end, in this article an improved non-periodic train scheduling model is suggested by presenting a new definition of headway time. Our claims and results of the improved and the main models are evaluated using two hypothetical and one real examples. The results illustrate that the new proposed model, which has less constraints and complexity, works more effectively than the main model and is more suitable for solving real train scheduling problems.

Ahmad Reza Jafarian-Moghaddam

Papers

Fuzzy dynamic multi-objective Data Envelopment Analysis model

New clustering algorithms for vehicular ad-hoc network in a highway communication environment

Multi-objective data envelopment analysis model in fuzzy dynamic environment with missing values.

Two New Clustering Algorithms for Vehicular Ad-Hoc Network Based on Ant Colony System

An Effective Improvement to Main Non-periodic Train Scheduling Models by a New Headway Definition