A Novel Integrated Fuzzy-Rough MCDM Model for Evaluation of Companies for Transport of Dangerous Goods

The organization and execution of the transport of dangerous goods is conditioned by a series of legal, technical, technological, safety, and engineering requirements, which must be met. In this way, a complex system is created which has a large number of participants and in which optimization should be performed at each stage from all the above aspects. The main goal of this paper is to create a novel Fuzzy-Rough MCDM (Multiple-Criteria Decision-Making) for the evaluation of companies engaged in the transport of dangerous goods. A group decision-making model was created to evaluate 11 transport companies based on nine legal, technical, technological criteria. The improved fuzzy stepwise weight assessment ratio analysis (IMF SWARA) method was used to calculate the criterion weights, while transport companies were ranked based on Rough Measurement Alternatives and Ranking according to the COmpromise Solution (R-MARCOS). The integration of these methods into a single model that combines two theories of uncertainty, fuzzy and rough, was performed for the first time in this study, which represents a significant contribution. The results show that the most significant criteria are as follows: dangerous goods are classified and permitted under ADR (Agreement Concerning the International Carriage of Dangerous Goods by Road), the prescribed documents are in the transport unit, and the equipment is in the transport unit. When it comes to the evaluation results of companies, it can be noticed that A1 and A4 show the best performance, while A8 and A9 are in the last position. In order to test the stability of the model developed, sensitivity analysis, comparative analysis, and the influence of the dynamic formation of the initial matrix were created

A Novel Integrated Fuzzy-Rough MCDM Model for Evaluation of Companies for Transport of Dangerous Goods

The organization and execution of the transport of dangerous goods is conditioned by a series of legal, technical, technological, safety, and engineering requirements, which must be met. In this way, a complex system is created which has a large number of participants and in which optimization should be performed at each stage from all the above aspects. The main goal of this paper is to create a novel Fuzzy-Rough MCDM (Multiple-Criteria Decision-Making) for the evaluation of companies engaged in the transport of dangerous goods. A group decision-making model was created to evaluate 11 transport companies based on nine legal, technical, technological criteria. The improved fuzzy stepwise weight assessment ratio analysis (IMF SWARA) method was used to calculate the criterion weights, while transport companies were ranked based on Rough Measurement Alternatives and Ranking according to the COmpromise Solution (R-MARCOS). The integration of these methods into a single model that combines two theories of uncertainty, fuzzy and rough, was performed for the first time in this study, which represents a significant contribution. The results show that the most significant criteria are as follows: dangerous goods are classified and permitted under ADR (Agreement Concerning the International Carriage of Dangerous Goods by Road), the prescribed documents are in the transport unit, and the equipment is in the transport unit. When it comes to the evaluation results of companies, it can be noticed that A1 and A4 show the best performance, while A8 and A9 are in the last position. In order to test the stability of the model developed, sensitivity analysis, comparative analysis, and the influence of the dynamic formation of the initial matrix were created

Application of Statistical Analysis for Risk Estimate of Railway Accidents and Traffic Incidents at Level Crossings

Abstract This paper deals with applying statistical analysis of traffic safety to analyze the risks at level crossings. The social costs of railway accidents and traffic incidents at level crossings are very high and lead to a reduction in the levels of traffic safety. In addition to the consequences reflected in the loss of human lives, injuries, and disabilities, the stress and trauma of direct participants in traffic, accidents, and incidents at level crossings cause huge property and economic losses and significant primary and secondary traffic delays. The traffic safety analysis was conducted on 2128 level crossings, which are differently protected on the lines Joint Stock Company for Public Railway Infrastructure Management ”Serbian Railway Infrastructure” with statistical data of accidents in the Republic of Serbia from 2007 to 2017. The paper analyzes the obtained results using the methods of descriptive and inferential statistics to define measures of possible improvement of safety levels at the obtained critical level crossings. Also, a proposal was made for improving and raising the level of safety on level crossings through innovative education of direct participants in traffic

Neuro-fuzzy inference systems approach to decision support system for economic order quantity

Supply chain management (SCM) has a dynamic structure involving the constant flow of information, product, and funds among different participants. SCM is a complex process and most often characterized by uncertainty. Many values are stochastic and cannot be precisely determined and described by classical mathematical methods. Therefore, in solving real and complex problems individual methods of artificial intelligence are increasingly used, or their combination in the form of hybrid methods. This paper has proposed the decision support system for determining economic order quantity and order implementation based on Adaptive neuro-fuzzy inference systems - ANFIS. A combination of two concepts of artificial intelligence in the form of hybrid neuro-fuzzy method has been applied into the decision support system in order to exploit the individual advantages of both methods. This method can deal with complexity and uncertainty in SCM better than classical methods because they it stems from experts’ opinions. The proposed decision support system showed good results for determining the amount of economic order and it is presented as a successful tool for planning in SCM. Sensitivity analysis has been applied, which indicates that the decision sup- port system gives valid results. The proposed system is flexible and can be applied to various types of goods in SC

New Analytic Solutions of Queueing System for Shared-Short Lanes at Unsignalized Intersections

Designing the crossroads capacity is a prerequisite for achieving a high level of service with the same sustainability in stochastic traffic flow. Also, modeling of crossroad capacity can influence on balancing (symmetry) of traffic flow. Loss of priority in a left turn and optimal dimensioning of shared-short line is one of the permanent problems at intersections. A shared-short lane for taking a left turn from a priority direction at unsignalized intersections with a homogenous traffic flow and heterogeneous demands is a two-phase queueing system requiring a first in-first out (FIFO) service discipline and single-server service facility. The first phase (short lane) of the system is the queueing system M(p lambda)/M(mu)/1/infinity, whereas the second phase (shared lane) is a system with a binomial distribution service. In this research, we explicitly derive the probability of the state of a queueing system with a short lane of a finite capacity for taking a left turn and shared lane of infinite capacity. The presented formulas are under the presumption that the system is Markovian, i.e., the vehicle arrivals in both the minor and major streams are distributed according to the Poisson law, and that the service of the vehicles is exponentially distributed. Complex recursive operations in the two-phase queueing system are explained and solved in manuscript

Numerical Model of Fragmentation Hazards Caused by a Tank Explosion

The paper analyses the fragmentation of a horizontal cylindrical tank caused by the effect of boiling liquid expanding vapour explosion (BLEVE). A fragmentation model for identification of kinematic parameters is proposed. The originality of the model lies in the introduction of initial acceleration. Using this model, the initial velocity can be assessed without knowing the values of explosion energy and the mass of fragments. The application of this model reduces the uncertainty in assessing the range of fragments and the risk of fragmentation. The initial acceleration of fragments generated in an explosion is assessed according to the geometry and type of the tank material. The initial acceleration, which does not depend on the kinematic parameters of the constant wall thickness of the tank, allows a reliable assessment of the launch angle of a fragment. Characteristic forms of the fragment trajectory are identified, depending on the aerodynamic and thrust acceleration coefficients, and probability distributions of the fragment ranges are given. Relevant factors in the assessment of fragmentation hazards include the trajectory of a fragment, the height of a target and its distance from the tank. It was concluded that aerodynamic fragments at distances of up to 50 m are not a danger to targets of up to 15 m high. Fragments with high air resistance and low thrust can endanger targets of the same height at distances of over 200 m. The presented fragmentation model includes the effect of heating due to the BLEVE effect and can be applied to all types of tanks

A new methodology for treating problems in the field of traffic safety: case study of Libyan cities

Traffic safety is an area of great importance, since there are many traffic accidents every day in which a significant number of people are killed. Defining certain strategies and identifying potentially the most dangerous towns and cities regarding this area are, on the one hand, a necessity, and, on the other hand, a challenge. In this paper, integrated Multi-Criteria Decision-Making (MCDM) model for ranking cities in Libya from the aspect of traffic safety has been proposed. The model implies a set of 8 criteria on the basis of which 5 decision-makers rated the 10 most deprived cities in Libya. The Full Consistency Model (FUCOM) in combination with the rough Dombi aggregator is used to determine the significance of the criteria. The Rough Simple Additive Weighting (R-SAW) method is used to rank the alternatives. The rough Dombi aggregator is also used for averaging in group decision-making while evaluating the alternatives. The stability of the model and the obtained results has been verified by the sensitivity analysis, which implies a 2-phase procedure. In the 1st phase, rough Additive Ratio Assessment (R-ARAS), Rough Weighted Aggregated Sum Product Assessment (R-WASPAS), Rough Complex Proportional Assessment (R-COPRAS) and Rough Multi-Attributive Border Approximation-area Comparison (R-MABAC) methods are applied. The 2nd phase implies changing the parameter ρ in the procedure of rough Dombi aggregator, while the 3rd phase includes the calculation of Spearman’s Correlation Coefficient (SCC) that shows a high correlation of ranks

Evaluation of a Third-Party Logistics (3PL) Provider Using a Rough SWARA–WASPAS Model Based on a New Rough Dombi Aggregator

For companies active in various sectors, the implementation of transport services and other logistics activities has become one of the key factors of efficiency in the total supply chain. Logistics outsourcing is becoming more and more important, and there is an increasing number of third party logistics providers. In this paper, logistics providers were evaluated using the Rough SWARA (Step-Wise Weight Assessment Ratio Analysis) and Rough WASPAS (Weighted Aggregated Sum Product Assessment) models. The significance of the eight criteria on the basis of which evaluation was carried out was determined using the Rough SWARA method. In order to allow for a more precise consensus in group decision-making, the Rough Dombi aggregator was developed in order to determine the initial rough matrix of multi-criteria decision-making. A total of 10 logistics providers dealing with the transport of dangerous goods for chemical industry companies were evaluated using the Rough WASPAS approach. The obtained results demonstrate that the first logistics provider is also the best one, a conclusion confirmed by a sensitivity analysis comprised of three parts. In the first part, parameter ρ was altered through 10 scenarios in which only alternatives four and five change their ranks. In the second part of the sensitivity analysis, a calculation was performed using the following approaches: Rough SAW (Simple Additive Weighting), Rough EDAS (Evaluation Based on Distance from Average Solution), Rough MABAC (MultiAttributive Border Approximation Area Comparison), and Rough TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). They showed a high correlation of ranks determined by applying Spearman’s correlation coefficient in the third part of the sensitivity analysis

A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM)

In this paper, a new multi-criteria problem solving method—the Full Consistency Method (FUCOM)—is proposed. The model implies the definition of two groups of constraints that need to satisfy the optimal values of weight coefficients. The first group of constraints is the condition that the relations of the weight coefficients of criteria should be equal to the comparative priorities of the criteria. The second group of constraints is defined on the basis of the conditions of mathematical transitivity. After defining the constraints and solving the model, in addition to optimal weight values, a deviation from full consistency (DFC) is obtained. The degree of DFC is the deviation value of the obtained weight coefficients from the estimated comparative priorities of the criteria. In addition, DFC is also the reliability confirmation of the obtained weights of criteria. In order to illustrate the proposed model and evaluate its performance, FUCOM was tested on several numerical examples from the literature. The model validation was performed by comparing it with the other subjective models (the Best Worst Method (BWM) and Analytic Hierarchy Process (AHP)), based on the pairwise comparisons of the criteria and the validation of the results by using DFC. The results show that FUCOM provides better results than the BWM and AHP methods, when the relation between consistency and the required number of the comparisons of the criteria are taken into consideration. The main advantages of FUCOM in relation to the existing multi-criteria decision-making (MCDM) methods are as follows: (1) a significantly smaller number of pairwise comparisons (only n − 1), (2) a consistent pairwise comparison of criteria, and (3) the calculation of the reliable values of criteria weight coefficients, which contribute to rational judgment