Various disasters with serious results are reported and happen around our lives. Most of them are unconventional contingency events. Emergency relief is an important activity dealing with the disasters to transfer a large number of materials to the destroyed places for casualties and reconstruction for the country, even the world. Container multimodal transportation will play an important role because of its superior characteristics. The relations in the process of supply and transportation build up the container supply chain. It is critical to schedule the multimodal transportation flow of the chain with time efficiency of higher reliability. In this study, the system of container multimodal transportation emergency relief is modeled as an affinity network inspired by the immune system. An integer linear programming model is proposed to build the path selection for container supply chain in the context of emergency relief. The simulation study shows the promising effects of the model. The study is valuable for designing the emergency logistics management system with optimal path selection and flow design of container supply chain in disaster environments.

A container multimodal transportation scheduling approach based on immune affinity model for emergency relief

This paper aims to compare the descriptive and predictive power of two classes of statistical estimation models for multimodal network flows, viz. the logit model and the neural network model. The application concerns a large data set on inter-regional European freight flows for two commodity categories (food and chemicals). After an exposition of policy issues, methodological and modelling questions and the database, a variety of experiments is carried out. The results show that in general the predictive potential of neural network models is higher than that of logit analysis. The statistical results are also used to investigate the implications of various road tax systems (e.g., e&axes) on various segments of the European road network

Comparative Modelling of Interregional Transport Flows Applications to Multimodal European Freight Transport

Recently, extensive studies have been carried out on web service standards because of the necessity of developing large-scale applications in open environments. In particular, they enable services to be dynamically bound. However, current techniques fail to address the critical problem of selecting the right service instances. Service selection should be determined based on customer preferences and service level. We propose a best web service selection method which helps to find a service provider providing the optimal quality. Web service selection process was described with multi-criteria decision making approach(e.g. PROMETHEE) on the basis of evaluated values of qualities and the defined service level. The PROMETHEE method has advantages in comparison with the others(e.g. MAUT, AHP) as follows. First, the PROMETHEE method classifies alternatives which is difficult to be compared because of a trade-off relation of evaluation standards as non-comparable alternatives. Second, the PROMETHEE method is different from the AHP method in that there's no need to perform a pair-wise comparison again when comparative alternatives are added or deleted. Therefore, this method is a suitable approach in the web service selection problem. Because the problem has a lot of quality parameters which are measured and evaluated at the same time and frequently induces a drop of another quality parameter by the improvement of one quality attribute. Consequently, our approach enables applications to be dynamically configured at runtime in a manner that continually adapts to the preferences of the customers. We verify our approach through case study.

Best web service selection based on the decision making between QoS criteria of service

SUMMARY
As a multi-criteria decision-making (MCDM) method, the analytic hierarchy process (AHP) has been used considerably to solve hierarchical or network-based decision problems in socio-economic fields. Following an in-depth explanation of the transport function in logistics and an overview of the MCDM methods, the AHP model is employed in the paper for a logistics company in selecting the most suitable way of transportation between two given locations in Turkey. The criteria used in the selection of transportation modes are identified as the cost, speed, safety, accessibility, reliability, environmental friendliness, and flexibility. Several cost parameters (transportation, storage, handling, bosphorus crossover) are incorporated into the decision-making process. The application is carried out in instructional character. The results of the study indicate that the railway transportation, which is not widely used in Turkey, is also an alternative and suitable means of transportation. Copyright © 2013 John Wiley & Sons, Ltd.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/atr.1240

Analytic hierarchy process application in selecting the mode of transport for a logistics company

Currently, high-income countries use multimodal transportation to export large quantities of secondhand vehicles to low-income countries. Secondhand vehicle export has shown its highest growth in recent years, especially in Korea. The problem of transporting secondhand vehicles from Korea to Central Asia is becoming an important issue, but few researchers are interested in it. The objective of this research is to determine the optimal transport network for exporting secondhand vehicles from Korea to Central Asian countries by combining experts’ opinions and real data from existing transport networks. The fuzzy Delphi method was applied to obtain factors to evaluate alternative multimodal transport networks for moving secondhand vehicles from Korea to Central Asian countries by judgment from experts. The analysis shows that among the five factors (total cost, total time, reliability, security, and transportation capability), total cost is the most considerable factor, followed by reliability, transportation capability, total time, and security. Additionally, in the mainly three multimodal transport networks, the sea+Trans-China Railway route is ranked first, followed by the sea+Trans-Siberian Railway and sea+truck routes.

https://cyberleninka.org/article/n/564353.pdf

A Study on International Multimodal Transport Networks from Korea to Central Asia: Focus on Secondhand Vehicles☆

The ability to efficiently and effectively select the inter-organizational and heterogeneous services is the important support for the development of the Web service technology. Quality of service (QoS) profiles of Web services are crucial in determining which service best addresses the user desires and objectives. This paper developed a general QoS-based service selection method. By importing the proposed QoS ontology into OWL-S standards, the method can express Web service's nonfunctional attributes in a semantic and extensible way. Web service QoS based selection is formulated as a multi-criteria decision making (MCDM) which can be solved by using different MCDM models to evaluate QoS criteria of the candidate Web services. The values of quality parameters of a Web service are normalized to a non-negative real-valued number where higher normalized values represent higher levels of service performance. The proposed approach can be used as a solution for the complexity and reliability web service selection problem.

QoS ontology based efficient web services selection

Multimodal transportation is a complex network, in which all the components should be seamlessly linked and efficiently coordinated. Considered many noncommensurable, nonlinear even conflicting criteria simultaneously, the transport mode selection in multimodal transportation is studied within the framework of multicriteria decision making (MCDM). The theoretical basis for feedforward artificial neural network (FANN) to solve this MCDM problem is presented. With the initial topology predetermined by fuzzy analysis hierarchy process (AHP), an adaptive ANN system is proposed, in which the number of ANN input nodes adapts the decision makerspsila preference threshold and the initial input weights are determined by fuzzy AHP. Empirical results evidently show this MCDM method is an accurate, flexible and efficient transport mode selection model.

A Transport Mode Selection Method for Multimodal Transportation Based on an Adaptive ANN System

With the fast development of World Wide Wed, Web-based applications and services should allow user to get the right personalized information quickly and effectively. User Profile acts a very important role both in web service personalization and information sharing. In this paper, a new user profile model was proposed to store the characteristics and preferences of person. By using intuitionistic fuzzy set, the Intuitionistic Fuzzy User Profile Model (IUM) has ability to handle fuzzy phenomenon and uncertainty information, especially in describing Hesitancy Degree (HD) of people and comparative analysis. IUM can also be shared on different web application. The experimental results show that the proposed approach is effectiveness in personalized information analysis, and this research facilitates the personalized information sharing and reuse on the web application and services.

A new User Profile Model based on Intuitionistic Fuzzy set for personalized information analysis and sharing

More and more data in practice is changing every minute and been collected in incremental mode, and incremental clustering has attracted much of researchers' attention. However, little research now focuses on partitioning categorical data in incremental mode. How to design incremental clustering for categorical data is an urgent problem. We propose an incremental clustering for categorical data using clustering ensemble in this paper. We firstly prune redundant attributes if needed, and then make use of true values of different attributes to form clustering memberships, and next use clustering ensemble to merge or divide clusters to gain optimal clustering. Finally, the proposed algorithm is applied in Yellow-Small dataset, Diagnosis dataset and Zoo dataset and results show that it is effective.

Incremental clustering for categorical data using clustering ensemble

A dynamic combined forecasting model for transport freight volume time series prediction is established. The time-varying combined weights are computed with the Bayesian posterior probability based on each local predictor's performance. This method's forecast performance is reliable, because it tracks the real-time prediction precision of the combined models and adjusts their credit values (weights) according to their past predictive error. The forgetting factor is proposed as a threshold in order to avoid the singular forecasting model's performance change so intensely over different time intervals as to cause unimaginable effect to the latter online weights computation. In error evaluation system, the performance of the proposed dynamic combination forecast model outperforms the singular predictor used respectively as well as some conventional combination forecasting methods.

Lili Qu

Papers

QoS ontology based efficient web services selection

A Transport Mode Selection Method for Multimodal Transportation Based on an Adaptive ANN System

A new User Profile Model based on Intuitionistic Fuzzy set for personalized information analysis and sharing

Incremental clustering for categorical data using clustering ensemble

A Dynamic Combination Forecast Model for Analysis Transport Volume Time Series