WHY FUZZY ANALYTIC HIERARCHY PROCESS APPROACH FOR TRANSPORT PROBLEMS?

The evaluation of transport projects has become increasingly complex. Different aspects have to be taken into account and the consequences of the problems are usually far reaching and the different policy alternatives are numerous and difficult to predict. Several pressure or action groups have also emerged causing an even more complex decision making process. The use of multi criteria analysis for the evaluation of transport projects has increased due to this increasing complexity of the problem situation. At the same time, the importance of stakeholders within this evaluation process should have been recognized. Researches on transport projects are generally carried out to provide information to policymakers that have to operate within restrictive parameters (political, economical, social, etc…). Researchers should therefore take greater account of the different priorities of stakeholders such as policymakers, private enterprises and households. These stakeholders should be incorporated explicitly in the evaluation process. The Analytic Hierarchy Process is one of the Fuzzy Multiple Criteria Decision Making methods. It can be applied in a very broad range of applications of decision problems. Logistics, urban planning, public politics, marketing, finance, education, economics are a part of this wide application area. In transport subjects it can be used for the evaluation of transport policy measures or decision making problems. Due to its wide range application area, it has been an exciting research subject for many different field researchers. The aim of this paper is to introduce AHP method and to offer how to benefit it for the preference of urban planners in transport problems. This paper is composed of two main parts. First part consists of the literature survey regarding with the AHP and its application areas. The advantage of methods had been mentioned. Second part focuses on a sample application of AHP technique. The study uses AHP technique to determine the selection criteria in the transhipment port selection decision-making process. Keywords: Analytic Hierarchy Process, Multi criteria analysis, Transshipment port selection.

An extensible manufacturing resource model for process integration

Driven by industrial needs and enabled by process technology and information technology, enterprise integration is rapidly shifting from information integration to process integration to improve overall performance of enterprises. Traditional resource models are established based on the needs of individual applications. They cannot effectively serve process integration which needs resources to be represented in a unified, comprehensive and flexible way to meet the needs of various applications for different business processes. This paper looks into this issue and presents a configurable and extensible resource model which can be rapidly reconfigured and extended to serve for different applications. To achieve generality, the presented resource model is established from macro level and micro level. A semantic representation method is developed to improve the flexibility and extensibility of the model

Network Selection Problems - QoE vs QoS Who is the Winner?

In network selection problem (NSP), there are now two schools of thought. There are those who think using QoE (Quality of Experience) is the best yardstick to measure the suitability of a Candidate Network (CN) to handover to. On the other hand, Quality of Service (QoS) is also advocated as the solution for network selection problems. In this article, a comprehensive framework that supports effective and efficient network selection is presented. The framework   attempts to provide a holistic solution to network selection problem that is achieved by combining both of the QoS and QoE measures.   Using this hybrid solution the best qualities in both methods are combined to overcome issues of the network selection problem According to ITU-R (International Telecommunications Union – Radio Standardization Sector), a 4G network is defined as having peak data rates of 100Mb/s for mobile nodes with speed up to 250 km/hr and 1Gb/s for mobile nodes moving at pedestrian speed. Based on this definition, it is safe to say that mobile nodes that can go from pedestrian speed to speed of up to 250 km/hr will be the norm in future. This indicates that the MN’s mobility will be highly dynamic. In particular, this article addresses the issue of network selection for high speed Mobile Nodes (MN) in 4G networks. The framework presented in this article also discusses how the QoS value collected from CNs can be fine-tuned to better reflect an MN’s current mobility scenario

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Deriving a preference-based measure for cancer using the EORTC QLQ-C30 : a confirmatory versus exploratory approach

Background: To derive preference-based measures from various condition-specific descriptive health-related quality of life (HRQOL) measures. A general 2-stage method is evolved: 1) an item from each domain of the HRQOL measure is selected to form a health state classification system (HSCS); 2) a sample of health states is valued and an algorithm derived for estimating the utility of all possible health states. The aim of this analysis was to determine whether confirmatory or exploratory factor analysis (CFA, EFA) should be used to derive a cancer-specific utility measure from the EORTC QLQ-C30. Methods: Data were collected with the QLQ-C30v3 from 356 patients receiving palliative radiotherapy for recurrent or metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter based on a conceptual model (the established domain structure of the QLQ-C30: physical, role, emotional, social and cognitive functioning, plus several symptoms) and clinical considerations (views of both patients and clinicians about issues relevant to HRQOL in cancer). The dimensions determined by each method were then subjected to item response theory, including Rasch analysis. Results: CFA results generally supported the proposed conceptual model, with residual correlations requiring only minor adjustments (namely, introduction of two cross-loadings) to improve model fit (increment χ2(2) = 77.78, p 75% observation at lowest score), 6 exhibited misfit to the Rasch model (fit residual > 2.5), none exhibited disordered item response thresholds, 4 exhibited DIF by gender or cancer site. Upon inspection of the remaining items, three were considered relatively less clinically important than the remaining nine. Conclusions: CFA appears more appropriate than EFA, given the well-established structure of the QLQ-C30 and its clinical relevance. Further, the confirmatory approach produced more interpretable results than the exploratory approach. Other aspects of the general method remain largely the same. The revised method will be applied to a large number of data sets as part of the international and interdisciplinary project to develop a multi-attribute utility instrument for cancer (MAUCa)

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations