Using the fuzzy multi-criteria decision making approach for software requirements prioritization

To avoid breach of agreement or contract in software development projects, stakeholders converge to prioritize specified requirements. This is due to the fact that, not all the specified requirements can be implemented in a single release. Therefore, prioritization is the act of rating requirements according to their relative importance by project stakeholders in order to plan for software release phases. The problem of existing prioritization techniques includes computational complexities, ranking inaccuracy and large disparities between final ranks among others. Consequently, this paper presents an improved approach for prioritizing requirements for software projects requirements with stakeholders based on the limitations of existing prioritization techniques using fuzzy multi-criteria decision-making (FMCDM) approach

Modelling travellers’ risky choice behaviour in revealed preference contexts: A comparison of EUT and non-EUT approaches

Recent work on risky choice modelling has sought to address the theoretical shortcomings of expected utility theory (EUT) by using non-expected utility theoretic (non-EUT) approaches. To date, however, there is little evidence to show whether the complexity of non-EUT actually leads to better model performance. Moreover, almost all the relevant research has adopted stated choice data which, although flexible and cheap, has limited validity. This thesis empirically investigates the feasibility and validity of non-EUT approaches in revealed preference (RP) contexts, in which travel time distribution is extracted from historical travel time data to subsequently present systematic comparisons between EUT and non-EUT approaches. Additionally, this thesis also discusses implementations based on these empirical results and, in particular, highlights the influence of non-EUT on the valuation of travel time savings. A risky choice framework is proposed so as to incorporate non-EUT into a Random Utility Maximization structure. The non-EUT approaches modelled in the thesis consist of Subjective Expected Value Theory, Subjective Expected Utility Theory, Weighted Utility theory, Rank Dependent Expected Value, Rank Dependent Expected Utility, Prospect Theory, and Cumulative Prospect Theory. The first dataset is collected from the SR91 corridor in California and involves a choice between a free flowing and reliable tolled facility and a congested and unreliable un-tolled facility. The second case study is based on the London Underground (LU) system and involves the choice between alternative competitive underground services linking pairs of stations. This thesis provides insights into how EUT and non-EUT models perform in the real world. The RP methodology and risky choice framework offers an avenue for future research to identify a wider range of alternative choice theories using realistic data. The empirical results suggest that there are merits in applying non-EUT to the modelling of travellers’ risky choice behaviours.Open Acces

Disruption analytics in urban metro systems with large-scale automated data

Urban metro systems are frequently affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. Such disruptions give rise to delays, congestion and inconvenience for public transport users, which in turn, lead to a wider range of negative impacts on the social economy and wellbeing. This PhD thesis aims to improve our understanding of disruption impacts and improve the ability of metro operators to detect and manage disruptions by using large-scale automated data. The crucial precondition of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. In pursuit of this goal, the thesis develops statistical models to detect disruptions via deviations in trains’ headways relative to their regular services. Our method is a unique contribution in the sense that it is based on automated vehicle location data (data-driven) and the probabilistic framework is effective to detect any type of service interruptions, including minor delays that last just a few minutes. As an important research outcome, the thesis delivers novel analyses of the propagation progress of disruptions along metro lines, thus enabling us to distinguish primary and secondary disruptions as well as recovery interventions performed by operators. The other part of the thesis provides new insights for quantifying disruption impacts and measuring metro vulnerability. One of our key messages is that in metro systems there are factors influencing both the occurrence of disruptions and their outcomes. With such confounding factors, we show that causal inference is a powerful tool to estimate unbiased impacts on passenger demand and journey time, which is also capable of quantifying the spatial-temporal propagation of disruption impacts within metro networks. The causal inference approaches are applied to empirical studies based on the Hong Kong Mass Transit Railway (MTR). Our conclusions can assist researchers and practitioners in two applications: (i) the evaluation of metro performance such as service reliability, system vulnerability and resilience, and (ii) the management of future disruptions.Open Acces

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management

Human-Machine Cooperative Decision Making

The research reported in this thesis focuses on the decision making aspect of human-machine cooperation and reveals new insights from theoretical modeling to experimental evaluations: Two mathematical behavior models of two emancipated cooperation partners in a cooperative decision making process are introduced. The model-based automation designs are experimentally evaluated and thereby demonstrate their benefits compared to state-of-the-art approaches