Multi-objective four-dimensional vehicle motion planning in large dynamic environments

This paper presents Multi-Step A* (MSA*), a search algorithm based on A* for multi-objective 4D vehicle motion planning (three spatial and one time dimension). The research is principally motivated by the need for offline and online motion planning for autonomous Unmanned Aerial Vehicles (UAVs). For UAVs operating in large, dynamic and uncertain 4D environments, the motion plan consists of a sequence of connected linear tracks (or trajectory segments). The track angle and velocity are important parameters that are often restricted by assumptions and grid geometry in conventional motion planners. Many existing planners also fail to incorporate multiple decision criteria and constraints such as wind, fuel, dynamic obstacles and the rules of the air. It is shown that MSA* finds a cost optimal solution using variable length, angle and velocity trajectory segments. These segments are approximated with a grid based cell sequence that provides an inherent tolerance to uncertainty. Computational efficiency is achieved by using variable successor operators to create a multi-resolution, memory efficient lattice sampling structure. Simulation studies on the UAV flight planning problem show that MSA* meets the time constraints of online replanning and finds paths of equivalent cost but in a quarter of the time (on average) of vector neighbourhood based A*.

Local adjustment and global adaptation of control periods for QoC management of control systems

Linking real-time schedulability directly to the Quality of Control (QoC), the ultimate goal of a control system, a hierarchical feedback QoC management framework with the Fixed Priority (FP) and the Earliest-Deadline-First (EDF) policies as plug-ins is proposed in this paper for real-time control systems with multiple control tasks. It uses a task decomposition model for continuous QoC evaluation even in overload conditions, and then employs heuristic rules to adjust the period of each of the control tasks for QoC improvement. If the total requested workload exceeds the desired value, global adaptation of control periods is triggered for workload maintenance. A sufficient stability condition is derived for a class of control systems with delay and period switching of the heuristic rules. Examples are given to demonstrate the proposed approach.

Wellness education : an integrated theoretical framework for fostering transformative learning

Optimum Wellness involves the development, refinement and practice of lifestyle choices which resonate with personally meaningful frames of reference. Personal transformations are the means by which our frames of reference are refined across the lifespan. It is through critical reflection, supportive relationships and meaning making of our experiences that we construct and reconstruct our life paths. When individuals are able to be what they are destined to be or reach their higher purpose, then they are able to contribute to the world in positive and meaningful ways. Transformative education facilitates the changes in perspective that enable one to contemplate and travel a path in life that leads to self-actualisation. This thesis argues for an integrated theoretical framework for optimum Wellness Education. It establishes a learner centred approach to Wellness education in the form of an integrated instructional design framework derived from both Wellness and Transformative education constructs. Students’ approaches to learning and their study strategies in a Wellness education context serve to highlight convergences in the manner in which students can experience perspective transformation. As they learn to critically reflect, pursue relationships and adapt their frames of reference to sustain their pursuit of both learning and Wellness goals, strengthening the nexus between instrumental and transformative learning is a strategically important goal for educators. The aim of this exploratory research study was to examine those facets that serve to optimise the learning experiences of students in a Wellness course. This was accomplished through three research issues: 1) What are the relationships between Wellness, approaches to learning and academic success? 2) How are students approaching learning in an undergraduate Wellness subject? Why are students approaching their learning in the ways they do? 3) What sorts of transformations are students experiencing in their Wellness? How can transformative education be formulated in the context of an undergraduate Wellness subject? Subsequent to a thorough review of the literature pertaining to Wellness education, a mixed method embedded case study design was formulated to explore the research issues. This thesis examines the interrelationships between student, content and context in a one semester university undergraduate unit (a coherent set of learning activities which is assigned a unit code and a credit point value). The experiences of a cohort of 285 undergraduate students in a Wellness course formed the unit of study and seven individual students from a total of sixteen volunteers whose profiles could be constructed from complete data sets were selected for analysis as embedded cases. The introductory level course required participants to engage in a personal project involving a behaviour modification plan for a self-selected, single dimension of Wellness. Students were given access to the Standard Edition Testwell Survey to assess and report their Wellness as a part of their personal projects. To identify relationships among the constructs of Self-Regulated Learning (SRL), Wellness and Student Approaches to Learning (SAL) a blend of quantitative and qualitative methods to collect and analyse data was formulated. Surveys were the primary instruments for acquiring quantitative data. Sources included the Wellness data from Testwell surveys, SAL data from R-SPQ surveys, SRL data from MSLQ surveys and student self-evaluation data from an end of semester survey. Students’ final grades and GPA scores were used as indicators of academic performance. The sources of qualitative data included subject documentation, structured interview transcripts and open-ended responses to survey items. Subsequent to a pilot study in which survey reliability and validity were tested in context, amendments to processes for and instruments of data collection were made. Students who adopted meaning oriented (deep/achieving) approaches tended to assess their Wellness at a higher level, seek effective learning strategies and perform better in formal study. Posttest data in the main study revealed that there were significant positive statistical relationships between academic performance and total wellness scores (rs=.297, n=205, p<.01). Deep (rs=.343, n=137, p<.01) and achieving (rs=.286, n=123, p<.01) approaches to learning also significantly correlated with Wellness whilst surface approaches had negative correlations that were not significant. SRL strategies including metacognitive selfregulation, effort, help-seeking and critical thinking were increasingly correlated with Wellness. Qualitative findings suggest that while all students adopt similar patterns of day to day activities for example attending classes, taking notes, working on assignments the level of care with which these activities is undertaken varies considerably. The dominant motivational trigger for students in this cohort was the personal relevance and associated benefits of the material being learned and practiced. Students were inclined to set goals that had a positive impact on affect and used “sense of happiness” to evaluate their achievement status. Students who had a higher drive to succeed and/or understand tended to have or seek a wider range of strategies. Their goal orientations were generally learning rather than performance based and barriers presented a challenge which could be overcome as opposed to a blockage which prevented progress. Findings from an empirical analysis of the Testwell data suggest that a single third order Wellness construct exists. A revision of the instrument is necessary in order to juxtapose it with the chosen six dimensional Wellness model that forms the foundation construct in the course. Further, redevelopment should be sensitive to the Australian context and culture including choice of language, examples and scenarios used in item construction. This study concludes with an heuristic for use in Wellness education. Guided by principles of Transformative education theory and behaviour change theory, and informed by this representative case study the “CARING” heuristic is proposed as an instructional design tool for Wellness educators seeking to foster transformative learning. Based upon this study, recommendations were made for university educators to provide authentic and personal experiences in Wellness curricula. Emphasis must focus on involving students and teachers in a partnership for implementing Wellness programs both in the curriculum and co-curricularly. The implications of this research for practice are predicated on the willingness of academics to embrace transformative learning at a personal level and a professional one. To explore students’ profiles in detail is not practical however teaching students how to guide us in supporting them through the “pain” of learning is a skill which would benefit them and optimise the learning and teaching process. At a theoretical level, this research contributes to an ecological theory of Wellness education as transformational change. By signposting the wider contexts in which learning takes place, it seeks to encourage changing paradigms to ones which harness the energy of each successive contextual layer in which students live. Future research which amplifies the qualities of individuals and groups who are “Well” and seeks the refinement and development of instruments to measure Wellness constructs would be desirable for both theoretical and applied knowledge bases. Mixed method Wellness research derived and conducted by teams that incorporate expertise from multiple disciplines such as psychology, anthropology, education, and medicine would enable creative and multi-perspective programs of investigation to be designed and implemented. Congruences and inconsistencies in health promotion and education would provide valuable material for strengthening the nexus between transformational learning and behaviour change theories. Future development of and research on the effectiveness of the CARING heuristic would be valuable in advancing the understanding of pedagogies which advance rather than impede learning as a transformative process. Exploring pedagogical models that marry with transformative education may render solutions to the vexing challenge of teaching and learning in diverse contexts.

Multi-objective optimisation of bijective s-boxes

In this paper we investigate the heuristic construction of bijective s-boxes that satisfy a wide range of cryptographic criteria including algebraic complexity, high nonlinearity, low autocorrelation and have none of the known weaknesses including linear structures, fixed points or linear redundancy. We demonstrate that the power mappings can be evolved (by iterated mutation operators alone) to generate bijective s-boxes with the best known tradeoffs among the considered criteria. The s-boxes found are suitable for use directly in modern encryption algorithms.

QoS-aware web service composition using genetic algorithms

Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition — by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers’ expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services.

Planning of distribution networks for medium voltage and low voltage

Determination of the placement and rating of transformers and feeders are the main objective of the basic distribution network planning. The bus voltage and the feeder current are two constraints which should be maintained within their standard range. The distribution network planning is hardened when the planning area is located far from the sources of power generation and the infrastructure. This is mainly as a consequence of the voltage drop, line loss and system reliability. Long distance to supply loads causes a significant amount of voltage drop across the distribution lines. Capacitors and Voltage Regulators (VRs) can be installed to decrease the voltage drop. This long distance also increases the probability of occurrence of a failure. This high probability leads the network reliability to be low. Cross-Connections (CC) and Distributed Generators (DGs) are devices which can be employed for improving system reliability. Another main factor which should be considered in planning of distribution networks (in both rural and urban areas) is load growth. For supporting this factor, transformers and feeders are conventionally upgraded which applies a large cost. Installation of DGs and capacitors in a distribution network can alleviate this issue while the other benefits are gained. In this research, a comprehensive planning is presented for the distribution networks. Since the distribution network is composed of low and medium voltage networks, both are included in this procedure. However, the main focus of this research is on the medium voltage network planning. The main objective is to minimize the investment cost, the line loss, and the reliability indices for a study timeframe and to support load growth. The investment cost is related to the distribution network elements such as the transformers, feeders, capacitors, VRs, CCs, and DGs. The voltage drop and the feeder current as the constraints are maintained within their standard range. In addition to minimizing the reliability and line loss costs, the planned network should support a continual growth of loads, which is an essential concern in planning distribution networks. In this thesis, a novel segmentation-based strategy is proposed for including this factor. Using this strategy, the computation time is significantly reduced compared with the exhaustive search method as the accuracy is still acceptable. In addition to being applicable for considering the load growth, this strategy is appropriate for inclusion of practical load characteristic (dynamic), as demonstrated in this thesis. The allocation and sizing problem has a discrete nature with several local minima. This highlights the importance of selecting a proper optimization method. Modified discrete particle swarm optimization as a heuristic method is introduced in this research to solve this complex planning problem. Discrete nonlinear programming and genetic algorithm as an analytical and a heuristic method respectively are also applied to this problem to evaluate the proposed optimization method.

Choosing landmarks for risky planning

This work examines the effect of landmark placement on the efficiency and accuracy of risk-bounded searches over probabilistic costmaps for mobile robot path planning. In previous work, risk-bounded searches were shown to offer in excess of 70% efficiency increases over normal heuristic search methods. The technique relies on precomputing distance estimates to landmarks which are then used to produce probability distributions over exact heuristics for use in heuristic searches such as A* and D*. The location and number of these landmarks therefore influence greatly the efficiency of the search and the quality of the risk bounds. Here four new methods of selecting landmarks for risk based search are evaluated. Results are shown which demonstrate that landmark selection needs to take into account the centrality of the landmark, and that diminishing rewards are obtained from using large numbers of landmarks.

It's only temporary: Time frame and the dynamics of creative project teams

The success of many knowledge-intensive industries depends on creative projects that lie at the heart of their logic of production. The temporality of such projects, however, is an issue that is insufficiently understood. To address this, we study the perceived time frame of teams that work on creative projects and its effects on project dynamics. An experiment with 267 managers assigned to creative project teams with varying time frames demonstrates that compared to creative project teams with a relatively longer time frame, project teams with a shorter time frame focus more on the immediate present, are less immersed in their task, and utilize a more heuristic mode of information processing. Furthermore, we find that time frame moderates the negative effect of team conflict on team cohesion. These results are consistent with our theory that the temporary nature of creative projects shapes different time frames among project participants, and that it is this time frame that is an important predictor of task and team processes.

Train control to reduce delays upon service disturbances at railway junctions

Train delay is one of the most important indexes to evaluate the service quality of the railway. Because of the interactions of movement among trains, a delayed train may conflict with trains scheduled on other lines at junction area. Train that loses conflict may be forced to stop or slow down because of restrictive signals, which consequently leads to the loss of run-time and probably enlarges more delays. This paper proposes a time-saving train control method to recover delays as soon as possible. In the proposed method, golden section search is adopted to identify the optimal train speed at the expected time of restrictive signal aspect upgrades, which enables the train to depart from the conflicting area as soon as possible. A heuristic method is then developed to attain the advisory train speed profile assisting drivers in train control. Simulation study indicates that the proposed method enables the train to recover delays as soon as possible in case of disturbances at railway junctions, in comparison with the traditional maximum traction strategy and the green wave strategy.

Acoustic hazard detection for pedestrians with obscured hearing

Pedestrians’ use of mp3 players or mobile phones can pose the risk of being hit by motor vehicles. We present an approach for detecting a crash risk level using the computing power and the microphone of mobile devices that can be used to alert the user in advance of an approaching vehicle so as to avoid a crash. A single feature extractor classifier is not usually able to deal with the diversity of risky acoustic scenarios. In this paper, we address the problem of detection of vehicles approaching a pedestrian by a novel, simple, non resource intensive acoustic method. The method uses a set of existing statistical tools to mine signal features. Audio features are adaptively thresholded for relevance and classified with a three component heuristic. The resulting Acoustic Hazard Detection (AHD) system has a very low false positive detection rate. The results of this study could help mobile device manufacturers to embed the presented features into future potable devices and contribute to road safety.

Scheduling Trains with Priorities : a no-wait blocking parallel-machine job-shop scheduling model

The paper investigates train scheduling problems when prioritised trains and non-prioritised trains are simultaneously traversed in a single-line rail network. In this case, no-wait conditions arise because the prioritised trains such as express passenger trains should traverse continuously without any interruption. In comparison, non-prioritised trains such as freight trains are allowed to enter the next section immediately if possible or to remain in a section until the next section on the routing becomes available, which is thought of as a relaxation of no-wait conditions. With thorough analysis of the structural properties of the No-Wait Blocking Parallel-Machine Job-Shop-Scheduling (NWBPMJSS) problem that is originated in this research, an innovative generic constructive algorithm (called NWBPMJSS_Liu-Kozan) is proposed to construct the feasible train timetable in terms of a given order of trains. In particular, the proposed NWBPMJSS_Liu-Kozan constructive algorithm comprises several recursively-used sub-algorithms (i.e. Best-Starting-Time-Determination Procedure, Blocking-Time-Determination Procedure, Conflict-Checking Procedure, Conflict-Eliminating Procedure, Tune-up Procedure and Fine-tune Procedure) to guarantee feasibility by satisfying the blocking, no-wait, deadlock-free and conflict-free constraints. A two-stage hybrid heuristic algorithm (NWBPMJSS_Liu-Kozan-BIH) is developed by combining the NWBPMJSS_Liu-Kozan constructive algorithm and the Best-Insertion-Heuristic (BIH) algorithm to find the preferable train schedule in an efficient and economical way. Extensive computational experiments show that the proposed methodology is promising because it can be applied as a standard and fundamental toolbox for identifying, analysing, modelling and solving real-world scheduling problems.

Optimising a coal rail network under capacity constraints

This research deals with an innovative methodology for optimising the coal train scheduling problem. Based on our previously published work, generic solution techniques are developed by utilising a “toolbox” of standard well-solved standard scheduling problems. According to our analysis, the coal train scheduling problem can be basically modelled a Blocking Parallel-Machine Job-Shop Scheduling (BPMJSS) problem with some minor constraints. To construct the feasible train schedules, an innovative constructive algorithm called the SLEK algorithm is proposed. To optimise the train schedule, a three-stage hybrid algorithm called the SLEK-BIH-TS algorithm is developed based on the definition of a sophisticated neighbourhood structure under the mechanism of the Best-Insertion-Heuristic (BIH) algorithm and Tabu Search (TS) metaheuristic algorithm. A case study is performed for optimising a complex real-world coal rail system in Australia. A method to calculate the lower bound of the makespan is proposed to evaluate results. The results indicate that the proposed methodology is promising to find the optimal or near-optimal feasible train timetables of a coal rail system under network and terminal capacity constraints.

Scheduling a flow-shop with combined buffer conditions

In this paper, No-Wait, No-Buffer, Limited-Buffer, and Infinite-Buffer conditions for the flow-shop problem (FSP) have been investigated. These four different buffer conditions have been combined to generate a new class of scheduling problem, which is significant for modelling many real-world scheduling problems. A new heuristic algorithm is developed to solve this strongly NP-hard problem. Detailed numerical implementations have been analysed and promising results have been achieved.

A comparative study of algorithms for the flowshop scheduling problem

In this paper, we propose three meta-heuristic algorithms for the permutation flowshop (PFS) and the general flowshop (GFS) problems. Two different neighborhood structures are used for these two types of flowshop problem. For the PFS problem, an insertion neighborhood structure is used, while for the GFS problem, a critical-path neighborhood structure is adopted. To evaluate the performance of the proposed algorithms, two sets of problem instances are tested against the algorithms for both types of flowshop problems. The computational results show that the proposed meta-heuristic algorithms with insertion neighborhood for the PFS problem perform slightly better than the corresponding algorithms with critical-path neighborhood for the GFS problem. But in terms of computation time, the GFS algorithms are faster than the corresponding PFS algorithms.

Project management governance : a situational approach

The purpose of this paper is to show how project management governance is addressed through the use of a specific meta-method. Governance is defined here on two criteria: accountability and performance. Accountability is promoted through transparency and performance is promoted by responsive and responsible decision-making. According to a systemic perspective, transparency and decision-making involve having information, tacit or explicit knowledge, as well as understanding of the context, the different parameters and variables, their interaction and conditions of change. Although this method of methods was built according a heuristic process involving 25 years of various researches and consulting activities, it seems appropriate to draw its foundations. I clarify first my epistemological position and the notion of project and project management, as Art and Science. This lead me to define a "Be" / "Have" posture to this regards. Then, the main theoretical roots of MAP Method are exposed: Boisot' s Social Learning Cycle, Praxeology and Theory of Convention. Then we introduced the main characteristics of the method and the 17 methods and tools constituting MAP "tool box", thus with regard to the project management governance perspective. Finally, I discuss the integration of two managerial modes (operational and project modes) and the consequence in term of governance in a specific socio-techno-economic project/context ecosystem.