An iterated multi-stage selection hyper-heuristic

There is a growing interest towards the design of reusable general purpose search methods that are applicable to different problems instead of tailored solutions to a single particular problem. Hyper-heuristics have emerged as such high level methods that explore the space formed by a set of heuristics (move operators) or heuristic components for solving computationally hard problems. A selection hyper-heuristic mixes and controls a predefined set of low level heuristics with the goal of improving an initially generated solution by choosing and applying an appropriate heuristic to a solution in hand and deciding whether to accept or reject the new solution at each step under an iterative framework. Designing an adaptive control mechanism for the heuristic selection and combining it with a suitable acceptance method is a major challenge, because both components can influence the overall performance of a selection hyper-heuristic. In this study, we describe a novel iterated multi-stage hyper-heuristic approach which cycles through two interacting hyper-heuristics and operates based on the principle that not all low level heuristics for a problem domain would be useful at any point of the search process. The empirical results on a hyper-heuristic benchmark indicate the success of the proposed selection hyper-heuristic across six problem domains beating the state-of-the-art approach

Quantum annealing for vehicle routing and scheduling problems

Metaheuristic approaches to solving combinatorial optimization problems have many attractions. They sidestep the issue of combinatorial explosion; they return good results; they are often conceptually simple and straight forward to implement. There are also shortcomings. Optimal solutions are not guaranteed; choosing the metaheuristic which best fits a problem is a matter of experimentation; and conceptual differences between metaheuristics make absolute comparisons of performance difficult. There is also the difficulty of configuration of the algorithm - the process of identifying precise values for the parameters which control the optimization process. Quantum annealing is a metaheuristic which is the quantum counterpart of the well known classical Simulated Annealing algorithm for combinatorial optimization problems. This research investigates the application of quantum annealing to the Vehicle Routing Problem, a difficult problem of practical significance within industries such as logistics and workforce scheduling. The work devises spin encoding schemes for routing and scheduling problem domains, enabling an effective quantum annealing algorithm which locates new solutions to widely used benchmarks. The performance of the metaheuristic is further improved by the development of an enhanced tuning approach using fitness clouds as behaviour models. The algorithm is shown to be further enhanced by taking advantage of multiprocessor environments, using threading techniques to parallelize the optimization workload. The work also shows quantum annealing applied successfully in an industrial setting to generate solutions to complex scheduling problems, results which created extra savings over an incumbent optimization technique. Components of the intellectual property rendered in this latter effort went on to secure a patent-protected status

An investigation into manufacturing execution systems

Hardware and software developments of this decade have exposed an hiatus between business/management applications and process control in heavy industry in the implementation of computer technology. This document examines the development of discrete manufacturing and of relevant implementations of computing. It seeks to examine and to clarify the issues involved in a perceived current drive to bridge this gap, to integrate all the systems in a manufacturing enterprise in a Manufacturing Execution System (MES) in order to address two hypotheses: I) That overseas trends towards the development of manufacturing execution systems have application in the Australian industrial context. 2) That significant gains in production efficiency and quality may be achieved by the application of an MES. It became apparent early in this study that any understanding the function of an MES requires an understanding of the context in which it works. Following the Introduction, therefore, Section Two contains a brief overview of the history and development of modem industry with particular attention to the subject of inventory and inventory management. Since the 1970s, three main streams of change in manufacturing management methodology developed. These are dealt with in some detail in Section Three. Section Four outlines a variety of areas of increasing computerisation on the shop floor while Section Five addresses the integration of the whole system, management and shop floor, seeking to demonstrate the complexity of the subject and to discover current trends and developments. Section Five includes a survey of some of the software and hardware options currently available and Section Six summarises the work and presents some observations and conclusions. Three appendices provide more detailed information on MES software availability, pricing and market penetratio

Using improvement location and improvement preference to create meta-heuristics

Local search algorithms are used to produce solutions to combinatorial logistics problems such as the vehicle routing problem. Where the algorithms aim to minimise the number of vehicles needed to deliver goods. Local search creates a first solution and that solution is the best until the heuristic finds a better one, hence it is argued that all existing local search algorithms make a series of improvements. Different local search algorithms locate these improvements with different improvement location mechanisms. Local Search also gives preference to using some improvements rather than others. To make these preference choices different local search algorithms use different improvement preference mechanisms. Current local search algorithms intertwine improvement location and improvement preference mechanisms, making it difficult to identify if they are both having a positive impact and the scale of the impact. The thesis hypothesis states “Distinguishing between improvement location and improvement preference is feasible and useful when creating local search algorithms”. Distinguishing between improvement location and preference produced results that suggest how local search methods in current use can be improved. The experiments compare methods of locating improvements and improvement preference. They use both whole non-improving solutions and partial solution changes, similar to Lin-Kemighan, to locate improvements. They also compare giving preference to small improvements or large improvements. The comparisons are done using the Solomon vehicle routing problem benchmarks. Using partial solution changes to locate improvements and giving preference to small improvements typically gave the highest quality solutions at the cost of doubling execution time. The results also show which methods of location and preference do well when only a small amount of execution time is available

The Rock and The Map: two tales of contemporary heritage landscaping in Scotland

As opposed to the ingrained and popularly rehearsed notion that Scotland’s quintessential landscapes are mountainous, remote, rugged and wild, this thesis considers the local landmarks of Dumbarton Rock and the Great Polish Map of Scotland as exemplary of a “New Scottish Landscape”. That is, a new aesthetic, or ‘way of seeing’ the Scottish landscape as one defined by ‘everyday’ local landscapes of affiliation, as much as the ‘special’ and spectacular. Such a belief is given added traction with the demographic fact that the majority of Scotland's population inhabits the densely urbanised Central Belt, in which landscape qualities of 'wildness' and 'remoteness' are generally lacking. Despite this ‘grandeur deficit’, there is increasing recognition that exurban, post-industrial, partially degraded or abandoned landscapes have the capacity to generate intensities of belonging and attachment, reflecting new, distinctive heritage values. Aligned with ‘processual’ conceptual understandings of landscape and heritage as situated, subjective phenomena, ‘the Rock’ and ‘the Map’ are approached in this thesis as instances of “heritage landscaping”, whereby landscape and heritage are figured as conjoined; emerging and unfolding together in practice and experience. Informing a phenomenological methodological design around fieldwork principles of observation, sensation, practice and performance, a range of research materials are gathered to tell the stories of the Rock and the Map. Recounted in two central empirical chapters, the Rock and the Map are explored respectively through the provision of a historical-cultural biography, lending context and time-depth to my own situated experiences through participative intervention. As contrasting but related instances of community-driven heritage landscaping, the Rock and the Map are then considered together to critically engage with recent conceptual developments in landscape and heritage practice towards ‘democratisation’. That is, a loosening of traditionally top-down professional landscape and heritage decision-making, to better account for the often intangible ‘social values’ held by ‘unofficial’ local communities of interest. Drawing upon my situated inquiries of the Rock and the Map, I contend that landscape phenomenology and a ‘performative ethos’ provide a creative and effective means of apprehending and accounting for these alternative narratives, allowing us to uncover and illuminate the latent potential and cultural value held within the New Scottish Landscape

Scheduling Problems

Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems

February 22, 2001

The Breeze is the student newspaper of James Madison University in Harrisonburg, Virginia

Programming frameworks for mobile sensing

The proliferation of smart mobile devices in people’s daily lives is making context-aware computing a reality. A plethora of sensors available in these devices can be utilized to understand users’ context better. Apps can provide more relevant data or services to the user based on improved understanding of user’s context. With the advent of cloud-assisted mobile platforms, apps can also perform collaborative computation over the sensing data collected from a group of users. However, there are still two main issues: (1) A lack of simple and effective personal sensing frameworks: existing frameworks do not provide support for real-time fusing of data from motion and visual sensors in a simple manner, and no existing framework collectively utilizes sensors from multiple personal devices and personal IoT sensors, and (2) a lack of collaborative/distributed computing frameworks for mobile users. This dissertation presents solutions for these two issues. The first issue is addressed by TagPix and Sentio, two frameworks for mobile sensing. The second issue is addressed by Moitree, a middleware for mobile distributed computing, and CASINO, a collaborative sensor-driven offloading system. TagPix is a real-time, privacy preserving photo tagging framework, which works locally on the phones and consumes little resources (e.g., battery). It generates relevant tags for landscape photos by utilizing sensors of a mobile device and it does not require any previous training or indexing. When a user aims the mobile camera to a particular landmark, the framework uses accelerometer and geomagnetic field sensor to identify in which direction the user is aiming the camera at. It then uses a landmark database and employs a smart distance estimation algorithm to identify which landmark(s) is targeted by the user. The framework then generates relevant tags for the captured photo using these information. A more versatile sensing framework can be developed using sensors from multiple devices possessed by a user. Sentio is such a framework which enables apps to seamlessly utilize the collective sensing capabilities of the user’s personal devices and of the IoT sensors located in the proximity of the user. With Sentio, an app running on any personal mobile/wearable device can access any sensor of the user in real-time using the same API, can selectively switch to the most suitable sensor of a particular type when multiple sensors of this type are available at different devices, and can build composite sensors. Sentio offers seamless connectivity to sensors even if the sensor-accessing code is offloaded to the cloud. Sentio provides these functionalities with a high-level API and a distributed middleware that handles all low-level communication and sensor management tasks. This dissertation also proposes Moitree, a middleware for the mobile cloud platforms where each mobile device is augmented by an avatar, a per-user always-on software entity that resides in the cloud. Mobile-avatar pairs participate in distributed computing as a unified computing entity. Moitree provides a common programming and execution framework for mobile distributed apps. Moitree allows the components of a distributed app to execute seamlessly over a set of mobile/avatar pairs, with the provision of offloading computation and communication to the cloud. The programming framework has two key features: user collaborations are modeled using group semantics - groups are created dynamically based on context and are hierarchical; data communication among group members is offloaded to the cloud through high-level communication channels. Finally, this dissertation presents and discusses CASINO, a collaborative sensor-driven computation offloading framework which can be used alongside Moitree. This framework includes a new scheduling algorithm which minimizes the total completion time of a collaborative computation that executes over a set of mobile/avatar pairs. Using the CASINO API, the programmers can mark their classes and functions as ”offloadable”. The framework collects profiling information (network, CPU, battery, etc.) from participating users’ mobile devices and avatars, and then schedules ”offloadable” tasks in mobiles and avatars in a way that reduces the total completion time. The scheduling problem is proven to be NP-Hard and there is no polynomial time optimization algorithm for it. The proposed algorithm can generate a schedule in polynomial time using a topological sorting and greedy technique