No description available.

In this thesis, we study theoretically and empirically the additive abstraction-based heuristics. First we present formal general definitions for abstractions that extend to general additive abstractions. We show that the general definition makes proofs of admissibility, consistency, and additivity easier, by proving that several previous methods for defining abstractions and additivity satisfy three imple conditions. Then we investigate two general methods for defining additive abstractions and run experiments to determine the effectiveness of these methods for two benchmark state spaces: TopSpin and the Pancake puzzle. Third, we propose that the accuracy of the heuristics generated by abstraction can be improved by checking for infeasibility. The theory and experiments demonstrate the approach to detect infeasibility and the application of this technique to different domains. Finally, we explore the applications of additive abstraction-based heuristics in two state spaces with nonuniform edge costs: the Sequential Ordering Problem (SOP) and the weighted Pancake puzzle. We formalize a novel way of generating additive and non-additive heuristics for these state spaces. Furthermore, we investigate the key concepts to generate good additive and non-additive abstractions. Experiments show that compared to some simple alternative heuristics, well chosen abstractions can enhance the quality of suboptimal solutions for large SOP instances and reduce search time for the weighted Pancake problems.

No description available.

Unmanned Vehicles (UVs) have been significantly utilized in military and civil applications over the last decade. Path-planning of UVs plays an important role in effectively using the available resources such as the UVs and sensors as efficiently as possible. The main purpose of this thesis is to address two path planning problems involving a single UV. The two problems we consider are the quota problem and the budget problem. In the quota problem, the vehicle has to visit a sufficient number of targets to satisfy the quota requirement on the total prize collected in the tour. In the budget problem, the vehicle has to comply with a constraint of the distance traveled by the UV. We solve both these problems using a practical heuristic called the prize-multiplier approach. This approach first uses a primal-dual algorithm to first assign the targets to the UV. The Lin – Kernighan Heuristic (LKH) is then applied to generate a tour of the assigned targets for the UV. We tested this approach on two different vehicle models. One model is a simple vehicle which can move in any direction without a constraint on its turning radius. The other model is a Reeds-Shepp vehicle. We also modeled both problems in C++ using the multi-commodity flow formulations, and solved them to optimality by using the Concert Technology of CPLEX. We used the results generated by CPLEX to determine the quality of the solutions produced by the heuristics. By comparing the objective values of the obtained solutions and the running times of the heuristics and CPLEX, one can conclude that the proposed heuristics produce solutions with good quality to our problems within our desired time limits.

No description available.

This thesis is a sociological history of the making of behavioural economics. Behavioural economics is a discipline in which economists draw on psychological knowledge and approaches to understand economic behaviour. The narrative begins with the lives and work of psychologists Daniel Kahneman and Amos Tversky in the newly established state of Israel. It then moves from the making of the so called Heuristics and Biases Programme in the 1970’s to the privately funded Behavioural Economics Program in the USA in the 1980’s. Using a blend of analysis of archival documents, published material, and interviews I seek to understand the formation of the discipline of behavioural economics by applying the notion of a psychology)of)error as an analytical tool. The small number of historians who have studied behavioural economics have all identified a concern with human error as a crucial element of its intellectual makeup. I take this observation further by arguing that both Kahneman and Tversky’s Heuristics and Biases Programme and behavioural economics are psychologies) of) error because the object to be explained in both fields was restricted to behavioural deviations from a normative core. In the case of Heuristics and Biases that normative core consisted of a blend of statistical and logical norms imported from traditional decision theory about what constituted rational decision making. In the case of behavioural economics the normative core was made up of assumptions about rational economic behaviour developed by neo-classical economists. Understanding behavioural economics as a psychology of error allows me to shed light on the complicated relationship between behavioural economics and neo-classical economics. Specifically it helps explain how behavioural economists sought to strike a careful balance between critiquing the descriptive claims of neo-classical economists and reinforcing their normative ambitions.

The flow shop scheduling problem is a classical combinatorial problem being studied for years. The focus of this research is to study two variants of the flow shop scheduling problem in order to minimize makespan by scheduling n jobs on m machines. A solution approach is developed for the modified flow shop problem with due dates and release times. This algorithm is an attempt to contribute to the limited literature for the problem. Another tabu search-based solution approach is developed to solve the classical flow shop scheduling problem. This meta-heuristic (called 3XTS) allows an efficient search of the neighboring solutions leading to a fast solution procedure. Several control parameters affecting the quality of the algorithm are experimentally tested, and certain rules are established for different problem instances. The 3XTS is compared to another tabu search method (that seems to be a champion) in terms of solution quality and computation time.

This study provides heuristic approaches, including an ant colony optimization (ACO) inspired heuristic, to solve a crane scheduling problem that exists in most shipyards, where cranes are a primary means of processing and handling materials. Cranes move on a network of tracks, thus, blocking of crane movements is an issue. The crane scheduling problem consists of two major sub-problems: scheduling problem that determines the best overall order in which jobs are to be performed; the assignment problem that assigns cranes to jobs. The proposed heuristic consists of an Earliest Due Date sorting procedure in combination with an ACO assignment procedure that aims to satisfy the objectives of minimizing makespan while maximizing crane utilization. Test data sets of various sizes are generated and the results of the proposed approach are compared to other developed heuristics. The proposed approach outperforms others in both objective measures and obtains solutions in a timely manner.

No description available.

An alarming number of adolescents experience obesity and related health issues, in part because of a lack of exercise. Increased mobile technology availability can have negative effects on amount of exercise, but they can have positive effects as well. Leveraging mobile technology to encourage and motivate exercise has potential to decrease unhealthy lifestyles, especially among young people. Mobile exergaming is an emerging field that has the potential to motivate users to exercise while also having fun. However, much of the early development work on mobile exergames has been ad-hoc, with little guidance available for designers. This work seeks to identify heuristics catered for mobile exergaming. This thesis presents four mobile exergaming heuristics were identified based on recent literature and on the author's mobile exergame design and development efforts: 1) Motivational game concepts that promote physical activity; 2) Game cues that engage active users; 3) Physically and temporally appropriate game structure to encourage continual, recurring play; 4) Game play movements that are safe for the user and for the device. This thesis describes the development of the mobile exergame heuristics, along with the creation and distribution of an ExergameApp Suite comprised of three mobile exergames: Fish Out of Water, Color Hunt and Space Rayders. / Master of Science