Solving real-world routing problems using evolutionary algorithms and multi-agent-systems

This thesis investigates the solving of routing problems using Evolutionary Algorithms (EAs). Routing problems are known to be hard and may possess complex search spaces. Evolutionary algorithms are potentially powerful tools for finding solutions within complex search spaces. The problem investigated is the routing of deliveries to households within an urban environment; the most common instance of this problem is that of daily postal deliveries. A representation known as Street Based Routing (SBR) is presented. This is a problem representation that makes use of the real world groupings of streets and houses. This representation is an indirect problem representation designed specifically for use with EAs. The SBR representation is incorporated within an EA and used to construct delivery routes around a variety of problem instances. The EA based system is compared against a Travelling Salesman Problem (TSP) solver, and the results are presented. The EA based system produces routes that are on average slightly longer than those produced by the TSP solver. Real world problems may often involve the construction of a network of delivery routes that are subject to multiple hard and soft constraints. A Multi Agent System (MAS) based framework for building delivery networks is presented that makes use of the SBR based EA presented earlier. Each agent within the system uses an EA to construct a single route. Agents may exchange work (via auctions or by directly negotiated exchanges) allowing the optimisation of their route. It is demonstrated that this approach has much potential and is capable of constructing delivery networks meeting set constraints, over a range of problem instances and constraint values.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Solving Real-World Routing Problems using Evolutionary Algorithms and Multi-Agent-Systems.

This thesis investigates the solving of routing problems using Evolutionary Algorithms (EAs). Routing problems are known to be hard and may possess complex search spaces. Evolutionary algorithms are potentially powerful tools for finding solutions within complex search spaces.The problem investigated is the routing of deliveries to households within an urban environment; the most common instance of this problem is that of daily postal deliveries. A representation known as Street Based Routing (SBR) is presented. This is a problem representation that makes use of the real world groupings of streets and houses. This representation is an indirect problem representationdesigned specifically for use with EAs. The SBR representation is incorporated within an EA and used to construct delivery routes around a variety of probleminstances. The EA based system is compared against a Travelling Salesman Problem (TSP) solver, and the results are presented. The EA based system producesroutes that are on average slightly longer than those produced by the TSP solver.Real world problems may often involve the construction of a network of delivery routes that are subject to multiple hard and soft constraints. A Multi Agent System (MAS) based framework for building delivery networks is presented thatmakes use of the SBR based EA presented earlier. Each agent within the system uses an EA to construct a single route. Agents may exchange work (via auctionsor by directly negotiated exchanges) allowing the optimisation of their route. It is demonstrated that this approach has much potential and is capable of constructingdelivery networks meeting set constraints, over a range of problem instances and constraint values

Agent motion planning with GAs enhanced by memory models.

The Tartarus problem may be considered a benchmark problem in the field of robotics. A robotic agent is required to move a number of blocks to the edge of an environment. The location of the blocks and position of the robot is unknown initially. The authors present a framework that allows the agent to learn about its environment and plan ahead using a GA to solve the problem. The authors prove that the GA based method provides the best published result on the Tartarus problem. An exhaustive search is used within the framework as a comparison, this provides a higher score still. This paper presents the two best Tartarus results yet publishe

State assignment for sequential circuits using multi-objective genetic algorithm

In this study, a new approach using a multi-objective genetic algorithm (MOGA) is proposed to determine the optimal state assignment with less area and power dissipations for completely and incompletely specified sequential circuits. The goal is to find the best assignments which reduce the component count and switching activity. The MOGA employs a Pareto ranking scheme and produces a set of state assignments, which are optimal in both objectives. The ESPRESSO tool is used to optimise the combinational parts of the sequential circuits. Experimental results are given using a personal computer with an Intel CPU of 2.4 GHz and 2 GB RAM. The algorithm is implemented using C++ and fully tested with benchmark examples. The experimental results show that saving in components and switching activity are achieved in most of the benchmarks tested compared with recent published research

Satisfaction with web-based training in an integrated healthcare delivery network: do age, education, computer skills and attitudes matter?

<p>Abstract</p> <p>Background</p> <p>Healthcare institutions spend enormous time and effort to train their workforce. Web-based training can potentially streamline this process. However the deployment of web-based training in a large-scale setting with a diverse healthcare workforce has not been evaluated. The aim of this study was to evaluate the satisfaction of healthcare professionals with web-based training and to determine the predictors of such satisfaction including age, education status and computer proficiency.</p> <p>Methods</p> <p>Observational, cross-sectional survey of healthcare professionals from six hospital systems in an integrated delivery network. We measured overall satisfaction to web-based training and response to survey items measuring Website Usability, Course Usefulness, Instructional Design Effectiveness, Computer Proficiency and Self-learning Attitude.</p> <p>Results</p> <p>A total of 17,891 healthcare professionals completed the web-based training on HIPAA Privacy Rule; and of these, 13,537 completed the survey (response rate 75.6%). Overall course satisfaction was good (median, 4; scale, 1 to 5) with more than 75% of the respondents satisfied with the training (rating 4 or 5) and 65% preferring web-based training over traditional instructor-led training (rating 4 or 5). Multivariable ordinal regression revealed 3 key predictors of satisfaction with web-based training: Instructional Design Effectiveness, Website Usability and Course Usefulness. Demographic predictors such as gender, age and education did not have an effect on satisfaction.</p> <p>Conclusion</p> <p>The study shows that web-based training when tailored to learners' background, is perceived as a satisfactory mode of learning by an interdisciplinary group of healthcare professionals, irrespective of age, education level or prior computer experience. Future studies should aim to measure the long-term outcomes of web-based training.</p