The crew-scheduling module in the GIST system

The public transportation is gaining importance every year basically due the population growth, environmental policies and, route and street congestion. Too able an efficient management of all the resources related to public transportation, several techniques from different areas are being applied and several projects in Transportation Planning Systems, in different countries, are being developed. In this work, we present the GIST Planning Transportation Systems, a Portuguese project involving two universities and six public transportation companies. We describe in detail one of the most relevant modules of this project, the crew-scheduling module. The crew-scheduling module is based on the application of meta-heuristics, in particular GRASP, tabu search and genetic algorithm to solve the bus-driver-scheduling problem. The metaheuristics have been successfully incorporated in the GIST Planning Transportation Systems and are actually used by several companies.Integrated transportation systems, crew scheduling, metaheuristics

Intelligent Energy Optimization for User Intelligible Goals in Smart Home Environments

Intelligent management of energy consumption is one of the key issues for future energy distribution systems, smart buildings, and consumer appliances. The problem can be tackled both from the point of view of the utility provider, with the intelligence embedded in the smart grid, or from the point of view of the consumer, thanks to suitable local energy management systems (EMS). Conserving energy, however, should respect the user requirements regarding the desired state of the environment, therefore an EMS should constantly and intelligently find the balance between user requirements and energy saving. The paper proposes a solution to this problem, based on explicit high-level modeling of user intentions and automatic control of device states through the solution and optimization of a constrained Boolean satisfiability problem. The proposed approach has been integrated into a smart environment framework, and promising preliminary results are reporte

Hardware-software codesign in a high-level synthesis environment

Interfacing hardware-oriented high-level synthesis to software development is a computationally hard problem for which no general solution exists. Under special conditions, the hardware-software codesign (system-level synthesis) problem may be analyzed with traditional tools and efficient heuristics. This dissertation introduces a new alternative to the currently used heuristic methods. The new approach combines the results of top-down hardware development with existing basic hardware units (bottom-up libraries) and compiler generation tools. The optimization goal is to maximize operating frequency or minimize cost with reasonable tradeoffs in other properties. The dissertation research provides a unified approach to hardware-software codesign. The improvements over previously existing design methodologies are presented in the frame-work of an academic CAD environment (PIPE). This CAD environment implements a sufficient subset of functions of commercial microelectronics CAD packages. The results may be generalized for other general-purpose algorithms or environments. Reference benchmarks are used to validate the new approach. Most of the well-known benchmarks are based on discrete-time numerical simulations, digital filtering applications, and cryptography (an emerging field in benchmarking). As there is a need for high-performance applications, an additional requirement for this dissertation is to investigate pipelined hardware-software systems\u27 performance and design methods. The results demonstrate that the quality of existing heuristics does not change in the enhanced, hardware-software environment

Microservices-based IoT Applications Scheduling in Edge and Fog Computing: A Taxonomy and Future Directions

Edge and Fog computing paradigms utilise distributed, heterogeneous and resource-constrained devices at the edge of the network for efficient deployment of latency-critical and bandwidth-hungry IoT application services. Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up with the rapid development and deployment needs of the fast-evolving IoT applications. Due to the fine-grained modularity of the microservices along with their independently deployable and scalable nature, MSA exhibits great potential in harnessing both Fog and Cloud resources to meet diverse QoS requirements of the IoT application services, thus giving rise to novel paradigms like Osmotic computing. However, efficient and scalable scheduling algorithms are required to utilise the said characteristics of the MSA while overcoming novel challenges introduced by the architecture. To this end, we present a comprehensive taxonomy of recent literature on microservices-based IoT applications scheduling in Edge and Fog computing environments. Furthermore, we organise multiple taxonomies to capture the main aspects of the scheduling problem, analyse and classify related works, identify research gaps within each category, and discuss future research directions.Comment: 35 pages, 10 figures, submitted to ACM Computing Survey

Route Planning for Long-Term Robotics Missions

Many future robotic applications such as the operation in large uncertain environment depend on a more autonomous robot. The robotics long term autonomy presents challenges on how to plan and schedule goal locations across multiple days of mission duration. This is an NP-hard problem that is infeasible to solve for an optimal solution due to the large number of vertices to visit. In some cases the robot hardware constraints also adds the requirement to return to a charging station multiple times in a long term mission. The uncertainties in the robot model and environment require the robot planner to account for them beforehand or to adapt and improve its plan during runtime. The problem to be solved in this work is how to plan multiple day routes for a robot where all predefined locations must be visited only a single time and at each route the robot must start and return to the same initial position while respecting the daily maximum operation time constraint. The proposed solution uses problem definitions from the delivery industry and compares various metaheuristic based techniques for planning and scheduling the multiple day routes for a robotic mission. Therefore the problem of planning multiple day routes for a robot is modeled as a time constrained Vehicle Routing Problem where the robot daily plan is limited by how long the robot with a full charge can operate. The costs are modeled as the time a robot takes to move among locations considering robot and environment characteristics. The solution for this method is obtained in a two step process where a greedy initial solution is generated and then a local search is performed using meta-heuristic based methods. A custom time window formulation with respect to the theoretical maximum daily route is presented to add human expert input, priorities or expiration time to the planned routes allowing the planner to be flexible to various robotic applications. This thesis also proposes an intermediary mission control layer, that connects the daily route plan to the robot navigation layer. The goal of the Mission Control is to monitor the robot operation, continuously improve its route and adapt to unexpected events by dropping waypoints according to some defined penalties. This is an iterative process where optimization is performed locally in real time as the robot traverse its goals and offline at the end of each day with the remaining vertices. The performance of the various meta-heuristic and how optimization improves over time are analysed in several robotic route planning and scheduling scenarios. Two robotic simulation environments were built to demonstrate practical application of these methods. An unmanned ground vehicle operated fully autonomously using the presented methods in a simulated underground stone mine environment where the goal is to inspect the pillars for structural failures and a farm environment where the goal is to pollinate flowers with an attached robotic arm. All the optimization methods tested presented significant improvement in the total route costs compared to the initial Path-Cheapest-Arc solution. However the Guided Local Search presented a smaller standard deviation among the methods in most situations. The time-windows allowed for a seamless integration with an expert human input and the mission control layer, forced the robot to operate within the mission constraints by dynamically choosing the routes and the necessity of dropping some of the vertices

Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT

Robot-based assembly in construction has emerged as a promising solution to address numerous challenges such as increasing costs, labor shortages, and the demand for safe and efficient construction processes. One of the main obstacles in realizing the full potential of these robotic systems is the need for effective and efficient sequence planning for construction tasks. Current approaches, including mathematical and heuristic techniques or machine learning methods, face limitations in their adaptability and scalability to dynamic construction environments. To expand the ability of the current robot system in sequential understanding, this paper introduces RoboGPT, a novel system that leverages the advanced reasoning capabilities of ChatGPT, a large language model, for automated sequence planning in robot-based assembly applied to construction tasks. The proposed system adapts ChatGPT for construction sequence planning and demonstrate its feasibility and effectiveness through experimental evaluation including Two case studies and 80 trials about real construction tasks. The results show that RoboGPT-driven robots can handle complex construction operations and adapt to changes on the fly. This paper contributes to the ongoing efforts to enhance the capabilities and performance of robot-based assembly systems in the construction industry, and it paves the way for further integration of large language model technologies in the field of construction robotics.Comment: 14 pages, 20 figures, submitted to IEEE Acces

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E