An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning

Mobile robotic platforms are an indispensable tool for various scientific and industrial applications. Robots are used to undertake missions whose execution is constrained by various factors, such as the allocated time or their remaining energy. Existing solutions for resource constrained multi-robot sensing mission planning provide optimal plans at a prohibitive computational complexity for online application [1],[2],[3]. A heuristic approach exists for an online, resource constrained sensing mission planning for a single vehicle [4]. This work proposes a Genetic Algorithm (GA) based heuristic for the Correlated Team Orienteering Problem (CTOP) that is used for planning sensing and monitoring missions for robotic teams that operate under resource constraints. The heuristic is compared against optimal Mixed Integer Quadratic Programming (MIQP) solutions. Results show that the quality of the heuristic solution is at the worst case equal to the 5% optimal solution. The heuristic solution proves to be at least 300 times more time efficient in the worst tested case. The GA heuristic execution required in the worst case less than a second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and Automation Letters (RA-L

Human resources for control of tuberculosis and HIV-associated tuberculosis.

The global targets for tuberculosis (TB) control were postponed from 2000 to 2005, but on current evidence a further postponement may be necessary. Of the constraints preventing these targets being met, the primary one appears to be the lack of adequately trained and qualified staff. This paper outlines: 1) the human resources and skills for global TB and human immunodeficiency virus (HIV) TB control, including the human resources for implementing the DOTS strategy, the additional human resources for implementing joint HIV-TB control strategies and what is known about human resource gaps at global level; 2) the attempts to quantify human resource gaps by focusing on a small country in sub-Saharan Africa, Malawi; and 3) the main constraints to human resources and their possible solutions, under six main headings: human resource planning; production of human resources; distribution of the work-force; motivation and staff retention; quality of existing staff; and the effect of HIV/AIDS. We recommend an urgent shift in thinking about the human resource paradigm, and exhort international policy makers and the donor community to make a concerted effort to bridge the current gaps by investing for real change

A derivative-free approach for a simulation-based optimization problem in healthcare

Hospitals have been challenged in recent years to deliver high quality care with limited resources. Given the pressure to contain costs,developing procedures for optimal resource allocation becomes more and more critical in this context. Indeed, under/overutilization of emergency room and ward resources can either compromise a hospital's ability to provide the best possible care, or result in precious funding going toward underutilized resources. Simulation--based optimization tools then help facilitating the planning and management of hospital services, by maximizing/minimizing some specific indices (e.g. net profit) subject to given clinical and economical constraints. In this work, we develop a simulation--based optimization approach for the resource planning of a specific hospital ward. At each step, we first consider a suitably chosen resource setting and evaluate both efficiency and satisfaction of the restrictions by means of a discrete--event simulation model. Then, taking into account the information obtained by the simulation process, we use a derivative--free optimization algorithm to modify the given setting. We report results for a real--world problem coming from the obstetrics ward of an Italian hospital showing both the effectiveness and the efficiency of the proposed approach

Perception-driven optimal motion planning under resource constraints

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Applied Ocean Science & Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2019.Over the past few years there has been a new wave of interest in fully autonomous robots operating in the real world, with applications from autonomous driving to search and rescue. These robots are expected to operate at high speeds in unknown, unstructured environments using only onboard sensing and computation, presenting significant challenges for high performance autonomous navigation. To enable research in these challenging scenarios, the first part of this thesis focuses on the development of a custom high-performance research UAV capable of high speed autonomous flight using only vision and inertial sensors. This research platform was used to develop stateof-the-art onboard visual inertial state estimation at high speeds in challenging scenarios such as flying through window gaps. While this platform is capable of high performance state estimation and control, its capabilities in unknown environments are severely limited by the computational costs of running traditional vision-based mapping and motion planning algorithms on an embedded platform. Motivated by these challenges, the second part of this thesis presents an algorithmic approach to the problem of motion planning in an unknown environment when the computational costs of mapping all available sensor data is prohibitively high. The algorithm is built around a tree of dynamically feasible and free space optimal trajectories to the goal state in configuration space. As the algorithm progresses it iteratively switches between processing new sensor data and locally updating the search tree. We show that the algorithm produces globally optimal motion plans, matching the optimal solution for the case with the full (unprocessed) sensor data, while only processing a subset of the data. The mapping and motion planning algorithm is demonstrated on a number of test systems, with a particular focus on a six-dimensional thrust limited model of a quadrotor

Dynamic transport scheduling under multiple resource constraints

This paper presents a heuristic for the dynamic vehicle scheduling problem with multiple resource capacity constraints. In the envisaged application, an automated transport system using Automated Guided Vehicles, bottleneck resources are (1) vehicles, (2) docks for loading/unloading, (3) vehicle parking places, and (4) load storage space. This problem is hard, because interrelated activities (loading, transportation, unloading) at several geographical locations have to be scheduled under multiple resource constraints, where the bottleneck resource varies over time. Besides, the method should be suitable for real-time planning. We developed a dedicated serial scheduling method and analyzed its dynamic behavior using discrete event simulation. We found that our method is very well able to find good vehicle schedules satisfying all resource constraints. For comparison, we used a simple approach where we left out the resource constraints and extended the processing times by statistically estimated waiting times to account for finite capacities. We found that our newly designed method finds better schedules in terms of service levels

Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms.

Adjustment of a given age distribution to a desired age distribution within a required time frame is dynamically performed for the purpose of Human Resource (HR) planning in Human Resource Management (HRM). The adjustment process is carried out by adding the adjustment magnitudes to the existing number of employees at the selected age groups on the yearly basis. A model of a discrete dynamical system is employed to emulate the evolution of the age distribution used under the adjustment process. Genetic Algorithms (GA) is applied for determining the adjustment magnitudes that influence the dynamics of the system. An interesting aspect of the problem lies in the high number of constraints; though the constraints are fundamental, they are considerably higher in number than in many other optimization problems. An adaptive penalty scheme is proposed for handling the constraints. Numerical examples show that GA with the utilized adaptive penalty scheme provides potential means for HR planning in HRM

Sustainable development strategies for product provision and manufacturing approaches

Manufacturing firms are under many pressures both financially and competitive which focus attention on the performance of their manufacturing processes. In this paper the opportunities for improving the environmental impact of products within the constraints of existing manufacturing infrastructure are examined. Approaches which support sustainability in two aspects are proposed, firstly, the provision of products to the users in ways which extend the product life and secondly, manufacturing approaches which reduce resource usage. The provision and manufacture of products in ways that are truly sustainable are inhibited by three issues: firstly, decisions are predominantly made solely from the perspective of the “vendor” (and do not consider the wider perspective); secondly, that generally the scope of business planning is still rooted in production/manufacturing costs (and not consumption costs) and thirdly, the current performance measures (e.g. KPIs) mainly focus on profitability. The rationale for this conference paper is the argument that there is a need to raise the awareness during the earliest stages of business planning that there may be alternative approaches which are more sustainable. The concepts presented here will underpin further research into performance measures which encompass sustainability and resulting business planning implications