2,950,441 research outputs found
Planning and Scheduling of Business Processes in Run-Time: A Repair Planning Example
Over the last decade, the efficient and flexible management of business
processes has become one of the most critical success aspects. Furthermore, there
exists a growing interest in the application of Artificial Intelligence Planning and
Scheduling techniques to automate the production and execution of models of organization.
However, from our point of view, several connections between both
disciplines remains to be exploited. The current work presents a proposal for modelling
and enacting business processes that involve the selection and order of the
activities to be executed (planning), besides the resource allocation (scheduling),
considering the optimization of several functions and the reach of some objectives.
The main novelty is that all decisions (even the activities selection) are taken in
run-time considering the actual parameters of the execution, so the business process
is managed in an efficient and flexible way. As an example, a complex and representative
problem, the repair planning problem, is managed through the proposed
approach.Ministerio de Ciencia e InnovaciĂłn TIN2009-13714Junta de AndalucĂa P08-TIC-0409
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Space and Time: Wind in an Investment Planning Model
Investment planning models inform investment decisions and government policies. Current models do not capture the intermittent nature of renewable energy sources, restricting the applicability of the models for high penetrations of renewables. We provide a methodology to capture spatial variation in wind output in combination with transmission constraints. The representation of wind distributions with stochastic approaches or an extensive historic data set would exceed computational constraints for real world application. Hence we restrict the amount of input data, and use boot-strapping to illustrate the robustness of the results. For the UK power system we model wind deployment and the value of transmission capacity
Dynamic Time-Dependent Route Planning in Road Networks with User Preferences
There has been tremendous progress in algorithmic methods for computing
driving directions on road networks. Most of that work focuses on
time-independent route planning, where it is assumed that the cost on each arc
is constant per query. In practice, the current traffic situation significantly
influences the travel time on large parts of the road network, and it changes
over the day. One can distinguish between traffic congestion that can be
predicted using historical traffic data, and congestion due to unpredictable
events, e.g., accidents. In this work, we study the \emph{dynamic and
time-dependent} route planning problem, which takes both prediction (based on
historical data) and live traffic into account. To this end, we propose a
practical algorithm that, while robust to user preferences, is able to
integrate global changes of the time-dependent metric~(e.g., due to traffic
updates or user restrictions) faster than previous approaches, while allowing
subsequent queries that enable interactive applications
The time horizon and its role in multiple species conservation planning
Survival probability within a certain time horizon T is a common measure of
population viability. The choice of T implicitly involves a time preference,
similar to economic discounting: Conservation success is evaluated at the time
horizon T, while all effects that occur later than T are not considered.
Despite the obvious relevance of the time horizon, ecological studies seldom
analyze its impact on the evaluation of conservation options. In this paper, we
show that, while the choice of T does not change the ranking of conservation
options for single species under stationary conditions, it may substantially
change conservation decisions for multiple species. We conclude that it is of
crucial importance to investigate the sensitivity of model results to the
choice of the time horizon or other measures of time preference when
prioritizing biodiversity conservation efforts.Comment: 8 pages, 5 figures. Minor changes to v1. This version corredponds to
the final publication in Biological Conservatio
Sub-Optimal Allocation of Time in Sequential Movements
The allocation of limited resources such as time or energy is a core problem that organisms face when planning complex
actions. Most previous research concerning planning of movement has focused on the planning of single, isolated
movements. Here we investigated the allocation of time in a pointing task where human subjects attempted to touch two
targets in a specified order to earn monetary rewards. Subjects were required to complete both movements within a limited time but could freely allocate the available time between the movements. The time constraint presents an allocation
problem to the subjects: the more time spent on one movement, the less time is available for the other. In different
conditions we assigned different rewards to the two tokens. How the subject allocated time between movements affected
their expected gain on each trial. We also varied the angle between the first and second movements and the length of the
second movement. Based on our results, we developed and tested a model of speed-accuracy tradeoff for sequential
movements. Using this model we could predict the time allocation that would maximize the expected gain of each subject
in each experimental condition. We compared human performance with predicted optimal performance. We found that all
subjects allocated time sub-optimally, spending more time than they should on the first movement even when the reward
of the second target was five times larger than the first. We conclude that the movement planning system fails to maximize
expected reward in planning sequences of as few as two movements and discuss possible interpretations drawn from
economic theory
Real-time Motion Planning For Autonomous Car in Multiple Situations Under Simulated Urban Environment
Advanced autonomous cars have revolutionary meaning for the automobile industry. While more and more companies have already started to build their own autonomous cars, no one has yet brought a practical autonomous car into the market. One key problem of their cars is lacking a reliable active real-time motion planning system for the urban environment. A real-time motion planning system makes cars can safely and stably drive under the urban environment. The final goal for this project is to design and implement a reliable real-time motion planning system to reduce accident rates in autonomous cars instead of human drivers. The real-time motion planning system includes lane-keeping, obstacle avoidance, moving car avoidance, adaptive cruise control, and accident avoidance function. In the research, EGO vehicles will be built and equipped with an image processing unit, a LIDAR, and two ultrasonic sensors to detect the environment. These environment data make it possible to implement a full control program in the real-time motion planning system. The control program will be implemented and tested in a scaled-down EGO vehicle with a scaled-down urban environment. The project has been divided into three phases: build EGO vehicles, implement the control program of the real-time motion planning system, and improve the control program by testing under the scale-down urban environment. In the first phase, each EGO vehicle will be built by an EGO vehicle chassis kit, a Raspberry Pi, a LIDAR, two ultrasonic sensors, a battery, and a power board. In the second phase, the control program of the real-time motion planning system will be implemented under the lane-keeping program in Raspberry Pi. Python is the programming language that will be used to implement the program. Lane-keeping, obstacle avoidance, moving car avoidance, adaptive cruise control functions will be built in this control program. In the last phase, testing and improvement works will be finished. Reliability tests will be designed and fulfilled. The more data grab from tests, the more stability of the real-time motion planning system can be implemented. Finally, one reliable motion planning system will be built, which will be used in normal scale EGO vehicles to reduce accident rates significantly under the urban environment.No embargoAcademic Major: Electrical and Computer Engineerin
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Distribution of Value of Time and Ways to Model Value of Time in Long-Range Planning Models
As managed lanes (ML) become more integrated in regional urban networks with existing general purpose (GP) lanes, the distribution of travelers’ value of time (VOT) is becoming more important for transportation planning agencies to quantify in order to accurately predict future travel patterns. Since travelers’ VOT varies depending on a multitude of factors, this study investigates ways that we can determine the VOT distribution of a region from existing travel data as well as effective ways that we can model VOT using traffic assignment algorithms. In networks with available link volumes and toll data on segments where travelers have the option of choosing to stay on the GP lanes or entering a ML facility, a VOT distribution can be inferred assuming that travelers who enter the ML choose to do so based on a certain “threshold” VOT. When modeling these VOT distributions, errors are observed in the traffic assignment results when both the continuous nature of VOT distributions are discretized, and when varying toll values are assumed to be constant. Specifically in the context of TransCAD software, link travel time errors appear to be much less significant than flow errors when tested on a nine node network. Additional experimentation on larger regional networks is needed to verify the significance of these errors and their impact on predicted travel patterns.Civil, Architectural, and Environmental Engineerin
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