565 research outputs found
A bi-objective stochastic approach for stochastic CARP
The Capacitated Arc Routing Problem (CARP) occurs in applications like urban waste collection or winter gritting. It is usually defined in literature on an undirected graph G = (V, E) , with a set V of n nodes and a set E of m edges. A fleet of identical vehicles of capacity Q is based at a depot node. Each edge i has a cost (length) ci and a demand qi (e.g. an amount of waste), and it may be traversed any number of times. The edges with non-zero demands or tasks require service by a vehicle. The goal is to determine a set of vehicle trips (routes) of minimum total cost, such that each trip starts and ends at the depot, each task is serviced by one single trip, and the total demand handled by any vehicle does not exceed Q . To the best of our knowledge the best published method is a memetic algorithm first introduced in 2001. This article provides a new extension of the NSGA II (Non-dominated Sorting Genetic Algorithm) template to comply with the stochastic sight of the CARP. The main contribution is: - to introduce mathematical expression to evaluate both cost and duration of the longest trip and also standard deviation of these two criteria. - to use a NGA-II template to optimize simultaneously the cost and the duration of the longest trip including standard deviation. The numerical experiments managed on the thee well-known benchmark sets of DeArmon, Belenguer and Benavent and Eglese, prove it is possible to obtain robust solutions in four simultaneous criteria in rather short computation times
An optimized ultrasound detector for photoacoustic breast tomography
Photoacoustic imaging has proven to be able to detect vascularization-driven
optical absorption contrast associated with tumors. In order to detect breast
tumors located a few centimeter deep in tissue, a sensitive ultrasound detector
is of crucial importance for photoacoustic mammography. Further, because the
expected photoacoustic frequency bandwidth (a few MHz to tens of kHz) is
inversely proportional to the dimensions of light absorbing structures (0.5 to
10+ mm), proper choices of materials and their geometries, and proper
considerations in design have to be made for optimal photoacoustic detectors.
In this study, we design and evaluate a specialized ultrasound detector for
photoacoustic mammography. Based on the required detector sensitivity and its
frequency response, a selection of active material and matching layers and
their geometries is made leading to a functional detector models. By iteration
between simulation of detector performances, fabrication and experimental
characterization of functional models an optimized implementation is made and
evaluated. The experimental results of the designed first and second functional
detectors matched with the simulations. In subsequent bare piezoelectric
samples the effect of lateral resonances was addressed and their influence
minimized by sub-dicing the samples. Consequently, using simulations, the final
optimized detector could be designed, with a center frequency of 1 MHz and a -6
dB bandwidth of ~80%. The minimum detectable pressure was measured to be 0.5
Pa, which will facilitate deeper imaging compared to the currrent systems. The
detector should be capable of detecting vascularized tumors with resolution of
1-2 mm. Further improvements by proper electrical grounding and shielding and
implementation of this design into an arrayed detector will pave the way for
clinical applications of photoacoustic mammography.Comment: Accepted for publication in Medical Physics (American Association of
Physicists in Medicine
Development of a Quadcopter Test Environment and Research Platform
This thesis first uses a model-based systems engineering approach to model, design, and implement a quadcopter test environment and research platform (TERP). TERP provides quadcopter state information, using a motion capture system, which can be used with custom feedback strategies to enable controlled flight.
Next, it makes use of control theory to develop two controllers for quadcopter flight trajectory tracking: one based on linear quadratic regulation (LQR) and one based on model reference adaption. Simulations of both controllers are done in MATLAB using Simulink and seek to demonstrate the improved performance of the adaptive controller over the LQR controller in flight trajectory tracking with payload uncertainties. Flight tests with the LQR controller are then done to validate the TERP System
Hybrid Job Shop and Parallel Machine Scheduling Problems: Minimization of Total Tardiness Criterion
International audienc
A Hybrid Heuristic for a Broad Class of Vehicle Routing Problems with Heterogeneous Fleet
We consider a family of Rich Vehicle Routing Problems (RVRP) which have the
particularity to combine a heterogeneous fleet with other attributes, such as
backhauls, multiple depots, split deliveries, site dependency, open routes,
duration limits, and time windows. To efficiently solve these problems, we
propose a hybrid metaheuristic which combines an iterated local search with
variable neighborhood descent, for solution improvement, and a set partitioning
formulation, to exploit the memory of the past search. Moreover, we investigate
a class of combined neighborhoods which jointly modify the sequences of visits
and perform either heuristic or optimal reassignments of vehicles to routes. To
the best of our knowledge, this is the first unified approach for a large class
of heterogeneous fleet RVRPs, capable of solving more than 12 problem variants.
The efficiency of the algorithm is evaluated on 643 well-known benchmark
instances, and 71.70\% of the best known solutions are either retrieved or
improved. Moreover, the proposed metaheuristic, which can be considered as a
matheuristic, produces high quality solutions with low standard deviation in
comparison with previous methods. Finally, we observe that the use of combined
neighborhoods does not lead to significant quality gains. Contrary to
intuition, the computational effort seems better spent on more intensive route
optimization rather than on more intelligent and frequent fleet re-assignments
Robust optimization criteria: state-of-the-art and new issues
Uncertain parameters appear in many optimization problems raised by real-world applications. To handle such problems, several approaches to model uncertainty are available, such as stochastic programming and robust optimization. This study is focused on robust optimization, in particular, the criteria to select and determine a robust solution. We provide an overview on robust optimization criteria and introduce two new classifications criteria for measuring the robustness of both scenarios and solutions. They can be used independently or coupled with classical robust optimization criteria and could work as a complementary tool for intensification in local searches
- …