6,725 research outputs found
An event-based architecture for solving constraint satisfaction problems
Constraint satisfaction problems (CSPs) are typically solved using
conventional von Neumann computing architectures. However, these architectures
do not reflect the distributed nature of many of these problems and are thus
ill-suited to solving them. In this paper we present a hybrid analog/digital
hardware architecture specifically designed to solve such problems. We cast
CSPs as networks of stereotyped multi-stable oscillatory elements that
communicate using digital pulses, or events. The oscillatory elements are
implemented using analog non-stochastic circuits. The non-repeating phase
relations among the oscillatory elements drive the exploration of the solution
space. We show that this hardware architecture can yield state-of-the-art
performance on a number of CSPs under reasonable assumptions on the
implementation. We present measurements from a prototype electronic chip to
demonstrate that a physical implementation of the proposed architecture is
robust to practical non-idealities and to validate the theory proposed.Comment: First two authors contributed equally to this wor
A new algorithm for the 2-period Balanced Traveling Salesman Problem in Euclidean graphs
In a previous paper, we proposed two heuristic algorithms for the euclidean 2-period Balanced Travelling Salesman Problem (2B-TSP). In this problem, which arises from a similar one introduced by Butler et al., a certain number of customers must be visited at minimum total cost over a period of two days: some customers must be visited daily, and the others on alternate days (even or odd days). Moreover, the number of customers visited in every tour must be âbalancedâ, i.e. it must be the same or, alternatively, the difference between the maximum and the minimum number of visited customers must be less than a given threshold: this kind of constraint does not appear explicitly in the paper by Butler. In this paper a third algorithm is presented which overcomes some inadequacy of the algorithm A2 we proposed in the previous paper. The new algorithmâs performance is then analyzed, with respect particularly with the first proposed algorithm.period routing problem, period travelling salesman problem, logistic, heuristic algorithms
QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
In this article, we present QuASeR, a reference-free DNA sequence
reconstruction implementation via de novo assembly on both gate-based and
quantum annealing platforms. Each one of the four steps of the implementation
(TSP, QUBO, Hamiltonians and QAOA) is explained with simple proof-of-concept
examples to target both the genomics research community and quantum application
developers in a self-contained manner. The details of the implementation are
discussed for the various layers of the quantum full-stack accelerator design.
We also highlight the limitations of current classical simulation and available
quantum hardware systems. The implementation is open-source and can be found on
https://github.com/prince-ph0en1x/QuASeR.Comment: 24 page
A Multi-Objective Optimization Approach for Multi-Head Beam-Type Placement Machines
This paper addresses a highly challenging scheduling problem in the field of printed circuit board (PCB) assembly systems using Surface Mounting Devices (SMD). After describing some challenging optimization sub-problems relating to the heads of multi-head surface mounting placement machines, we formulate an integrated multi-objective mathematical model considering of two main sub-problems simultaneously. The proposed model is a mixed integer nonlinear programming one which is very complex to be solved optimally. Therefore, it is first converted into a linearized model and then solved using an efficient multi-objective approach, i.e., the augmented epsilon constraint method. An illustrative example is also provided to show the usefulness and applicability of the proposed model and solution method.PCB assembly. Multi-head beam-type placement machine. Multi-objective mathematical programming. Augmented epsilon-constraint method
The 2-period balanced traveling salesman problem
In the 2-period Balanced Traveling Salesman Problem (2B-TSP), the customers must be visited over a period of two days: some must be visited daily, and the others on alternate days (even or odd days); moreover, the number of customers visited in every tour must be balancedĂą, i.e. it must be the same or, alternatively, the difference between the maximum and the minimum number of visited customers must be less than a given threshold. The salesman's objective is to minimize the total distance travelled over the two tours. Although this problem may be viewed as a particular case of the Period Traveling Salesman Problem, in the 2-period Balanced TSP the assumptions allow for emphasizing on routing aspects, more than on the assignment of the customers to the various days of the period. The paper proposes two heuristic algorithms particularly suited for the case of Euclidean distances between the customers. Computational experiences and a comparison between the two algorithms are also given.
Optimal Recombination in Genetic Algorithms
This paper surveys results on complexity of the optimal recombination problem
(ORP), which consists in finding the best possible offspring as a result of a
recombination operator in a genetic algorithm, given two parent solutions. We
consider efficient reductions of the ORPs, allowing to establish polynomial
solvability or NP-hardness of the ORPs, as well as direct proofs of hardness
results
Parallel drone scheduling vehicle routing problems with collective drones
We study last-mile delivery problems where trucks and drones collaborate to
deliver goods to final customers. In particular, we focus on problem settings
where either a single truck or a fleet with several homogeneous trucks work in
parallel to drones, and drones have the capability of collaborating for
delivering missions. This cooperative behaviour of the drones, which are able
to connect to each other and work together for some delivery tasks, enhance
their potential, since connected drone has increased lifting capabilities and
can fly at higher speed, overcoming the main limitations of the setting where
the drones can only work independently.
In this work, we contribute a Constraint Programming model and a valid
inequality for the version of the problem with one truck, namely the
\emph{Parallel Drone Scheduling Traveling Salesman Problem with Collective
Drones} and we introduce for the first time the variant with multiple trucks,
called the \emph{Parallel Drone Scheduling Vehicle Routing Problem with
Collective Drones}. For the latter variant, we propose two Constraint
Programming models and a Mixed Integer Linear Programming model.
An extensive experimental campaign leads to state-of-the-art results for the
problem with one truck and some understanding of the presented models'
behaviour on the version with multiple trucks. Some insights about future
research are finally discussed
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