47,982 research outputs found
Optimal Placement of Valves in a Water Distribution Network with CLP(FD)
This paper presents a new application of logic programming to a real-life
problem in hydraulic engineering. The work is developed as a collaboration of
computer scientists and hydraulic engineers, and applies Constraint Logic
Programming to solve a hard combinatorial problem. This application deals with
one aspect of the design of a water distribution network, i.e., the valve
isolation system design.
We take the formulation of the problem by Giustolisi and Savic (2008) and
show how, thanks to constraint propagation, we can get better solutions than
the best solution known in the literature for the Apulian distribution network.
We believe that the area of the so-called hydroinformatics can benefit from
the techniques developed in Constraint Logic Programming and possibly from
other areas of logic programming, such as Answer Set Programming.Comment: Best paper award at the 27th International Conference on Logic
Programming - ICLP 2011; Theory and Practice of Logic Programming, (ICLP'11)
Special Issue, volume 11, issue 4-5, 201
Finding Optimal Strategies in a Multi-Period Multi-Leader-Follower Stackelberg Game Using an Evolutionary Algorithm
Stackelberg games are a classic example of bilevel optimization problems,
which are often encountered in game theory and economics. These are complex
problems with a hierarchical structure, where one optimization task is nested
within the other. Despite a number of studies on handling bilevel optimization
problems, these problems still remain a challenging territory, and existing
methodologies are able to handle only simple problems with few variables under
assumptions of continuity and differentiability. In this paper, we consider a
special case of a multi-period multi-leader-follower Stackelberg competition
model with non-linear cost and demand functions and discrete production
variables. The model has potential applications, for instance in aircraft
manufacturing industry, which is an oligopoly where a few giant firms enjoy a
tremendous commitment power over the other smaller players. We solve cases with
different number of leaders and followers, and show how the entrance or exit of
a player affects the profits of the other players. In the presence of various
model complexities, we use a computationally intensive nested evolutionary
strategy to find an optimal solution for the model. The strategy is evaluated
on a test-suite of bilevel problems, and it has been shown that the method is
successful in handling difficult bilevel problems.Comment: To be published in Computers and Operations Researc
Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks
The proliferation of innovative mobile services such as augmented reality,
networked gaming, and autonomous driving has spurred a growing need for
low-latency access to computing resources that cannot be met solely by existing
centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an
effective solution to meet the demand for low-latency services by enabling the
execution of computing tasks at the network-periphery, in proximity to
end-users. While a number of recent studies have addressed the problem of
determining the execution of service tasks and the routing of user requests to
corresponding edge servers, the focus has primarily been on the efficient
utilization of computing resources, neglecting the fact that non-trivial
amounts of data need to be stored to enable service execution, and that many
emerging services exhibit asymmetric bandwidth requirements. To fill this gap,
we study the joint optimization of service placement and request routing in
MEC-enabled multi-cell networks with multidimensional
(storage-computation-communication) constraints. We show that this problem
generalizes several problems in literature and propose an algorithm that
achieves close-to-optimal performance using randomized rounding. Evaluation
results demonstrate that our approach can effectively utilize the available
resources to maximize the number of requests served by low-latency edge cloud
servers.Comment: IEEE Infocom 201
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