370,150 research outputs found
DynCNET: a negotiation and coordination protocol for dynamic task assignment.
Task assignment in Multi-Agent Systems is a complex coordination problem, especially in systems that operate under dynamic and changing conditions. Adaptive task assignment is used to handle these dynamic and changing circumstances. This technical document describes an adaptive task assignment protocol, DynCNET which is an extension of the Contract Net Protocol. In this document, the DynCNET protocol will be build step by step, starting from the Contract Net protocol. We will add dynamic task assignment, synchronization of abort messages and scope handling. The final result will be the DynCNET protocol with support for synchronization of abort messages and scope handling.
The dynamic weapon-target assignment problem
Caption title. "To appear in Proc. 1989 Symposium on C2 research, Washington, D.C.Includes bibliographical references.Research supported by the Joint Directors of Laboratories (JDL), Basic Research Group on C3 Systems, under contract with the Office of Naval Research. ONR/N00014-85-K-0782 ONR/N00014-84-K-0519Patrick Hosein, Michael Athans
Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation
One of the major challenges in the coordination of large, open,
collaborative, and commercial vehicle fleets is dynamic task allocation.
Self-concerned individually rational vehicle drivers have both local and global
objectives, which require coordination using some fair and efficient task
allocation method. In this paper, we review the literature on scalable and
dynamic task allocation focusing on deterministic and dynamic two-dimensional
linear assignment problems. We focus on multiagent system representation of
open vehicle fleets where dynamically appearing vehicles are represented by
software agents that should be allocated to a set of dynamically appearing
tasks. We give a comparison and critical analysis of recent research results
focusing on centralized, distributed, and decentralized solution approaches.
Moreover, we propose mathematical models for dynamic versions of the following
assignment problems well known in combinatorial optimization: the assignment
problem, bottleneck assignment problem, fair matching problem, dynamic minimum
deviation assignment problem, -assignment problem, the semiassignment
problem, the assignment problem with side constraints, and the assignment
problem while recognizing agent qualification; all while considering the main
aspect of open vehicle fleets: random arrival of tasks and vehicles (agents)
that may become available after assisting previous tasks or by participating in
the fleet at times based on individual interest
Joint Cache Partition and Job Assignment on Multi-Core Processors
Multicore shared cache processors pose a challenge for designers of embedded
systems who try to achieve minimal and predictable execution time of workloads
consisting of several jobs. To address this challenge the cache is statically
partitioned among the cores and the jobs are assigned to the cores so as to
minimize the makespan. Several heuristic algorithms have been proposed that
jointly decide how to partition the cache among the cores and assign the jobs.
We initiate a theoretical study of this problem which we call the joint cache
partition and job assignment problem.
By a careful analysis of the possible cache partitions we obtain a constant
approximation algorithm for this problem. For some practical special cases we
obtain a 2-approximation algorithm, and show how to improve the approximation
factor even further by allowing the algorithm to use additional cache. We also
study possible improvements that can be obtained by allowing dynamic cache
partitions and dynamic job assignments.
We define a natural special case of the well known scheduling problem on
unrelated machines in which machines are ordered by "strength". Our joint cache
partition and job assignment problem generalizes this scheduling problem which
we think is of independent interest. We give a polynomial time algorithm for
this scheduling problem for instances obtained by fixing the cache partition in
a practical case of the joint cache partition and job assignment problem where
job loads are step functions
Analysis of dynamic system optimal assignment with departure time choice
Most analyses on dynamic system optimal (DSO) assignment are done by using the
control theory with an outflow traffic model. On the one hand, this control theoretical
formulation provides some attractive mathematical properties for analysis. On the
other hand, however, this kind of formulation often ignores the importance of
ensuring proper flow propagation. Moreover, the outflow models have also been
extensively criticized for their implausible traffic behaviour. This paper aims to
provide another framework for analysing a DSO assignment problem based upon
sound traffic models. The assignment problem we considered aims to minimize the
total system cost in a network by seeking an optimal inflow profile within a fixed
planning horizon. This paper first summarizes the requirements on a plausible traffic
model and reviews three common traffic models. The necessary conditions for the
optimization problem are then derived using a calculus of variations technique.
Finally, a simple working example and some concluding remarks are given
A hybrid cross entropy algorithm for solving dynamic transit network design problem
This paper proposes a hybrid multiagent learning algorithm for solving the
dynamic simulation-based bilevel network design problem. The objective is to
determine the op-timal frequency of a multimodal transit network, which
minimizes total users' travel cost and operation cost of transit lines. The
problem is formulated as a bilevel programming problem with equilibrium
constraints describing non-cooperative Nash equilibrium in a dynamic
simulation-based transit assignment context. A hybrid algorithm combing the
cross entropy multiagent learning algorithm and Hooke-Jeeves algorithm is
proposed. Computational results are provided on the Sioux Falls network to
illustrate the perform-ance of the proposed algorithm
A Novel Solution to the Dynamic Routing and Wavelength Assignment Problem in Transparent Optical Networks
We present an evolutionary programming algorithm for solving the dynamic
routing and wavelength assignment (DRWA) problem in optical wavelength-division
multiplexing (WDM) networks under wavelength continuity constraint. We assume
an ideal physical channel and therefore neglect the blocking of connection
requests due to the physical impairments. The problem formulation includes
suitable constraints that enable the algorithm to balance the load among the
individuals and thus results in a lower blocking probability and lower mean
execution time than the existing bio-inspired algorithms available in the
literature for the DRWA problems. Three types of wavelength assignment
techniques, such as First fit, Random, and Round Robin wavelength assignment
techniques have been investigated here. The ability to guarantee both low
blocking probability without any wavelength converters and small delay makes
the improved algorithm very attractive for current optical switching networks.Comment: 12 Pages, IJCNC Journal 201
Traffic models for dynamic system optimal assignment
Most analyses on dynamic system optimal (DSO) assignment are done by using a control theory
with an outflow traffic model. On the one hand, this control theoretical formulation provides some
attractive mathematical properties for analysis. On the other hand, however, this kind of formulation
often ignores the importance of ensuring proper flow propagation. Moreover, the outflow models
have also been extensively criticized for their implausible traffic behaviour. This paper aims to
provide another framework for analysing a DSO assignment problem based upon sound traffic
models. The assignment problem we considered aims to minimize the total system cost in a
network by seeking an optimal inflow profile within a fixed planning horizon. This paper first
summarizes the requirements on a plausible traffic model and reviews three common traffic
models. The necessary conditions for the optimization problem are then derived using a calculus of
variations technique. Finally, a simple working example and concluding remarks are given
Real-Time Heuristics and Metaheuristics for Static and Dynamic Weapon Target Assignments
The problem of targeting and engaging individual missiles (targets) with an arsenal of interceptors (weapons) is known as the weapon target assignment problem. This problem has been well-researched since the seminal work in 1958. There are two distinct categories of the weapon target assignment problem: static and dynamic. The static weapon target assignment problem considers a single instance in which a known number of incoming missiles is to be engaged with a finite number of interceptors. By contrast, the dynamic weapon target assignment problem considers either follow on engagement(s) should the first engagement(s) fail, a subsequent salvo of incoming missiles, or both. This research seeks to define and solve a realistic dynamic model. First, assignment heuristics and metaheuristics are developed to provide rapid near-optimal solutions to the static weapon target assignment. Next, a technique capable of determining how many of each interceptor type to reserve for a second salvo by means of approximate dynamic programming is developed. Lastly, a model that realistically considers erratic flight paths of incoming missiles and determines assignments and firing sequences of interceptors within a simulation to minimize the number of hits to a protected asset is developed. Additionally, the first contemporary survey of the weapon target assignment problem since 1985 is presented. Collectively, this work extends the research of missile defense into practical application more so than currently is found within the literature
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