Search results for "Resource allocation"
showing 10 items of 142 documents
A new approach for critical resources allocation
2009
This paper presents a solution based on Artificial Intelligence using Multi-objective Genetic Algorithms to optimize the allocation of teachers and classrooms. The implementation was created in order to optimize the process in both cases, allowing them to compete so as to establish a balance and arrive at a feasible solution quickly and efficiently.
A simulation/optimization model for selecting infrastructure alternatives in complex water resource systems
2010
The paper introduces a simulation/optimization procedure for the assessment and the selection of infrastructure alternatives in a complex water resources system, i.e. in a multisource (reservoirs) multipurpose bulk water supply scheme. An infrastucture alternative is here a vector X of n decision variables describing the candidate expansions/new plants/water transfers etc. Each parameter may take on a discrete number of values, with its own investment cost attached. The procedure uses genetic algorithms for the search of the optimal vector X through operators mimicking the mechanisms of natural selection. For each X, the value of the objective function (O.F.) is assessed via a simulation mo…
On Using a Hierarchy of Twofold Resource Allocation Automata to Solve Stochastic Nonlinear Resource Allocation Problems
2007
Recent trends in AI attempt to solve difficult NP-hard problems using intelligent techniques so as to obtain approximately-optimal solutions. In this paper, we consider a family of such problems which fall under the general umbrella of "knapsack-like" problems, and demonstrate how we can solve all of them fast and accurately using a hierarchy of Learning Automata (LA). In a multitude of real-world situations, resources must be allocated based on incomplete and noisy information, which often renders traditional resource allocation techniques ineffective. This paper addresses one such class of problems, namely, Stochastic Non-linear Fractional Knapsack Problems. We first present a completely …
Pre-processing techniques for resource allocation in the heterogeneous case
1998
The Heterogeneous Resource Allocation Problem (HRAP) deals with the allocation of resources, whose units do not all share the same characteristics, to an established plan of activities. Each activity requires one or more units of each resource which possess particular characteristics, and the objective is to find the minimum number of resource units of each type, necessary to carry out all the activities within the plan, in such a way that two activities whose processing overlaps in time do not have the same resource unit assigned. The HRAP is an NP-Complete problem and it is possible to optimally solve medium-sized HRAP instances in a reasonable time. The objective of this work is to devel…
Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm.
2020
Author's accepted manuscript. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this article, we consider the problem of load balancing (LB), but, unlike the approaches that have been proposed earlier, we attempt to resolve the problem in a fair manner (or rather, it would probably be more appropriate to describe it as an ε-fair manner because, although the LB…
Energy-Efficient Resource Allocationin for D2D Enabled Cellular Networks
2020
Energy-efficiency (EE) is critical for D2D enabled cellular networks due to limited battery capacity and severe co-channel interference. In this chapter, we address the EE optimization problem by adopting a stable matching approach. The NP-hard joint resource allocation problem is formulated as a one-to-one matching problem under two-sided preferences, which vary dynamically with channel states and interference levels. A game-theoretic approach is employed to analyze the interactions and correlations among user equipments (UEs), and an iterative power allocation algorithm is developed to establish mutual preferences based on nonlinear fractional programming. We then employ the Gale–Shapley …
Distributed Resource Allocation in Underlay Multicast D2D Communications
2021
Multicast device-to-device communications operating underlay with cellular networks is a spectral efficient technique for disseminating data to nearby receivers. However, due to the critical challenge of having an intelligent interference coordination between multicast groups along with the cellular network, it is necessary to judiciously perform resource allocation for the combined network. In this work, we present a framework for a joint channel and power allocation strategy to maximize the sum rate of the combined network while guaranteeing minimum rate to individual groups and cellular users. The objective function is augmented by an austerity function that penalizes excessive assignmen…
Reliable Multicast D2D Communication Over Multiple Channels in Underlay Cellular Networks
2020
Multicast device-to-device (D2D) communications operating underlay with cellular networks is a spectral efficient technique for disseminating data to the nearby receivers. However, due to critical challenges such as, mitigating mutual interference and unavailability of perfect channel state information (CSI), the resource allocation to multicast groups needs significant attention. In this work, we present a framework for joint channel assignment and power allocation strategy to maximize the sum rate of the combined network. The proposed framework allows access of multiple channels to the multicast groups, thus improving the achievable rate of the individual groups. Furthermore, fairness in …
Learning Automata-Based Solutions to Stochastic Nonlinear Resource Allocation Problems
2009
“Computational Intelligence” is an extremely wide-ranging and all-encompassing area. However, it is fair to say that the strength of a system that possesses “Computational Intelligence” can be quantified by its ability to solve problems that are intrinsically hard. One such class of NP-Hard problems concerns the so-called family of Knapsack Problems, and in this Chapter, we shall explain how a sub-field of Artificial Intelligence, namely that which involves “Learning Automata”, can be used to produce fast and accurate solutions to “difficult” and randomized versions of the Knapsack problem (KP).
Joint Optimization of Detection Threshold and Resource Allocation in Infrastructure-based Multi-band Cognitive Radio Networks
2012
[EN] Consider an infrastructure-based multi-band cognitive radio network (CRN) where secondary users (SUs) opportunistically access a set of sub-carriers when sensed as idle. The carrier sensing threshold which affects the access opportunities of SUs is conventionally regarded as static and treated independently from the resource allocation in the model. In this article, we study jointly the optimization of detection threshold and resource allocation with the goal of maximizing the total downlink capacity of SUs in such CRNs. The optimization problem is formulated considering three sets of variables, i.e., detection threshold, sub-carrier assignment and power allocation, with constraints on…