Dynamic programming (DP) is a mathematical procedure designed primarily to improve the computational efficiency of solving select mathematical programming problems by decomposing them into smaller, and hence computati...
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Dynamic programming (DP) is a mathematical procedure designed primarily to improve the computational efficiency of solving select mathematical programming problems by decomposing them into smaller, and hence computationally simpler, subproblems. In solving Multiple objectives Dynamic Programming problem (MODP), classical approaches reduce the multiple objectives into a single objective of minimizing a weighted sum of objectives. The determination of these weights indicate the relative importance of the various objective. Also, if the problem scale increases, it become difficult to be dealt with even in the case of single objective because of the rapid expansion of the number of states to be considered. In this paper, we investigated the possibility of using genetic algorithms (GAs) to solve multiobjective resource allocation problems (MORAPs). This procedure eliminates the need of any user defined weight factor for each objective. Also, the proposed approach is developed to deal with the problems with both single or multiple objectives. The simulation results for multiobjective resource allocation problems (MORAP) shows that genetic algorithms (GAs) may hopefully be a new approach for such kinds of problems. (c) 2004 Published by Elsevier Inc.
The assignment of multiobjective human resources is a very important phase of the decision-making process, especially with respect to research and development projects where performance strongly depends on human resou...
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The assignment of multiobjective human resources is a very important phase of the decision-making process, especially with respect to research and development projects where performance strongly depends on human resources capabilities. Unfortunately, the input data or related parameters are frequently imprecise/fuzzy owing to incomplete or unobtainable information, which can be represented as a fuzzy numbers. This paper presents a multiobjective multipheromone ant colony optimization approach (MM-ACO) with an application in fuzzy multiobjective human resourceallocationproblem. Our approach has two characteristic features. Firstly, a set of nondominated solutions is obtained by exploring the optimal Pareto frontier using different a cut level and subsequently, based on the definition of Pareto stability, the Pareto frontier may be reduced to manageable sizes (i.e., stable Pareto optimal solutions) where in a practical sense only Pareto optimal solutions that are stable are of interest, since there are always uncertainties associated with the efficiency data. Furthermore, we provided an example of optimum utilization of human resources in reclamation of derelict land in Toshka Egypt. (C) 2013 Elsevier B.V. All rights reserved.
In this paper, we investigate the Energy Efficiency (EE)- Spectrum Efficiency (SE) tradeoff issue in an OFDM-based cognitive radio (CR) network. A multi-objective resourceallocationproblem is formulated, where we tr...
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ISBN:
(纸本)9781479935130
In this paper, we investigate the Energy Efficiency (EE)- Spectrum Efficiency (SE) tradeoff issue in an OFDM-based cognitive radio (CR) network. A multi-objective resourceallocationproblem is formulated, where we try to maximize the EE and the SE simultaneously. The Pareto optimal set of the formulated problem is characterized by analyzing the relationship between the EE and the SE. To find a unique globally optimal solution, we proposed a unified EE-SE tradeoff metric, based on which the original optimization task is transformed into a single-objective problem that has a D.C. (Difference of two Convex functions/sets) structure. Then an efficient barrier method is developed, where we speeds up the time-consuming computation of Newton step by exploiting the structure of the D.C. programming problem. Simulation results validate the effectiveness and efficiency of the proposed algorithm. Our general problem formulation sheds some insights on how to design an energy- and spectrum-efficient CR system.
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