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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|7877||2013||23 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Swarm and Evolutionary Computation, Available online 28 June 2013
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 resource allocation problem. Our approach has two characteristic features. Firstly, a set of nondominated solutions is obtained by exploring the optimal Pareto frontier using differentααcut 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.
Resource allocation problem (RAP) is the process of allocating resources among the various activities, projects or business units for maximization of profit or minimization of cost. Resource allocation has a variety of applications as follows: 1. Product allocation: for allocating a limited number of products among plants such that the incurred cost is minimized. 2. Portfolio optimization: for creating efficient portfolios on allocating funds to stocks or bonds to maximize return for a given level of risk, or to minimize risk for a target rate of return. 3. Capital or project budgeting: for allocating funds to projects that initially consume cash but later generate cash, to maximize a firm's return on capital. 4. Local government audit: for audit scope and auditor tenure and the hours of labor charged to audit activities (e.g., planning, internal control evaluation and testing, etc.). 5. Software testing  and : for optimal testing-resource allocation of modular software systems their efficacy in solving computationally intensive problems. 6. Health care resource allocation: for cost-effectiveness analysis. 7. The cost effectiveness analysis for the health care resource allocation. 8. Network-based cooperative resource allocation strategies: via an imperfect communication network multiple processors can share the load presented by multiple task types. 9. Processor allocation or job shop scheduling: for allocating time for work orders on different types of production equipment, to minimize delivery time or maximize equipment utilization. An almost infinite variety of problems can be tackled this way . Recently, there has been a boom in applying evolutionary algorithms to solve multiobjective optimization problems , ,  and . Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behavior of species. The development of meta-heuristic optimization theory has been flourishing. Many meta-heuristic paradigms such as genetic algorithm  and , simulated annealing , tabu search , and particle swarm optimization PSO  have shown their efficacy in solving computationally intensive problems. Recently the ant colony algorithm (ACO) , ,  and  has become an interesting approach to solve many hard problems because it combines and extends the attractive features of both GA and PSO and has encouraged many researchers to develop ACO variants for tackling well-known NP-hard problems, such as the traveling salesman problem , quadratic assignment problem , scheduling problem , minimum weight vertex cover problem and curve segmentation problem , just to name a few. The development of agriculture is recognized to be an essential preliminary and on-going element in the economic development strategies of most developing countries. In addition, in many countries the agricultural sector is regarded as one with a potential for generating a surplus to sustain other economic activities and to have an important stabilizing role in reducing the movement of population from rural areas. Egyptian interest in desert development has increased in the 20 century by the general authority for desert reclamation, where they implemented pilot reclamation projects and started research in the fields of geology, hydrology, geophysics, topographical survey and soil classification. This paper intends to present a human recourse allocation problem in land reclamation. Since there is instabilities in the global market, implications of global financial crisis and the rapid fluctuations of prices, for this reasons a fuzzy representation of the multiobjective human resource allocation FM-RAP has been defined, where the input data involve many parameters whose possible values may be assigned by the experts. In practice, it is natural to consider that the possible values of these parameters as fuzzy numerical data which can be represented by means of fuzzy subsets of the real line known as fuzzy numbers. Recently, Sakawa  introduced the concept of α-problem based on the α-level sets of the fuzzy. Based on this concept FM-RAP can be transformed to multiobjective resource allocation problem (M-RAP) at certain degree of α (α-cut level). Also, we intend to get a stable Pareto set of 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
نتیجه گیری انگلیسی
Ant colony optimization has been and continues to be a fruitful paradigm for designing effective combinatorial optimization solution algorithms, in this paper; we proposed a new optimization system MM-ACO for solving FM-RAP with an application in reclamation of derelict land in Egypt. Our approach has two characteristic features. Firstly, a set of nondominated solutions is obtained by exploring the optimal Pareto frontier using different αα-cut level and subsequently, based on the definition of Pareto stability, the Pareto frontier may be reduced to a manageable size (i.e., stable Pareto-optimal solutions). The main features of the proposed algorithm could be summarized as follows: (a) The proposed approach is capable of determining the stable Pareto optimal solution with two objectives, with no limitation in handing higher dimensional problems. (b) The size of the Pareto optimal set has been effectively reduced to a manageable size with no further information from DM. (c) Empirically, we demonstrate that our algorithm yields consistently better results. The performance improvement of ACO algorithm still remain in the experimental stage for lack of solid theoretical support; thus, for future work, we intend to test the algorithm on more complex real-world applications. Also, conduct research on the parallel mechanism of the ant colony optimization algorithms so that it improves the efficiency of the algorithm used in the intelligent systems.