| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 96 === Resource Allocation Problems (RAP) have been applied to many fields including Global Allocation Problem, Medical Material Allocation Problem, and Budget Allocation Problem, etc. Many researchers devote their efforts in developing new methods for tackling this problem. In reality, how to optimize the allocation of limited resources while satisfying all capacity constraints simultaneously, has caused much attention from researchers and practitioners.
RAP belongs to NP-hard problem, but most existing methods for solving RAP are mathematical programming approaches, such as dynamic programming, linear programming, and branch and bound. However, as the number of variables and constraints increase, these exact methods will have to spend a lot of time to find the optimal solution. This research, therefore, aims at applying a metaheuristics - Variable Neighborhood Search (VNS) to solve RAP. This method employs the systematic neighborhood local search to obtain the near-optimal solutions. Different from the traditional VNS structures, the proposed algorithm adopts a dynamic neighborhood structure, i.e., the number of neighborhoods is determined after the shaking operation. The test on a set of benchmark instances has successfully shown the effectiveness and efficiency of the proposed VNS algorithm on solving large size RAPs.
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