Resource Leveling Based on Ant Colony Algorithm and MATLAB Optimization Decision

Abstract

Abstract:

The resource leveling problem has been shown to belong to NP-hard problem in combinatorial optimization. As the network becomes complex, it is difficult to solve the problem with traditional mathematical programming and the heuristic algorithms. The sum of standard deviation of resources is used as the measure of resource leveling, and a mathematical model of resource leveling optimization decisions is built. Secondly, using Matlab, design steps of ant colony algorithm are realized, and using ant colony algorithm, the scope of the non-critical process start work time determined, the ants randomly distributed in the feasible region. Then the ants will conduct global search and local search according to the transition probability, to solve the global optimization of resource leveling. Finally, a single resource leveling and multi-resource leveling are taken for examples, to verify the effectiveness of ant colony algorithm. The convergence speed of the algorithm is fast, and it overcomes the defects of traditional algorithm of easily falling into local optimum, and it is suitable for solving the problems of all resources leveling, with good generality. But the optimization model of this paper assumes that any activity must be in continuous construction, and in between all kinds of resources are independent of each other. It is often not consistent with actual situation, and future research can be further optimized, and the ant colony algorithm can be combined with other algorithms to solve the problem.

Key words: resource leveling, ant colony algorithm, optimization decision

He Li-juan, Li Xin. Resource Leveling Based on Ant Colony Algorithm and MATLAB Optimization Decision[J]. Industrial Engineering Journal .

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