Multiagent genetic optimisation to solve the project scheduling problem under uncertainty

This paper considers a project scheduling problem under uncertainty, which belongs to a class of mult iobjective problems of complex systems control whose decision search time grows exponentially depending on the problem d imension. In this paper, we propose a multiagent genetic opti misation method based on evolutionary and multiagent modelli ng by implementing different decision searching strategie s, including a simulation module and numerical methods applicati on. The comparative analysis of the scheduling methods has shown that the proposed method supports all features that migh t be useful in effective decision searching of the stochastic s cheduling problem. The proposed multiagent genetic optimisati on method, the MS Project resource reallocation method , and a heuristic simulation method were compared whilst adressing a real-world deterministic scheduling problem. The comparison has shown: firstly, the unsuitability of the MS Project planning method for solving the formulated problem; and secondly, both the advantage of the multiagent genetic optimisation method in terms of economic effect and disadvantage in terms of performance. Experimental results in conditions of uncertainty demonstrate the e ffectiveness of the proposed method. Some techniques to reduce the impa ct of the method’s disadvantage are proposed in the conclusio n, as well as the aims of future work. Keywords-project scheduling; genetic algorithms; simulation; subcontract work optimisation; problem under