A multi-agent learning approach for the multi-mode resource-constrained project scheduling problem

This paper introduces a novel approach for solving the multi-mode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. The new approach applies simple agent learning devices, i.e. learning automata,to construct the project schedules. We present some comparative results, to show that our decentralized method can easily compete with the best performing algorithms for the MRCPSP.status: publishe