Abstract
In this paper, a multi-agent system (MAS) based on the A-Team concept is proposed to solve the Distributed Resource-Constrained Multi-Project Scheduling Problem (DRCMPSP). In the DRCMPSP, multiple distributed projects are considered. Hence, the local task schedule for each project and a coordination of the shared decisions are considered. The DRCMPSP belongs to the class of the strongly NP-hard optimization problems. Multi-agent system seems the natural way of solving such problems. The A-Team MAS, proposed in this paper, has been built using the JABAT environment where two types of the optimization agents are used: local and global. Local optimization agents are used to find solutions for the local projects, and global optimization agents are responsible for the coordination of the local projects and for finding the global solutions. The approach has been tested experimentally using 140 benchmark problem instances from MPSPLIB library with minimizing the Average Project Delay (APD) as global optimization criterion.
Highlights
Global companies from construction, development, commerce and other industries often run multiple projects located all over the world
More practical models assume that multiple projects are located in di®erent places and each local project is managed by autonomous decision maker, i.e. local project manager who has to deal with the asymmetric distribution of information
The part of the system responsible for solving local projects resource-constrained project scheduling problem (RCPSP) with makespan minimalization consists of a set of solution managers (SolutionManager), each of them cooperates with a set of optimization agents (OptiAgent) according to the dynamic cooperation based on integration (DCI) strategy
Summary
Global companies from construction, development, commerce and other industries often run multiple projects located all over the world. Agents of therst type are called local optimization agents They use the local cooperation strategy andnd solutions of the local projects. They use global cooperation strategy andnd the global solutions Both types of agents implement the dedicated heuristic and metaheuristic algorithms, for example, local search or path relinking algorithm. The system has been described in more details and after additional tests, the second version of the ATMAS has been presented where the additional optimization agent implementing 3Opt heuristic has been used. The results of both versions have been compared.
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