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PLA Based Strategy for Solving MRCPSP by a Team of Agents

  • Piotr Jędrzejowicz
  • Ewa Ratajczak-RopelEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)

Abstract

In this paper the dynamic interaction strategy based on the Population Learning Algorithm (PLA) for the A-Team solving the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP) is proposed and experimentally validated. The MRCPSP belongs to the NP-hard problem class. To solve this problem a team of asynchronous agents (A-Team) has been implemented using multiagent system. An A-Team is the set of objects including multiple agents and the common memory which through interactions produce solutions of optimization problems. These interactions are usually managed by some static strategy. In this paper the dynamic learning strategy based on PLA is suggested. The proposed strategy supervises interactions between optimization agents and the common memory. To validate the proposed approach computational experiment has been carried out.

Keywords

Multi-mode resource-constrained project scheduling MRCPSP Optimization Agent A-team Population learning algorithm 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Chair of Information SystemsGdynia Maritime UniversityGdyniaPoland

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