A cross-entropy based population learning algorithm for multi-mode resource-constrained project scheduling problem with minimum and maximum time lags

Authors:
Piotr Jedrzejowicz;Aleksander Skakovski
Affiliations:
Gdynia Maritime University, Information Systems, Gdynia, Poland;Department of Navigation, Gdynia Maritime University, Gdynia, Poland
Venue:
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
Year:
2010

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Abstract

The multi-mode resource-constrained project scheduling problem with minimum and maximum time lags is considered in the paper. An activity is performed in a mode, which determines the demand of renewable and nonrenewable resources required for its processing and minimum and maximum time lags between adjacent activities. The goal is to find a mode assignment to the activities and their start times such that all constraints are satisfied and the project duration is minimized. Because the problem is NP-hard a population-learning algorithm (PLA2) is proposed to tackle the problem. PLA2 is a population-based approach which takes advantage of the features common to the social education system rather than to the evolutionary processes. The proposed approach perfectly suits for multi-agent systems because it is based on the idea of constructing a hybrid algorithm integrating different optimization techniques complementing each other and producing a synergetic effect. Results of the experiment were compared to the results published in Project Scheduling Problem Library.