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An estimation of distribution algorithm and new computational results for the stochastic resource-constrained project scheduling problem

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Abstract

In this paper we propose an estimation of distribution algorithm (EDA) to solve the stochastic resource-constrained project scheduling problem. The algorithm employs a novel probability model as well as a permutation-based local search. In a comprehensive computational study, we scrutinize the performance of EDA on a set of widely used benchmark instances. Thereby, we analyze the impact of different problem parameters as well as the variance of activity durations. By benchmarking EDA with state-of-the-art algorithms, we can show that its performance compares very favorably to the latter, with a clear dominance in instances with medium to high variance of activity duration.

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Acknowledgments

This paper was written during Chen Fang’s one year research stay at the TUM School of Management. The authors thank two anonymous reviewers for their valuable comments. This research has been partially supported by National Key Basic Research and Development Program of China (Grant No. 2013CB329503), National Science Foundation of China (Grant No. 61174189), and Doctoral Program Foundation of Institutions of Higher Education of China (Grant No. 20130002110057).

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Correspondence to Rainer Kolisch.

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Fang, C., Kolisch, R., Wang, L. et al. An estimation of distribution algorithm and new computational results for the stochastic resource-constrained project scheduling problem. Flex Serv Manuf J 27, 585–605 (2015). https://doi.org/10.1007/s10696-015-9210-x

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