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Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

Ning Yang, Shiaaulir Wang, Paul Schonfeld
Copyright: © 2015 |Volume: 6 |Issue: 1 |Pages: 15
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781466677999|DOI: 10.4018/ijoris.2015010104
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MLA

Yang, Ning, et al. "Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm." IJORIS vol.6, no.1 2015: pp.49-63. http://doi.org/10.4018/ijoris.2015010104

APA

Yang, N., Wang, S., & Schonfeld, P. (2015). Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm. International Journal of Operations Research and Information Systems (IJORIS), 6(1), 49-63. http://doi.org/10.4018/ijoris.2015010104

Chicago

Yang, Ning, Shiaaulir Wang, and Paul Schonfeld. "Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm," International Journal of Operations Research and Information Systems (IJORIS) 6, no.1: 49-63. http://doi.org/10.4018/ijoris.2015010104

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Abstract

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

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