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A genetic algorithm with local search using activity list characteristics for solving resource‐constrained multiproject scheduling problem
Author(s) -
Okada Ikutaro,
Weng Wei,
Yang Wenbai,
Fujimura Shigeru
Publication year - 2016
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22324
Subject(s) - crossover , computer science , mathematical optimization , scheduling (production processes) , local search (optimization) , genetic algorithm , job shop scheduling , operations research , algorithm , artificial intelligence , machine learning , mathematics , schedule , operating system
In this paper, we aim to solve the resource‐constrained multiproject scheduling problem (rc‐mPSP), in which more than one project are scheduled simultaneously, projects share global resources, and the average project delay and total project time are minimized as objectives. In order to solve this problem by a centralized scheduling method, we present a new genetic algorithm (GA) approach. In this procedure, we follow the GA described in Okada et al. . (2014) and improve its genetic operators, such as crossover and mutation, and local search so as to work better on rc‐mPSP. Furthermore, in order to evaluate the proposed approach, we compare the results of the computational experiment on certain standard project instances with the several competing decentralized methods and centralized methods presented in the literature. We show that our procedure is one of the most competitive among such algorithms. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.