A genetic algorithm with local search using activity list characteristics for solving resource-constrained multiproject scheduling problem

Ikutaro Okada, Wei Weng, Wenbai Yang, Shigeru Fujimura

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)S34-S43
JournalIEEJ Transactions on Electrical and Electronic Engineering
Volume11
DOIs
Publication statusPublished - 2016 Dec 1

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Genetic algorithms
Scheduling
Mathematical operators
Local search (optimization)
Experiments

Keywords

  • activity list
  • critical path improvement local search
  • critical path-based mutation
  • genetic algorithm
  • resource-constrained multiproject scheduling problem

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

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abstract = "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.",
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AB - 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.

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