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Related Experiment Video

Updated: Apr 26, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Published on: December 9, 2012

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Multiple R&D projects scheduling optimization with improved particle swarm algorithm.

Mengqi Liu1, Miyuan Shan1, Juan Wu1

  • 1School of Business Administration, Hunan University, Changsha, Hunan 410082, China.

Thescientificworldjournal
|July 18, 2014
PubMed
Summary

This study introduces a new model for scheduling multiple research and development (R&D) projects in large make-to-order companies facing resource constraints. An improved particle swarm algorithm effectively optimizes R&D project scheduling for better efficiency.

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Last Updated: Apr 26, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

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Area of Science:

  • Operations Research
  • Management Science
  • Industrial Engineering

Background:

  • Improving R&D capabilities is crucial for enterprises to gain market initiative.
  • Large make-to-order enterprises face challenges in R&D due to constrained human resources and budgets.
  • Timely and cost-effective customer demand fulfillment requires efficient R&D project management.

Purpose of the Study:

  • To address the complexities of multi-project scheduling in resource-constrained R&D environments.
  • To propose a novel multi-project scheduling model tailored for large make-to-order enterprises.
  • To enhance existing algorithms for optimizing R&D project scheduling.

Main Methods:

  • Development of a multi-project scheduling model for a defined period.
  • Improvement of the particle swarm optimization algorithm.
  • Application of the enhanced algorithm to the resource-constrained multi-project scheduling model.
  • Simulation experiments to validate the model and algorithm.

Main Results:

  • The proposed model effectively addresses R&D environments with limited resources.
  • The improved particle swarm algorithm demonstrates validity in optimizing project schedules.
  • Simulation results confirm the feasibility of the scheduling model.
  • The approach enables more timely and cost-effective R&D outcomes.

Conclusions:

  • The developed resource-constrained multi-project scheduling model is feasible for large make-to-order enterprises.
  • The enhanced particle swarm algorithm provides a valid and effective method for R&D project scheduling optimization.
  • This research contributes to improving enterprise competitiveness through efficient R&D management.