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Related Experiment Videos

Multimode resource-constrained multiple project scheduling problem under fuzzy random environment and its application

Jiuping Xu1, Cuiying Feng2

  • 1State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064, China ; Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, China.

Thescientificworldjournal
|February 20, 2014
PubMed
Summary

This study develops a new optimization model and algorithm for large-scale construction projects facing uncertainty. It balances cost, project duration, and quality in complex, multi-project environments.

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Area of Science:

  • Operations Research
  • Construction Management
  • Optimization

Background:

  • Large-scale construction projects often involve multiple parallel activities and face uncertain, fuzzy random environments.
  • Traditional project scheduling models may not adequately address the trade-offs between cost, makespan, and quality under such uncertainties.
  • Effective resource and activity management is crucial for project success in complex scenarios.

Purpose of the Study:

  • To extend the multimode resource-constrained project scheduling problem (MRCPSP) for large-scale construction with parallel projects and fuzzy random environments.
  • To construct a cost/weighted makespan/quality trade-off optimization model.
  • To develop an efficient hybrid optimization algorithm for the proposed model.

Main Methods:

  • A hybrid crisp approach was used to transform fuzzy random parameters into fuzzy variables, then defuzzified using an expected value operator with an optimistic-pessimistic index.
  • A combinatorial-priority-based hybrid particle swarm optimization (PSO) algorithm was developed.
  • Combinatorial PSO assigns activity modes, while priority-based PSO schedules activities.

Main Results:

  • The proposed model and algorithm were applied to a large-scale hydropower construction project.
  • The results demonstrated the practicality and efficiency of the developed optimization approach.
  • The method effectively handles trade-offs between cost, weighted makespan, and quality in uncertain environments.

Conclusions:

  • The extended MRCPSP model effectively addresses complex construction project scheduling challenges.
  • The hybrid PSO algorithm provides an efficient solution for optimizing project cost, makespan, and quality.
  • The approach offers a valuable tool for managing large-scale, uncertain construction projects.