Stochastic Project Scheduling Optimization for Multi-stage Prefabricated Building Construction with Reliability Application

This study investigates a project scheduling problem of prefabricated building (PB) construction in an uncertain environment. Different from the traditional scheduling models in PB construction, we consider a complex multi-stage cooperation system including the production, transportation and assembl...

Full description

Saved in:
Bibliographic Details
Published in:KSCE journal of civil engineering 2023, 27(6), , pp.2356-2371
Main Authors: Wang, Jingjing, Liu, Huimin, Wang, Zongxi
Format: Article
Language:English
Subjects:
Algorithms
Civil Engineering
Constraint modelling
Construction
Construction Management
Engineering
Evolution
Evolutionary algorithms
Evolutionary computation
Geotechnical Engineering & Applied Earth Sciences
Heuristic methods
Industrial Pollution Prevention
Optimization
Particle swarm optimization
Polynomials
Prefabricated buildings
Probability theory
Project management
Reliability
Reliability analysis
Scheduling
Stochasticity
토목공학
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study investigates a project scheduling problem of prefabricated building (PB) construction in an uncertain environment. Different from the traditional scheduling models in PB construction, we consider a complex multi-stage cooperation system including the production, transportation and assembly (PTA) phases. In this system, both activity durations and resource amounts are stochastic variables. By applying the reliability theory to the stochastic scheduling model innovatively, we formulate a duration reliability model to maximize the probability of non-delayed project completion, within the resource constraints. As the proposed model is a non-deterministic polynomial hard (NP-hard) problem, a hybrid meta-heuristic differential evolution particle swarm optimization (DEPSO) algorithm is developed, which is utilized the mutation factor of the differential evolution (DE) algorithm in the framework of the particle swarm optimization (PSO). Finally, a real-life example of a PB construction project is used to explore the performance of the proposed DEPSO algorithm. The result shows that the hybrid algorithm DEPSO can better find the global optimal solution in the multi-dimensional optimization problem.
ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-023-2164-8