Number

Key Focus

Methodology

Advantage

Limitation

[1]

GA & IFC for prefab scheduling

Genetic Algorithm, BIM integration

High accuracy in scheduling

Complexity in GA implementation

[2]

Lean production scheduling

Enhanced Biogeography-Based Optimization

Effective real-time adjustments

Limited to specific scenarios

[3]

Cost-effective scheduling

Discrete Differential Evolution Algorithm

Cost reduction in production

Requires specific parameter tuning

[4]

IoT for real-time planning in prefab

IoT, Multistage Decision-Making

Real-time data utilization

Dependency on IoT infrastructure

[5]

Parallel operations of serial machines

Genetic Algorithm-based model

Improves overall production time

Complex model implementation

[6]

Resource allocation & machine maintenance

Differential Evolution Algorithm

Balances resources and maintenance needs

May not suit all production scales

[7]

Managing constraints in PHP

Smart Work Packaging, Smart Construction Objects

Efficient PHP process management

Framework validation in real scenarios

[8]

Multi-project scheduling

Niche Genetic-Raccoon Family Optimization

Effective in complex project scenarios

Complex algorithm design

[9]

Site selection for PBIPs

Bi-level programming, Genetic Algorithm, Partan Frank-Wolfe algorithm

Economic and environmental balance

Specific to regional characteristics

[10]

Addressing operational uncertainties in precast production

Simulation-GA Hybrid Model

Cost-effective production scheduling

Requires detailed operational data

[11]

Reducing on-site production time variation

Game theory models

Better time management

Complexity in implementation

[12]

Multi-shift precast production scheduling

Flowshop scheduling model

Enhanced time and resource management

Limited to specific production settings

[13]

Addressing process connection and blocking

Genetic algorithm

Minimized delays

Specific to flowshop environments

[14]

Balancing production and transportation scheduling

Genetic algorithm

Cost and time efficiency

Dependency on specific transport conditions

[15]

Resource-constrained precast component production

Advanced scheduling model

Adaptable to diverse conditions

Requires detailed resource data

[16]

Prefabricated building production scheduling

Hybrid optimization algorithm

Effective in complex scheduling

Algorithm complexity

[17]

Resource-constrained scheduling

Optimization models

Better handling of constraints

Complexity in resource management

[18]

Fuzzy logic in project scheduling

Multi-objective optimization

Flexibility in uncertain durations

Dependence on accurate fuzzy modeling

[19]

Balancing MTO and MTS in production

Hybrid flow shop model

Improved production flexibility

Requires specific production setup

[20]

Prefabricated component scheduling

Artificial Fish Swarm Algorithm

Enhanced optimization capabilities

Algorithm complexity and setup

[21]

Process connection and blocking in precast production

Genetic algorithm

Improved production flow

Specific to precast production environments

[22]

Production layout optimization

Layout optimization model

Space and resource optimization

Specific to layout constraints

[23]

Fuzzy logic in production planning

Cooperative co-evolution algorithm

Adaptable to uncertain conditions

Complexity in algorithm implementation

[24]

Flow shop scheduling in precast production

Mixed-Integer Linear Programming

Precise and robust solutions

Requires extensive computational resources

[25]

Network planning in precast project scheduling

Network planning techniques

Reduced lead times and costs

Limited to network-compatible projects

[26]

Lean and BIM in ETO prefab systems

Lean principles, BIM integration

Improved project efficiency

Requires Lean and BIM expertise

[27]

Scheduling optimization in prefab construction

Discrete Cuckoo Search Algorithm

Enhanced optimization capabilities

Algorithm complexity and setup

[28]

Scheduling with preventive maintenance

Joint optimization method

Reliable and sustainable processes

Complexity in joint optimization

[29]

Mixed production line efficiency

Ant Colony Optimization Algorithm

Efficient line configuration

Specific to mixed production environments

[30]

Transportation scheduling of prefab components

Hybrid optimization algorithm

Reduced transportation costs

Depends on accurate algorithm tuning

[31]

Lean planning in precast production

Discrete Event Simulation

Waste reduction and efficiency

Reliance on precise simulation modeling

[32]

Production scheduling optimization

Automated optimization techniques

Improved efficiency and accuracy

Complexity in implementation

[33]

Demand fluctuation in prefab production

Dynamic scheduling model

Reduced waste, better resource utilization

Requires accurate demand forecasting

[34]

Scheduling in construction projects

Genetic Algorithm

Reduced time and cost overruns

Dependency on algorithm parameter tuning

[35]

Optimization in PCs production

Gene Expression Programming (GEP)

Effective handling of due date variations

Complex algorithm design

[36]

MOO in project management

multi-objective optimization techniques

Balances multiple project objectives

Requires sophisticated computational tools

[37]

Synergy of BIM and prefabrication

Building Information Modeling (BIM)

Enhances design accuracy and efficiency

Requires expertise in BIM

[38]

Supply chain management in construction

supply chain management (SCM)

Identifies key areas in off-site SCM

Limited integration of new technologies

[39]

Metaheuristics in modular construction

simulation models, optimization algorithms

Effective in complex scheduling

Specific to modular construction contexts

[40]

Optimization in precast production scheduling

linear programming, heuristic approaches

Focus on mainstream practices

May not cover all off-site construction scenarios