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Updated: May 18, 2026

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

Multi-objective scheduling optimization for linear megaprojects considering technological innovation.

Xuan Zhao¹, Rui Lu²

¹School of Architecture and Civil Engineering, Xihua University, Chengdu, 610039, China.

Scientific Reports

|May 16, 2026

Summary

This summary is machine-generated.

Incorporating technological innovation time (ET) into construction management is key for linear megaprojects. An optimal schedule balancing cost, time, and robustness is achievable when ET is less than one-eighth of the duration limit.

Keywords:

Genetic simulated annealing algorithm Linear megaprojects Linear scheduling method Multi-objective optimization Technology innovation

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Last Updated: May 18, 2026

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

Area of Science:

Construction Management
Project Scheduling
Technological Innovation

Background:

Linear megaprojects face challenges with uncertainty and long timelines for technological innovation.
Effective integration of expected technological innovation time (ET) is crucial for optimizing project outcomes.

Purpose of the Study:

To propose a multi-objective scheduling optimization model for linear megaprojects.
To incorporate ET into construction management using robust scheduling methods.
To determine the impact of ET on project duration, cost, and robustness.

Main Methods:

Developed a multi-objective optimization model using the Linear Scheduling Method (LSM).
Incorporated ET as buffers predicted by the Graphic Evaluation and Review Technique (GERT).
Employed an improved Nondominated Sorting Genetic Algorithm III (I-NSGA3) for solutions.
Conducted algorithm comparison, sensitivity analysis, and case studies.

Main Results:

The proposed model effectively balances duration, cost, and robustness.
Sensitivity analysis revealed ET thresholds impacting schedule and cost.
Optimal schedules are achieved when ET is less than 1/8th of the duration limit.
Schedule delays increase significantly when ET exceeds 1/2 of the duration limit, with minimal cost changes.

Conclusions:

The model provides a framework for managing technological innovation in linear megaprojects.
ET significantly influences project scheduling, cost, and robustness.
Strategic management of ET is vital for successful megaproject delivery.