Precise algorithms for minimizing feedback in new energy product development

Area of Science:

• Engineering Management
• Operations Research
• Computer Science

Background:

• Efficient project management is crucial for rapid new energy technology development.
• Design Structure Matrix (DSM) models task interdependencies but can suffer from delays due to feedback loops.

Purpose of the Study:

• To address feedback loop issues in DSM for improved project management.
• To develop an efficient algorithm for optimizing task sequences in complex projects.

Main Methods:

• Introduced two novel properties of the feedback minimization problem.
• Developed a Parallel Branch-and-Bound (PBB) algorithm.
• Enhanced the PBB algorithm with hash functions for improved computational efficiency.

Main Results:

• The hash-enhanced PBB algorithm efficiently solves DSM instances up to 40 activities within an hour.
• The proposed algorithm significantly outperforms traditional methods in computational efficiency.
• Demonstrated precise and scalable optimization for complex product development.

Conclusions:

• The hash-enhanced PBB algorithm offers an effective solution for optimizing complex project task sequences.
• This approach enhances efficiency and scalability in managing new energy technology development projects.