Effective social spider optimization algorithms for distributed assembly permutation flowshop scheduling problem in...

Area of Science:

• Operations Research
• Industrial Engineering
• Manufacturing Systems

Background:

• Addresses the novel distributed assembly permutation flowshop scheduling problem (DAPFSP) inspired by automotive production complexities.
• Incorporates multi-part components and integrates manufacturing processes, aligning with Industry 4.0 objectives.
• Extends existing DAPFSP research by considering component manufacturing before final product assembly.

Purpose of the Study:

• To develop and evaluate novel algorithms for solving the DAPFSP with the objective of minimizing the makespan.
• To introduce a three-level representation and initialization method tailored for the DAPFSP.
• To enhance the search capabilities of optimization algorithms through problem-specific local search and restart procedures.

Main Methods:

• Introduced a three-level representation and a novel initialization method for the DAPFSP.
• Designed three local search methods and two restart procedures to improve algorithm performance.
• Developed three variants of the Social Spider Optimization (SSO) algorithm: HSSO, HSSOR, and HSSORP, integrating problem-specific knowledge.

Main Results:

• Tested the proposed algorithms on 810 extended instances derived from well-known benchmark problems.
• The HSSOR algorithm demonstrated superior performance, achieving an average comparison metric of 0.158%.
• HSSOR significantly outperformed the best comparison method, which had an average metric of 2.446%, by a factor of 15.481.

Conclusions:

• The proposed algorithms, particularly HSSOR, are efficient in solving the distributed assembly permutation flowshop scheduling problem.
• The integration of local search strategies and restart procedures effectively enhances optimization performance.
• The study provides a valuable contribution to Industry 4.0 by optimizing integrated manufacturing and assembly processes.