Two hybrid flow shop scheduling lines with assembly stage and compatibility constraints

Area of Science:

• Operations Research
• Manufacturing Systems Engineering
• Industrial Engineering

Background:

• Coordinating multiple production lines presents significant scheduling challenges.
• Simultaneous arrival of jobs at assembly stages is critical for product completion.
• Optimizing makespan in hybrid flow shop environments requires advanced methodologies.

Purpose of the Study:

• To develop and evaluate methods for coordinating two hybrid flow shop scheduling lines.
• To minimize the makespan for assembling products composed of jobs from different lines.
• To ensure simultaneous arrival of product-forming jobs at the final assembly stage.

Main Methods:

• Formulation of a mixed integer linear programming (MILP) model.
• Development of a pull-matheuristic algorithm based on the MILP model.
• Implementation and comparison of two metaheuristics: Greedy Randomized Adaptive Search Procedure (GRASP) and Biased Random Key Genetic Algorithm (BRKGA).

Main Results:

• The GRASP algorithm achieved high-quality solutions for the production scheduling problem.
• The BRKGA demonstrated superior computational times compared to other methods.
• All proposed methodologies were validated using real-based instances from the automobile manufacturing industry.

Conclusions:

• The study provides effective optimization strategies for complex hybrid flow shop scheduling.
• Both heuristic and metaheuristic approaches offer viable solutions for minimizing makespan and ensuring timely assembly.
• The findings are directly applicable to optimizing scheduling in the automotive manufacturing sector.