Enhancing Manufacturing Cell Formation Through Availability-Based Optimization Using the Black Widow Optimizer...

Area of Science:

• Operations Research
• Manufacturing Systems Engineering
• Industrial Engineering

Background:

• Traditional cell formation problems often overlook machine reliability and repair dynamics.
• Equipment availability significantly impacts production efficiency and cost.
• Dynamic changes in manufacturing systems over time require advanced modeling approaches.

Purpose of the Study:

• To develop a multi-period Generalized Cell Formation Problem with Machine Availability (GCFP-MA) model.
• To integrate equipment reliability (Mean Time Between Failures - MTBF, Mean Time to Repair - MTTR) and temporal degradation into cell design.
• To optimize manufacturing cell configurations considering dynamic system behavior and downtime costs.

Main Methods:

• Formulation of the GCFP-MA incorporating availability-based constraints and downtime penalties.
• Utilization of Markov-Chain models to represent machine availability states.
• Implementation and validation of the Black Widow Optimizer (BWO) metaheuristic for solving the NP-hard optimization problem.

Main Results:

• The Black Widow Optimizer (BWO) effectively finds the global optimum for the GCFP-MA.
• BWO demonstrates significantly reduced computational effort compared to exhaustive search benchmarks.
• The model prevents infeasible or over-optimistic cell designs by considering availability and repair dynamics.

Conclusions:

• Integrating machine availability and repair dynamics leads to more cost-effective and robust manufacturing cell layouts.
• The proposed unified framework enhances production system design by incorporating availability engineering principles.
• The BWO metaheuristic offers an efficient solution for complex cell formation problems with dynamic machine availability.