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Structural Design and Manufacturing of a Cruiser Class Solar Vehicle

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Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios.

Ricardo Soto¹, Broderick Crawford¹, Angelo Aste Toledo¹

¹Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile.

Computational Intelligence and Neuroscience

|March 26, 2019

Summary

This summary is machine-generated.

This study introduces Binary Cat Swarm Optimization (BCSO) to solve the Manufacturing Cell Design Problem (MCDP), minimizing part transportation. The developed BCSO algorithm effectively found optimal solutions for numerous industrial production plant instances.

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Area of Science:

Operations Research
Industrial Engineering
Computational Intelligence

Background:

The Manufacturing Cell Design Problem (MCDP) aims to partition production plants into cells to minimize inter-cell part transportation.
Effective cell organization is crucial for optimizing manufacturing efficiency and reducing logistical costs.

Purpose of the Study:

To present a Binary Cat Swarm Optimization (BCSO) algorithm for solving the MCDP.
To evaluate the performance of BCSO, including a novel Autonomous Search variant with dynamic mixture ratios.

Main Methods:

Developed a Binary Cat Swarm Optimization (BCSO) algorithm inspired by feline behavior (seeking and tracing modes).
Implemented an Autonomous Search algorithm with dynamic mixture ratios for enhanced BCSO performance.
Tested algorithms on 90 known instances and 35 new instances of the MCDP.

Main Results:

Both standard BCSO and the Autonomous Search BCSO achieved global optimums for all tested MCDP instances.
The algorithms demonstrated high effectiveness in finding optimal cell organizations.

Conclusions:

Binary Cat Swarm Optimization is a highly effective metaheuristic for solving the Manufacturing Cell Design Problem.
The proposed BCSO variants provide robust solutions for optimizing manufacturing cell organization and minimizing transportation.