Environment-Aware Production Schedulingfor Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization...

Area of Science:

• Industrial Engineering
• Operations Research
• Environmental Management

Background:

• Traditional production scheduling prioritizes profit over environmental impact, neglecting emissions.
• Manufacturing processes, like auto paint shops, generate significant chemical pollutants during color changes.
• Interdependencies between production stages (e.g., paint shop and assembly) complicate scheduling and impact delivery times.

Purpose of the Study:

• To develop an environment-aware production scheduling model for automobile paint shops.
• To minimize chemical pollutant emissions from equipment cleaning during color changes.
• To simultaneously reduce due date violations in downstream assembly by considering inter-shop dependencies.

Main Methods:

• Formulation of a bi-objective optimization problem using mixed-integer programming.
• Development of a novel multi-objective particle swarm optimization (MOPSO) algorithm with problem-specific enhancements.
• Implementation of a branch-and-bound algorithm for precise evaluation of optimal solutions.

Main Results:

• The proposed MOPSO algorithm achieves solution quality comparable to exact solvers on small problem instances.
• MOPSO demonstrates superior performance against state-of-the-art multi-objective optimizers on large-scale instances (up to 200 cars).
• The approach effectively balances environmental objectives (pollutant reduction) with operational goals (due date adherence).

Conclusions:

• The developed MOPSO algorithm offers an effective solution for environment-aware production scheduling in complex manufacturing settings.
• This research bridges the gap between profit-driven scheduling and environmental sustainability in the automotive industry.
• The findings provide a practical framework for reducing emissions and improving efficiency in paint shop operations.