A genetic algorithm/mathematical programming approach to solve a two-level soft drink production problem

• We propose a genetic algorithm with mathematical programming to solve a two-level lot sizing and scheduling problem that arises in soft drink production.
• The genetic algorithm defines sequencing decisions and a linear programming model defines lot sizing.
• This approach is evaluated in instances from real data provided by a soft drink company.
• The method outperforms literature approaches applied to the same problem in terms of solution quality and runtimes.

This study applies a genetic algorithm embedded with mathematical programming techniques to solve a synchronized and integrated two-level lot sizing and scheduling problem motivated by a real-world problem that arises in soft drink production. The problem considers a production process compounded by raw material preparation/storage and soft drink bottling. The lot sizing and scheduling decisions should be made simultaneously for raw material preparation/storage in tanks and soft drink bottling in several production lines minimizing inventory, shortage and setup costs. The literature provides mixed-integer programming models for this problem, as well as solution methods based on evolutionary algorithms and relax-and-fix approaches. The method applied by this paper uses a new approach which combines a genetic algorithm (GA) with mathematical programming techniques. The GA deals with sequencing decisions for production lots, so that an exact method can solve a simplified linear programming model, responsible for lot sizing decisions. The computational results show that this evolutionary/mathematical programming approach outperforms the literature methods in terms of production costs and run times when applied to a set of real-world problem instances provided by a soft drink company.