Automatic Generation of Optimization Algorithms for Production Lot-Sizing Problems

Successful applications of heuristic-based methods are able to find high-quality solutions for complex problems in a feasible timeframe. However, they are usually tailored towards the problem instances under consideration and any changes in the underlying problem structure might require a redesign of the algorithm, which is expensive and very time-consuming. This paper presents results of an automatic algorithm-generation approach used to find good-performing optimization methods for the multi-level capacitated lot-sizing problem, a relevant and hard combinatorial problem in production planning. A new template for generating algorithms is proposed for enabling the generation of different hybridizations between genetic algorithm-components and mathematical heuristics. Several experiments are carried out to evaluate the ability of the proposed method to generate competitive algorithms for benchmark instances, under consideration of different functions set and cutoff times. Results indicate that the method is able to generate heuristic algorithms that find high-quality solutions significantly faster than the compared human-designed algorithm.