Guided restarting local search for production planning

Planning problems can be solved with a large variety of different approaches, and a significant amount of work has been devoted to the automation of planning processes using different kinds of methods. This paper focuses on the use of specific local search algorithms for real-world production planning based on experiments with real-world data, and presents an adapted local search guided by evolutionary metaheuristics. To make algorithms efficient, many specifics need to be considered and included in the problem solving. We demonstrate that the use of specialized local searches can significantly improve the convergence and efficiency of the algorithm. The paper also includes an experimental study of the efficiency of two memetic algorithms, and presents a real-world software implementation for the production planning.

► We optimize real-world-industrial production planning schedule.
► We present an adapted local search guided by evolutionary metaheuristics.
► Solution search is faster and more efficient due to the use of local searches.
► The resulting plans are of much better quality than the expert's manual solutions.
► We present a software implementation for the production planning in the company.