Martusorn Khaengkhan, Phornprom Rungrueang, Chanicha Moryadee, Wawmayura Chamsuk, Kiatkulchai Jitt-Aer
This research presented a multi-level lot sizing problem application with an extensive model to solve a problem with a real demand forecasted from a sales record. The model here is defined as multi-item multi-level multi-period capacitated lot sizing by using Genetic algorithm. The first experiment is focused on finding the best solution found in the model compared to the initial feasible solution by using different crossover rates and mutation rates. The second experiment is an extension to the solution found in the first experiment, by conducting four case studies, each with a different cost reduction. Therefore, it is aimed to find the effect of each cost on the solution and provide a recommendation to the manufacturer on the cost effect. For the forecasting model, all products have a decreasing trend both in the sales records and in the forecasted data. For the lot sizing model, the best solution for the model is obtained by Genetic Algorithm run with the crossover rate of 0.2 and the mutation rate of 0.1. The best solution has a 21.03% reduction in terms of total cost compared to the initial feasible solution. The case studies result showed the most significant change is made in case III (10% over-capacity production cost reduction), which reduces the total cost by 6.90% while in other cases have very small significant changes.