Modelagem de um Problema de Dimensionamento de Lotes com Demanda Variavel e Deterministica e Efeitos de Learning e Forgetting

TL;DR

This paper investigates how learning and forgetting effects influence lot-sizing problems with variable demand, demonstrating their significant impact and highlighting the need for improved algorithms for quadratic problem formulations.

Contribution

It quantifies the importance of learning and forgetting effects in lot-sizing, showing their substantial impact and identifying the need for algorithmic improvements.

Findings

Learning curve effects can significantly reduce costs.

Current algorithms may not solve quadratic problems optimally.

Learning effects contribute notably even with minimal discounts.

Abstract

The main goal of this paper was to analyze the importance that the effects of learning and forgetting might have in a lot-sizing problem. It assumes that the learning curve and the economies of scale are present in several industries yet are, in most cases, not considered when dealing with a lot-sizing problem. The importance of the effects was demonstrated and quantified, showing that there is still space for developments in this field. However, as the problem becomes quadratic, there is a possibility that the current algorithms are not able to solve the problem to optimality. Thus, future improvements in the algorithms may further improve the results. However, the overall results found with current algorithms show that the contribution of a discount from a learning curve can be very considerable, even if it is a minimal amount.