An optimization model and customized solution approaches for in-plant logistic problem within the context of lean management

Employing effectiveness, responsiveness, and lean metrics, this study focuses on the impact of product picking/stacking, trailer allocation, and lot splitting implementation strategies on the in-plant logistic problem. While in-plant logistic problems have attracted attention in recent years, the addressed problem involving decisions on production, transportation, and inventory management has received relatively little attention in the literature. Furthermore, to the best of our knowledge, this problem has not yet been explored with operational-level strategies in the context of lean management principles. To fill this gap, we develop a novel generic optimization model with the aim of minimizing overall costs by integrating decisions related to production, transportation, and inventory. Given the NP-hard nature of this problem, we propose customized solution approaches regarding the implemented strategies for handling large-sized problems. To analyze the impact of controlled factors, a Design of Experiment (DoE) framework is established based on a real case study from the wood-based panel industry. On top of that, several metrics, such as WIP levels, utilization rates, and lead time, are considered to provide a comprehensive analysis of the scenarios. The computational results affirm that employing the balanced storage rule for the product picking/stacking strategy, along with the equal sublot division methodology, significantly reduces the overall cost. Additionally, the findings demonstrate that the designed algorithm, namely GA-BSMATE, exhibits robustness to address diverse situations, particularly when the minimum arrival time rule is implemented as a trailer allocation strategy.