A novel cross docking system for distributing the perishable products considering preemption: a machine learning approach

In this article, a new cross-docking approach with two characteristics, namely paired-doors and preemption, is developed based on the sustainable development goals (SDG12) paradigm that expresses targets to decrease the food losses throughout the supply chain. For every receiving door, there is a corresponding shipping door in front of it. The packages can only be transferred from a receiving door to its shipping counterpart. This new cross-dock is suitable for distributing the perishable products due to the products' time-sensitive feature. The proposed cross-docking system, which is easier to implement considering the lower automation level required, is compared with the conventional approach to evaluate the new characteristics. Moreover, a genetic algorithm and grey wolf optimizer are proposed to solve the model for the large-sized problem instances. The results show that the proposed cross-docking approach can transfer the products faster than the traditional approach. Also, a predictive model is built by a machine learning algorithm based on the mathematical modeling outputs to predict the makespan by analyzing the factors such as the total product number, number of inbound and outbound vehicles, and the number of paired doors. The results showed that the predictive model could predict the makespan (with an average of 92.8%).

Automated summary

In this article, a new cross-docking approach with paired-doors and preemption is proposed based on the SDG12 paradigm. It is suitable for distributing perishable products due to their time-sensitive nature. The proposed approach is compared with the conventional approach and shown to be faster in transferring products. Additionally, a predictive model is built using a machine learning algorithm to predict the makespan with an average accuracy of 92.8%.