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Journal of Combinatorial Optimization

Erschienen in:

01.07.2023

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

verfasst von: Mohammad Amin Amani, Mohammad Mahdi Nasiri

Erschienen in: Journal of Combinatorial Optimization | Ausgabe 5/2023

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Abstract

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%).

Vorheriger Artikel A green routing-location problem in a cold chain logistics network design within the Balanced Score Card pillars in fuzzy environment

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Titel: A novel cross docking system for distributing the perishable products considering preemption: a machine learning approach
verfasst von: Mohammad Amin Amani
Mohammad Mahdi Nasiri
Publikationsdatum: 01.07.2023
Verlag: Springer US
Erschienen in: Journal of Combinatorial Optimization / Ausgabe 5/2023
Print ISSN: 1382-6905
Elektronische ISSN: 1573-2886
DOI: https://doi.org/10.1007/s10878-023-01057-y

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