Benefit Maximization or the-Quality-First? An E-CARGO Perspective on the Logistics Chain

Abstract

In the logistics chain, through collaboration, multiple supplier enterprises are able to achieve resource sharing, thereby offering a broader and more stable service scope while enhancing risk resilience. However, despite these benefits, differences in the distribution capabilities of various supplier enterprises can lead to inconsistencies in the overall service quality of distribution tasks. Therefore, in collaborative distribution, it is crucial to ensure the overall service quality while maximizing benefit. To address this challenge, this article formalizes the collaborative distribution problem (CDP) in the logistics chain via the environments — classes, agents, roles, groups, objects (E-CARGO) model. A novel solution is designed for CDP by extending the group multirole assignment (GMRA) model. This solution incorporates the qualification matrix adjustments (QMA) algorithm to systematically prioritize suppliers based on their qualifications, thereby maximizing benefit while ensuring the overall service quality of collaborative distribution tasks. Large-scale random experiments show that the proposed method effectively balances the tradeoff between the benefit and overall service quality in the CDP under various data distributions. Moreover, decision-makers can obtain an optimal assignment solution through the Pareto front.