A biased‐randomized matheuristic for designing physical internet supply chain networks with multiple paths

Abstract

Within the current interconnected and volatile global economy, supply chain network design has become a strategic priority for organizations aiming to enhance resilience, efficiency, and sustainability. The physical internet (PI) paradigm offers a transformative approach to logistics by promoting hyperconnectivity, interoperability, and dynamic reconfiguration. However, designing PI‐based supply chain networks, especially those involving multipath routing and decentralized decision‐making, poses significant computational challenges that traditional optimization methods have not yet addressed. This paper introduces a series of biased‐randomized matheuristic algorithms tailored to the design of PI supply chain networks. Two problem variants are considered: a basic model and a multipath model incorporating network connectivity constraints. Both are formulated as mixed‐integer programming models, which become computationally inefficient for large‐scale instances. Hence, scalable matheuristics are also proposed, based on greedy and biased‐randomized strategies, and on an extended Dijkstra's algorithm in the case of the multipath version. Computational experiments show that these methods have strong potential for real‐world logistics applications, offering robust and time‐efficient tools for complex supply chain network design.