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The Physical Internet (PI) application in a supply chain is explored by automakers to achieve a digital supply chain to challenge timely delivery while maintaining high customised production at the lowest operating cost.

A bi-objective mixed integer model is formulated, where production is performed in multistage manufacturing systems (MMS) and then delivered in a two-level distribution system. Next, a hybrid iterative method algorithm is developed to solve the practical-scale problem within an admissible time. Finally, PI's benefits on production and supply chain operation are discussed through extensive computational experiments in different supply chain configurations.

Three significant findings are obtained. First, PI can achieve a comparable or better service level, while the cost is always lower. Second, PI can improve the utilisation of production and transportation resources. Third, with a more complex supply chain and a higher production cost or truck fixed cost, PI's advantages over traditional supply chain become more vigorous, but the increase in orders will weaken it.

The auto enterprise should adopt a PI-enabled supply chain (PI-SC), especially with the increase of network complexity and specific cost factors.

Importance should be attached to the PI-SC to make customers better involved in the supply chain.

First, the application of PI in the existing plant is described. Second, MMS production with multi-mode transportation is jointly scheduled. Third, the decision support of the PI-SC is provided for auto enterprises.

The purpose of this paper is to propose an innovative integration method based on decision-theoretic rough set and the extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) methods to address the resilient-sustainable supplier selection and order allocation (SS/OA) problem.

Specifically, a two-stage approach is designed in this paper. First, the decision-theoretic rough set is employed to calculate the rough number for coping with the subjective uncertainty of data and assigning the weights for a resilient-sustainable evaluation criterion. On this basis, the supplier resilient-sustainable performance is ranked in combination with the extended VIKOR method. Second, a novel multi-objective optimization model is proposed that applies an improved genetic algorithm to select the resilient-sustainable supplier and allocate the corresponding order quantity under a multi-tier supplier network.

The results reveal that joint consideration of resilience and sustainability is essential in the SS/OA process. The method proposed in this study based on decision-theoretic rough sets and the extended VIKOR method can handle imprecise information flexibly, reduce information loss and obtain acceptable solutions for decision-makers. Numerical cases validate that this integrated approach can combine resilience and sustainability for effective and efficient SS/OA.

This paper provides industry managers with a new perspective on SS/OA from a resilience and sustainability perspective as a basis for best practices for industry resilience and sustainability. The proposed method helps to evaluate the resilient-sustainable performance of potential suppliers, which is applicable to solving real-world SS/OA problems and has important practical implications for the resilient-sustainable development of supply chains.

The two interrelated priorities of resilience and sustainability have emerged as key strategic challenges in SS/OA issues. This paper is the first study of this issue that uses the proposed integrated approach.