The resilience and sustainability of supply chains are facing new challenges due to the increasing complexity of supply chains. Compared to traditional supply chain networks, the Physical Internet (PI) has the potential to address sustainable development challenges and build resilient supply chain networks by providing interconnected and open logistics services. However, the interplay between resilience and sustainability has yet to be thoroughly explored in the PI-enabled supply chain literature. This paper aims to investigate the relationship between resilience and sustainability in PI-enabled supply chains. An innovative hybrid approach that combines the resilient-sustainable performance scoring method with mixed-possibilistic programming is proposed to trade-off and analyze the relationship between the two. Applying the concepts of resilience and sustainability to PI-enabled supply chains involves optimizing costs, sustainable performance, and resilience levels simultaneously. This enables coping with unforeseen disruptions from suppliers, plants, and PI hubs. The results of computational experiments have demonstrated that the PI-enabled supply chain exhibits stronger sustainable and resilience performance in dealing with random disruptions compared to traditional supply chains. The fruitful research yielded important management insights and practical implications, thereby contributing to the reinforcement of the literature on PI.

The sustainable combined location-inventory-routing problem (CLIRP) based on demand forecasting is studied in this paper. Based on the construction of a multi-stage demand forecasting model, five parts of total logistics costs: the costs of trunk transportation and regional transportation, the fixed costs of distribution center construction, the inventory holding costs, shortage costs, and salvage, are comprehensively considered. The existing CLIRP model does not consider the environmental influence. Thus, a sustainable CLIRP model considering carbon emission is established with minimum logistics costs and emission as the objective function. A heuristic algorithm gives the initial solution, and then a hybrid heuristic algorithm combining the tabu search algorithm with the simulated annealing algorithm is proposed to find the global near-optimal solution. Finally, a numerical example of a garment chain enterprise is given to illustrate the solving process of the model. The results show that using the proposed algorithm determines the optimal locations of RDCs, and the transportation routes with each region are obtained with the minimum total logistics costs and carbon emission. The model realizes the combination of location, inventory, and routing problems of the large garment enterprises and finally realizes the goal of optimizing the sustainable logistics distribution network of the garment industry, which verifies the effectiveness of the model. Moreover, a comparison is made to show the efficiency of the proposed algorithm; the results show that the proposed algorithm in this paper optimizes the route and selections of RDCs.