As an integrator and coordinator of supply chain resources, the fourth-party logistics (4PL) provider can effectively reduce supply chain costs through resource integration. From the perspective of a 4PL as a supply chain integrator, this paper focuses on the challenges of multi-period procurement, warehousing, multimodal transportation, and the selection of third-party logistics (3PL) service providers. A joint optimization model is constructed with the objective of minimizing total operational costs. To enhance the quality and efficiency of the solution, a quantum micro-evolution algorithm that combines a graph sample and aggregate algorithm and a $K $-shortest path algorithm is designed. Numerical experiments are conducted on both small-scale and large-scale networks based on the Chicago-regional dataset. The results indicate that, in the tested instances, the proposed model can reduce total operational costs by up to 11.8% and 15% compared to a joint optimization strategy without warehousing and a non-joint optimization strategy, respectively. In the large-scale network, a comparison with the micro-evolution algorithm, quantum genetic algorithm, genetic algorithm, and particle swarm optimization demonstrates the superiority of the proposed algorithm in terms of both solution quality and efficiency. Finally, by analysing the changes in operational costs under scenarios of parameter fluctuations, the model's capability to handle multi-period variations is validated, achieving a maximum cost reduction of 18.07%.