Green fourth-party logistics network design under carbon cap-and-trade policy

As carbon emissions become a significant issue worldwide, sustainability has emerged as a driving force in fourth-party logistics (4PL) network design. To ensure service quality, transportation time cannot be ignored. Considering the carbon cap-and-trade policy, a novel mixed-integer non-linear programming model is proposed to design a green 4PL network under the service time constraint. An equivalent reformulation is proposed to obtain the optimal solution for small-scale problems. For larger-scale problems, the Q-learning based dynamic memetic particle swarm optimisation algorithm is proposed to adaptively select suitable parameters and local search strategies for each individual. Numerical experimental results demonstrate the effectiveness and efficiency of proposed algorithm. Furthermore, the influence of 4PL, credit price, carbon cap, service time, and different carbon policies on the network are investigated. Compared with traditional third-party logistics, 4PL has more advantages in terms of cost, carbon emissions, and customer service. The credit price has a greater influence on cost and carbon emissions compared to the cap. Emissions sensitivity shifts from the purchased credit price under lower caps to the sell price under higher caps. The influence of the cap on emissions is driven by the prices of purchased and sold credits. The appropriate maximum allowable service time is crucial for cost and carbon emissions. Overall, the carbon cap-and-trade policy is beneficial to company economics and sustainable development.