Collaborative Scheduling Optimization of Container Port Berths and Cranes under Low-Carbon Environment

Abstract

Motivated by the need for a green and low-carbon economy, we explore the co-scheduling optimization of berths and cranes. Our aim is to balance the carbon tax and operating costs of ports under uncertain conditions, proposing an innovative nonlinear mixed-integer programming formulation. To address this optimization challenge, we have developed an enhanced version of the adaptive spiral flying dung beetle algorithm (ASFDBO). In order to evaluate the performance of the ASFDBO algorithm, we performed a benchmark function test and a convergence analysis with other recognized metaheuristics. In addition, we verified the practical applicability of the ASFDBO algorithm in different test scenarios. Through numerical experiments, we analyze the feasibility and effectiveness of the algorithm’s scheduling solutions and improvement strategies. Results indicate that our collaborative scheduling optimization, which considers both carbon and production costs, achieves feasible solutions and reduces carbon expenses. Finally, we investigate the impact of different carbon tax rates on the joint scheduling optimization of berths and quay cranes, and the results show that a reasonable carbon tax policy can effectively reduce the carbon emissions of ports.

Variable Neighborhood Search for Multi-Port Berth Allocation with Vessel Speed Optimization

“…Constraint (13) is the planning time limit, and constraint (14) guarantees that the vessel sails at a certain speed between any two visiting ports. Constraints (15) to (18) set the range of the decision variables.…”

“…In reality, the oil price is time varying, so it is extremely important to optimize the vessel speed reasonably according to different oil prices. Specifically, when the Constraints (3), ( 7)~( 12), ( 14)~ (18). Based on models M1 and M2, the VNS is called for 20 times to solve, and the comparison results between their best solutions and M0 are recorded in Table 7, including average vessel speed, total fuel consumption, and latest vessel completion time.…”

“…s.t. Constraints (3), ( 7)~ (18). Then, the planning period restriction (constraint ( 13)) is further relaxed on the basis of M1, and the model M2 is obtained as follows:…”

“…Considering environmental sustainability, some scholars have begun to pay attention to emissions during vessel and port operations. Among them, some studies introduce the goal of minimizing carbon emissions in port resource scheduling problems [16][17][18], and some scholars study the promising emissions reduction technology of on-shore power supply [19][20][21][22][23]. In addition, some studies have introduced the VSO into BAP-related problems to reduce emissions during vessel sailing by optimizing vessels' speeds.…”

Energy–Logistics Cooperative Optimization for a Port-Integrated Energy System

“…Shi et al [29] use a mixed-integer linear programming model; numerical simulations were carried out verify that the proposed strategy can minimize the total cost of the integrated energy-logistics system and increase the utilization rate of renewable energy systems for green ports. Jiang et al [36] innovatively propose a nonlinear mixed-integer programming formula to obtain the optimal mode of berth and quay bridge collaborative optimization in ports under uncertain conditions. Yu et al [37] aim to minimize cranes' carbon emissions and operating costs.…”