With the electrification of port equipment, container terminals have become electricity-intensive consumers. The time-of-use (TOU) pricing policy has prompted container terminals to reoptimize their operations planning to decrease electricity costs. However, it is an essential challenge to optimally plan highly correlated operations of a container terminal in response to TOU tariffs. Traditional operations planning of a container terminal focuses on improving operational efficiency under flat electricity tariffs. The obtained solution under flat electricity tariffs may be far from optimal when the TOU pricing policy is involved. To this end, this paper addresses an integrated operations planning problem from container terminals under TOU tariffs. In particular, several vital resources, including quay cranes, yard cranes, and berths, are jointly scheduled to handle tasks from vessels and external trucks. The objective is to minimize the total costs. We formulate the integrated optimization problem as a mixed-integer linear programming (MILP) model. We then propose a logic-based Benders decomposition (LBBD) algorithm for the problem. The proposed LBBD method uses valid inequalities to speed up the solution procedure. To address practical-sized instances, we design a tailored genetic algorithm (GA) with several acceleration techniques. We demonstrate the performance of the LBBD method and tailored GA through numerical experiments. Results indicate that the proposed integrated approach yields a better solution than its decentralized counterpart, demonstrating that the total operational cost can be significantly reduced by applying the developed model. We also discuss managerial implications drawn from our results for the integrated operations planning under TOU tariffs, which help port operators make critical decisions.

随着港口设备电气化，集装箱码头成为用电大户。分时使用 (TOU) 定价政策促使集装箱码头重新优化其运营计划以降低电力成本。然而，优化集装箱码头高度相关的运营以应对 TOU 关税是一项重要挑战。集装箱码头的传统运营规划侧重于在统一电价下提高运营效率。当涉及 TOU 定价政策时，在统一电价下获得的解决方案可能远非最优。为此，本文解决了 TOU 关税下集装箱码头的综合运营规划问题。特别是，一些重要的资源，包括码头起重机、堆场起重机和泊位，联合安排处理来自船只和外部卡车的任务。目标是最小化总成本。我们将综合优化问题制定为混合整数线性规划 (MILP) 模型。然后，我们针对该问题提出了一种基于逻辑的 Benders 分解 (LBBD) 算法。所提出的 LBBD 方法使用有效的不等式来加速求解过程。为了解决实际大小的实例，我们设计了一种具有多种加速技术的定制遗传算法 (GA)。我们通过数值实验展示了 LBBD 方法和定制 GA 的性能。结果表明，所提出的集成方法比分散的方法产生了更好的解决方案，表明通过应用开发的模型可以显着降低总运营成本。