The effective production scheduling of dry bulk ports is a challenging task that demands meticulous planning, task allocation based on customer requirements, as well as strategic route and timing scheduling. Dry bulk ports dedicated to handling commodities like coal and iron ore frequently engage in blending operations as a strategic imperative to gain market competitiveness. The process of blending coal and ore entails the timely arrival of the requisite raw materials at predetermined locations. Simultaneously, it necessitates the coordination of the sequencing of goods entering and departing the port to align with the operational demands associated with material stockpiles. This paper describes and analyzes an operational scheduling problem encountered by one of the largest coal blending sea ports in China. Specifically, a rich constraint programming model is presented to define operation sequences integrating daily inbound and outbound services provided by the port, minimizing the overall operation time. In order to enhance the practicality of the method, a CP-based adaptive simulated annealing local search algorithm has been designed and developed for the optimization problem. The empirical validation of the proposed method is conducted using both real production data and generated experimental data adhering to specific rules. The results conclusively demonstrate the efficacy and feasibility of the proposed method. This also substantiates its practicality and effectiveness in real-world applications, facilitating efficient production and energy-saving operations for the coal port.

This study investigates a coordinated inbound and outbound operation scheduling problem for Huanghua Port, one of the largest coal ports in China. The annual throughput of Huanghua port keeps around 200 million tonnes for the last three years, which accounts for 7% of the overall coal consumption in China. As a leading coal terminal in automation, Huanghua port implemented an inbound-to-outbound unmanned operation system in 2020. Intelligent logistics management is the next target to achieve a system-wide operation efficiency. This could be a challenging task due to several safety issues. Firstly, two stockpiles sometimes must be reclaimed simultaneously to accomplish a blending process when loading onto vessels. This may increase their collision risk if the two reclaimers are on the same track. Secondly, operations on one stockpile consist of stacking operations while coals are dumped onto the stockpile, and reclaiming operations while coals are reclaimed from the stockpile. Therefore scheduling of these operations must be aligned such that coals on stockpiles do not exceeds their safety inventories. Moreover, ship loading operations are delicate due to the stability and strength requirements of vessels. In this case, reclaiming and ship loading of the cargos of one vessel must follow a pre-defined sequence. These issues make the operation planning and scheduling of the port logistics system very complicated, and most of these tasks are stilled handled by humans. However, even the most experienced planners can make only short-term local decisions. Once the problem sizes are getting larger, operation conflicts may occur which leads to a system interruption. In this study, the scheduling problem is modeled and solved with constraint programming, a fundamental technique in artificial intelligence [1]. Specifically, the afore mentioned safety issues are captured and addressed with some global constraints provided by IBM ILOG CP solver [2]. Based on a comprehensive experimental analysis, parameters of solving engines are tuned for a better solution. Computational results verified the correctness and effectiveness of this approach. Real world data shows that the presented method is able to effectively improve the operation efficiency and service level of the coal port.

Driven by the increasing reliance on maritime transportation, achieving energy efficiency and reducing emissions in automated container terminals (ACTs) has become an urgent priority. The complex structure of a low-carbon terminal, along with the interdependent relationships between ACT container operation system and energy system, presents significant challenges to container terminal management. This issue has not yet been discussed in the existing research literature. To this end, this paper introduces a co-optimization problem of the operation, logistics, and energy (CPOL&E) based on the coupling mechanism between multiple subsystems in the ACTs. The CPOL&E considers energy recovery, where the cranes equipped with super-capacitors can recover energy during container unloading and release the stored energy during container lifting to effectively reduce the total energy consumption. A co-scheduling model is developed to simultaneously minimize the makespan and the terminal energy cost. To solve this multi-objective model, we design a multi-subpopulation differential co-evolution algorithm (MDCEA). In this algorithm, an initialization method based on heuristic rules and population entropy is introduced. The MDCEA employs a differential multi-subpopulation co-evolution strategy combined with an adaptive local search approach to enhance population performance. Through comparative experiments with three representative multi-objective algorithms, the results indicate that the MDCEA is capable of obtaining high-quality solutions and demonstrates competitive performance in solving the CPOL&E under the tested instances. Simulation results of the co-optimization mode demonstrate that the scheduling of terminal equipment significantly affects the temporal distribution of its energy demand, which in turn influences the output of the energy system.