Xi Guo

Dynamic yard crane dispatching in container terminals with predicted vehicle arrival information

The performance of a container terminal depends on many aspects of operations. This paper focuses on the optimal sequencing of a yard crane (or YC for short) for serving a fleet of vehicles for delivery and pickup jobs. The objective is to minimize the average vehicle waiting time. While heuristic algorithms could not guarantee an optimal solution, a conventional mathematical formulation such as mixed integer program would require too much computing time. We present two new algorithms to efficiently compute YC dispatching sequences that are provably optimal within the planning window. The first algorithm is based on the well-known A^* search along with an admissible heuristics. We also incorporate this heuristics into a second backtracking algorithm which uses a prioritized search order to accelerate the computation. Experimental results show that both new algorithms perform very well for realistic YC jobs. Specifically, both are able to find within seconds optimal solutions for heavy workload scenarios with over 2.4x10^1^8 possible dispatching sequences. Moreover, even when the vehicle arrival times are not accurately forecasted, the new algorithms are still robust enough to produce optimal or near-optimal sequences, and they consistently outperform all the other algorithms evaluated.

Dynamic Space and Time Partitioning for Yard Crane Workload Management in Container Terminals

We propose a new hierarchical scheme for yard crane (YC) workload management in container terminals. We also propose a time partitioning algorithm and a space partitioning algorithm for deploying YCs to handle changing job arrival patterns in a row of yard blocks. The main differences between our approach and most of the methods in literature are (1) the average vehicle job waiting time instead of the number of jobs is used to balance YC workload and to evaluate the quality of a partition, (2) the YC working zone assignment is not in units of yard blocks and our space partitioning algorithm generates more flexible divisions of the workload from all blocks, and (3) the YC deployment frequency is not fixed but is decided by our time partitioning algorithm with the objective of minimizing average vehicle waiting times. The scheme combines simulation and optimization to achieve our objective for a row of yard blocks. Experimental results show that the proposed binary partitioning algorithm TP2 makes substantial improvements in job waiting times over the basic partitioning scheme and another existing algorithm (Ng, W. C. 2005. Crane scheduling in container yards with intercrane interference. Eur. J. Oper. Res.164(1) 64--78) in all tested job arrival scenarios.

Yard crane dispatching based on real time data driven simulation for container terminals

This paper studies the problem of real time yard crane dispatching in container terminals. Many technologies, including transponders, RFID and GPS have been used in the container terminal setting for real-time tracking of terminal equipment. A judicious integration of real-time data into the yard crane management system will allow better utilization of terminal resources to improve overall terminal productivity. We propose a yard crane dispatching algorithm based on real time data driven simulation to solve the problem of yard crane job sequencing to minimize average vehicle waiting time. The algorithm will produce optimal operation sequence for each planning window. Several policies to select jobs to form the planning window are also proposed. Our simulation results show that dispatching yard crane based on real time data driven simulation is of great value in improving yard crane performance in 3 scenarios with different vehicle arriving patterns and our results are 10% worse off a loosely estimated overall optimal performance result.

Performing A* Search for Yard Crane Dispatching in Container Terminals

We address the problem of dispatching a yard crane in its appointed zone of a container terminal. The objective of yard crane dispatching is to determine a sequence for handling all coming jobs within the zone that minimizes the average vehicle waiting time. We propose 2 modified A* search algorithms with admissible heuristics to provide fast and optimal dispatching solution. Simulation results show that the algorithm using the improved heuristic is able to find the optimal solution over 2.4 times 1018 possible dispatching sequences in about 3 to 4 seconds under heavy work load.

A simulation based hybrid algorithm for yard crane dispatching in container terminals

The problem of yard crane dispatching in container terminals is addressed in this paper. We proposed two new hybrid algorithms which combine the advantages of A⋆ heuristic search and Recursive Backtracking with prioritized search order to accelerate the solution process. The algorithms proposed use real time data-driven simulation to accurately predict the time taken by the yard crane in performing its operations and this helps in getting an optimal dispatching sequence that can be followed by the yard crane. Experiments carried out show that the proposed algorithms consistently perform very well over all tested cases. The best performing algorithm is able to find the optimal solution over 2.4×1018 possible dispatching sequences in about 0.3 to 0.4 seconds under heavy workload. The characteristics of memory-saving and interruptibility enable the algorithm to be easily integrated into a complete yard crane management system in real world applications. In such real time yard crane management system, our proposed algorithms can be used as an effective and efficient tool to support complex and intelligent higher level planning in addition to managing the yard crane operations in its appointed zone.

Reducing simulation costs of embedded simulation in yard crane dispatching in container terminals

Embedding simulation in optimization algorithms will incur computational costs. For NP-hard problems the computational costs of the embedded simulation in the optimization algorithm are likely to be substantial. YC dispatching is NP-hard. So it is very important to be able to minimize simulation costs in YC dispatching algorithms. In the optimization algorithm for yard crane dispatching published, simulation of YC operations of the entire (partial) sequence of YC jobs are carried out each time the tardiness of a (partial) sequence needs to be evaluated. In this paper we study two approaches to reduce simulation costs in these embedded simulations in the optimization algorithm. Experimental results show that one approach significantly reduces the computational time of the optimization algorithm. We also analyze the reasons for the other approach which fails to reduce the computational time.

Embedding simulation in yard crane dispatching to minimize job tardiness in container terminals

Two optimal algorithms, MTA* and MT-RBA*, are presented to find the optimal yard crane (YC) job sequence for serving a fleet of vehicles for delivery and pickup jobs with scheduled deadlines and predicted vehicle arrival times. The objective is to minimize the total tardiness of incoming vehicle jobs. This is important for minimizing vessel turnaround time. In the search for an optimal job sequence, the evaluation of the total tardiness of (partial) job sequences requires sequence dependent job service times. Simulation is embedded in our optimization algorithms to help provide accurate YC service times. This results in a more accurate evaluation of job tardiness but incurs costs. Experimental results show that this is feasible despite the simulation costs. MTA* and MT-RBA* significantly outperform the Earliest Due Date First and the Smallest Completion time Job First heuristics in minimizing job tardiness. MT-RBA* is computationally more efficient.

A two stage yard crane workload partitioning and job sequencing algorithm for container terminals

We propose a new YC workload partitioning scheme which employs dynamic data driven simulations to conduct what-if experiments in container terminals. Both the optimal partition of the workload in a row of yard blocks and the optimal dispatching sequences for individual YCs are achieved. The practical consideration of the safety constraints is included. A dynamic programming (DP) approach is used to avoid re-computation. An efficient two stage workload partition algorithm (TSWP) is proposed which successfully reduces the number of full what-if simulations while maintaining solution optimality. An effective lower bound (LB) generator with adjustable LB accuracy is designed in supporting the TSWP algorithm. Experimental results show that the TSWP algorithm outperforms the pure DP approach in all tested scenarios and takes less than 1 part per thousand computational time of the DP approach.

Yard crane dispatching to minimize vessel turnaround times in container terminals

Yard crane (YC) dispatching in the operational planning of container terminals usually aims to minimize makespan of YC operations or waiting time of vehicles. We propose that minimizing the maximum tardiness of vehicle jobs at yard blocks will minimize the operational delay of the longest quay crane (QC). This will minimize vessel turnaround time which is one of the most important objectives of container terminals. A provably optimal algorithm, MMT-RBA* to minimize maximum job tardiness, is presented to sequence the YC jobs. Jobs requiring reshuffling of other containers, often ignored in other studies, are handled by embedded simulation in our optimization algorithms. Another provably optimal algorithm, MMS-RBA* to minimize makespan, is also presented. Simulation experiments confirm that MMT-RBA* significantly outperforms the optimal algorithm RBA* to minimize vehicle waiting time from earlier studies and MMS-RBA* to minimize makespan in minimizing vessel turnaround time.

A three-level hierarchical workload management scheme for yard cranes in container terminals

We propose a three-level, hierarchical scheme for yard crane (YC) workload management in container terminals. Level 1 distributes YCs among different rows in the storage yard at suitable times based on predicted future workload. This is done a few times during a shift of 8 hours. Level 2 dispatches YCs to work in various non-overlapping working zones in each row for the time window in between two rounds of YC re-distributions at Level 1. Level 3 determines the serving sequences of vehicle jobs for an YC in a working zone over a period of time (e.g., a sub-planning window). The algorithms for levels 2 and 3 have been published elsewhere. This paper proposes the proportional distribution and the uniform distribution strategies for YC deployment at level 1. We compare the performance of the three-level hierarchical scheme in terms of average job waiting times and the average number of overflow jobs at the end of each planning window under the two distribution strategies.