Maximizing container terminal throughput by internal tractors assignment and scheduling

Abstract

Terminal operations are known to be among the most challenging operational research topics because the field is full of theories with applications to real-life practice. In the last decade, terminal operations research has been carried out on such topics as berth allocation, quay crane scheduling, yard planning, container locating and internal tractors assignment and scheduling (ITAS). The latter is a particularly important topic since it directly affects the overall performance of container terminals, involving terminal productivity, equipment utilization and service rate, as well as associated seaside operations with yard-side operations. It involves the scheduling of internal tractors to transport different loading and unloading containers between vessels at the quayside and the yard side. The idea of ITAS is to improve the internal traffic flow in the container terminal. It mainly deals with the problem of assigning different internal tractors to pick-and-drop different exporting containers from the yard side to the quayside, and importing containers from the quayside to the yard side. However, many container terminal companies are still not able to efficiently and effectively cope with ITAS in the terminal logistic. There are a few challenging problems with respect to planning and managing a container transportation network that trouble terminal operators in terminal industry. In fact, the experience of an operator plays a crucial role in making the decision for exact number of internal tractors and transportation routes in daily operations. This is due to that the number of containers and storage locations are so large that the job of assigning and scheduling internal tractors to maximize terminal efficiency and enhance service level is extremely complicated. Moreover, allocating suitable storage locations for inbound containers requires a jointly consideration of managing internal tractors. It is difficult to decide which storage locations should be reserved for a certain vessel and where an inbound container should be stored, and this situation becomes even more complicated when transhipment is required. Although ITAS has been thoroughly researched, most of the studies lack consideration of practical issues, such as terminals outsourcing a number of internal tractors to perform a set of container delivery requests, and the transportation of transhipment containers. Due to a highly competitive business environment, container terminals are facing a tradeoff between container transportation efficiency and cost. Terminals strive to increase the efficiency of container delivery, which may need good transportation schedules and internal tractor deployment strategies. The scheduling and assignment of internal tractors is critical for maintaining high terminal performance in the overall container terminal system. In this connection, this study considers ITAS integration with the internal tractor deployment strategy and transhipment operations, and new models are developed. The ultimate goal is to maximize the terminal operation efficiency. Heuristic algorithms are proposed to solve these integrated and complicated models. Computational results show that solving the integration problem as a whole can significantly improve the terminal operation efficiency. This research is not only pioneering in the area of container terminal operations, but the findings also provide a vital reference for future research on ITAS. The study has generated the following five deliverables: (i) ITAS is enriched by formulating and modeling all potential available storage locations; (ii) a novel ITAS system integrating container storage allocation and yard truck deployment strategy is identified and modeled; (iii) a two-level heuristic optimization methodology is developed to deal with the novel ITAS system, which is shown to obtain better results compared with other algorithms; (iv) a new ITAS system for transhipment container delivery and transportation in transhipment hubs is detailed demonstrated and modeled; and (v) a decomposition iterative algorithm optimization methodology for the proposed ITAS system in transhipment hubs is proposed.