Horizon CDT Research Highlights

Research Highlights

Integrated Solution Approach for Seaside Operations in Container Terminals

  Shuli Liu (2013 cohort)

1. PROJECT OBJECTIVES

Nowadays, container terminals are becoming increasingly automated which requires advanced optimisation technologies to efficiently solve the numerous logistics problems arising. Reflected by recent research in the last 5 years, the integration of seaside operations planning is getting state-of-the-art. Despite a significant increase of research activity, there are still many topics that have to be addressed by further research. The main objectives of my future research are two-fold.

On one hand, designing an innovative mathematical model and an efficient solution algorithm for integrated seaside operations planning that comprises berth allocation problem (BAP), quay crane assignment problem (QCAP) and quay crane scheduling problem (QCSP). In the recent scientific literature on seaside operations within a container terminal, different integration mechanisms are presented for solving two or all three interrelated problems simultaneously. However, there is very little research which provides adequate support to solve the issue via practical considerations. With regard to the solution algorithms, Genetic and Evolutionary Algorithms stand in the dominant position in the literature, while meta-heuristics and especially mathematically driven heuristics and exact methods are clearly underrepresented so far. Consequently, we aim to answer the following research question:

  • How can we design our model and solution algorithm, embed up-to-date technological developments, for the integrated problem to achieve better computational performance, especially for large-scale instances?

On the other hand, to address the challenge of handling enormous amount of uncertain factors associate with port operations. These may arise from unpredictable weather conditions, mechanical problems, inaccurate container information, late arriving vessels and trains etc. Even a small perturbation can render a planned schedule infeasible and result in high losses. The stochastic feature of vessels arrival and handling times adds to the complexity of the integrated problem. In the current literature, very few studies related to seaside operations address this challenge. Aiming to fill this research gap, we plan to answer the question listed below.

  • How to modify our model to be efficient and robust to tackle uncertain factors associated with seaside operations within a container terminal?

With the above framework, we are planning to study the integrated problem considering continuous berth layout and stochastic arrival & handling time of vessels. Hopefully, we aim to implement the designed approach with a self-developed decision support system (DSS).

2. SCOPE AND BACKGROUND OF RESEARCH

2.1 Research background

Maritime transportation is by far the most cost-effective way to transfer general cargo and bulk cargo around the world. According to recent estimates, over 80% of the world trade by volume is carried by sea and handled by ports (UNCTAD, 2012). Over the last decade, the annual average increase of container transportation by volume is 8.2% which is much more dramatic as compared to that of bulk cargo. Although it is generally agreed that future container demand growth will not be as strong as the boom period in the 1990s and 2000s, global container port demand is still forecast to exceed 800 million TEU p.a. by 2017, growing by just over 5%. To put this growth into context, the 186 million TEU that this growth represents is the equivalent of the entire throughput of all Chinese ports in 2012. Due to the dramatic increase in the global container traffic, the efficient management of operations in seaport container terminals has become a crucial issue.

Container terminal is a facility where containers are transhipped between different transport vehicles and it can be divided into five main areas, namely, the berth, quay, transport area, storage yard and terminal gate. The berth and quay areas are considered seaside, while the yard and the terminal gate are consider landside. The transport area is the inter-section of the seaside and landside areas. Container terminal operations planning typically involve six key operations which can be used to evaluate port productivity, that is, stowage planning, berthing activities, crane assignment and scheduling for loading and discharge, quay to storage transfer, yard storage, and intermodal transfer and inland distribution. Each operation is closely interrelated with the other and is a critical factor in improving operational efficiency.

My future research will mainly focus on seaside operations since it has received increasingly attention in the recent OR and transportation literature within the academic community. Seaside operations in container terminals basically comprise the berth allocation problem (BAP), the quay crane assignment problem (QCAP), the quay crane scheduling problem (QCSP). The three sub-problems determine the stay times of container vessels, which reflect the service qualities and competitiveness of a container terminal. While significant research has been devoted to the BAP, QCAP, and QCSP separately, these problems are in fact tightly interconnected. Also, a trend towards a deeply integrated scheme of these operations is observed in the recent literature. Moreover, with the new technological developments in container terminals in recent years, such as the change of fully automated terminals, the arising of indented berths, and the invention of new spreader-technology, these developments inspire us to do some innovative work and fill the research gap by considering the up-to-date developments in real-world.

2.2 Research scope

After reviewing two survey papers (Bierwirth and Meisel 2010, Bierwirth and Meisel 2014), we conclude that the integrating BAP and QCAP is by far the most often treated integration concept in seaside operations planning. Although we have observed 33 approaches published with the past five years for the integrated problem of seaside operations, very few scholars investigate the combination of BAP, QCAP and QCSP up to now. To the best of our knowledge merely five papers, Song et al. (2012), Meisel and Bierwirth (2013), Rodriguez-Molins et al. (2014b), (Türkoğulları, Taşkın et al. 2014), (Ursavas 2014), treated this path so far. Thus, my future research will deal with the integration of these three problems (BAP + QCAP + QCSP) and tackle the uncertain factors encountered in terminal operations. Based on the classification scheme in the research work by Bierwirth and Meisel, our approach will be represented by BAP, QCAP, QCSP. In particular, the BAP is defined by con|stoch|QCAP,QCSP,stoch|∑(wait +hand), Therefore, we will consider the following attributes and performance measures in our model:

  • Spatial attribute: We assume that the berth is a continuous layout (cont), which means vessels can berth at arbitrary positions within the boundaries of the quay.

  • Temporal attribute: We assume stochastic arrival times (stoch), where the arrival times of vessels are stochastic parameters either defined by continuous random distribution or by scenarios with discrete probability of occurrence.

  • Handling time attribute: We assume the handling time depends on the assignment and scheduling of QCs incorporating the stochastic factors during the operations (QCAP, QCSP, stoch).

  • Performance measure: Our objective is to minimize the sum of the waiting time (wait) and handling time (hand) of all the vessels to be served within a pre-set time horizon.

The research will consider dynamic assignment of QCs for modelling QCAP

  • Variable-in-time crane assignment: which means the number of cranes can change dynamically during the service processes. The QCSP is defined by bay|TW,move|cross,safe|max(compl), correspondingly, the following attributes will be consider:
  • Task attribute: We partition a vessel into several bays and each bay is defined as a task comprising all containers to be unloaded and loaded.
  • Crane attribute: We assume the availability of QCs is restricted to given time windows (TW) and the time for moving cranes along the quay is taken into account (move),
  • Interference attribute: To avoid traffic congestions, we will consider non-cross constraint (cross) and safety margins among each cranes during operation (safe).
  • Performance measure: Our objective is to minimize the completion times of tasks.

Based on the preceding discussion, our approach will present a stochastic and continuous berthing model that takes into account the dynamic assignment and scheduling of QCs in order to obtain the handling time.

3. RESEARCH SIGNIFICANCE AND VALUE

We can summarize the value of our research in the following aspects:

From the recent OR literature on container terminal operations, it is agreed that integrated planning of operations can allow port terminals to lower delay costs, reduce congestion and enhance efficiency.

  • Exact algorithms for large-scale integrated problems: through the research, we plan to propose an exact solution algorithm based on hybrid meta-heuristics or mathematically driven exact method to solve the integrated BAP, QCAP and QCSP.

To the best of our knowledge, there are very few scholars have attempted to deal with unexpected events occurring in the terminal with a continuous berth layout due to its complexity.

  • Handling uncertainty: we aim to propose a stochastic programming model to tackle the uncertain factors associate with terminal operations and design an algorithm to solve the model.

References

  1. Bierwirth, C. and F. Meisel (2010). "A survey of berth allocation and quay crane scheduling problems in container terminals." European Journal of Operational Research 202(3): 615-627.
  2. Bierwirth, C. and F. Meisel (2014). "A follow-up survey of berth allocation and quay crane scheduling problems in container terminals." European Journal of Operational Research.
  3. Türkoğulları, Y. B., et al. (2014). "Optimal berth allocation and time-invariant quay crane assignment in container terminals." European Journal of Operational Research 235(1): 88-101.
  4. Ursavas, E. (2014). "A decision support system for quayside operations in a container terminal." Decision Support Systems 59: 312-324.

This work was carried out at the International Doctoral Innovation Centre (IDIC). The authors acknowledge the financial support from Ningbo Education Bureau, Ningbo Science and Technology Bureau, China's MOST, and the University of Nottingham. The work is also partially supported by EPSRC grant no EP/G037574/1.