Integrated Berth Allocation and Quay Crane Assignment Problem: Set partitioning models and computational results

Most of the operational problems in container terminals are strongly interconnected. In this paper, we study the integrated Berth Allocation and Quay Crane Assignment Problem in seaport container terminals. We will extend the current state-of-the-art by proposing novel set partitioning models. To improve the performance of the set partitioning formulations, a number of variable reduction techniques are proposed. Furthermore, we analyze the effects of different discretization schemes and the impact of using a time-variant/invariant quay crane allocation policy. Computational experiments show that the proposed models significantly improve the benchmark solutions of the current state-of-art optimal approaches

Container Terminal Berth-Quay Crane Capacity Planning Based on Markov Chain

This paper constructs a berth-quay crane capacity planning model with the lowest average daily cost in the container terminal, and analyzes the influence of the number of berths and quay cranes on the terminal operation. The object of berth-quay crane capacity planning is to optimize the number of berths and quay cranes to maximize the benefits of the container terminal. A steady state probability transfer model based on Markov chain for container terminal is constructed by the historical time series of the queuing process. The current minimum time operation principle (MTOP) strategy is proposed to correct the state transition probability of the Markov chain due to the characteristics of the quay crane movement to change the service capacity of a single berth. The solution error is reduced from 7.03% to 0.65% compared to the queuing theory without considering the quay crane movement, which provides a basis for the accurate solution of the berth-quay crane capacity planning model. The proposed berth-quay crane capacity planning model is validated by two container terminal examples, and the results show that the model can greatly guide the container terminal berth-quay crane planning

Models and algorithms for berth allocation problems in port terminals

Seaports play a key role in maritime commerce and the global market economy. Goods of different kinds are carried in specialized vessels whose handling requires ad hoc port facilities. Port terminals comprise the quays, infrastructures, and services dedicated to handling the inbound and outbound cargo carried on vessels. Increasing seaborne trade and ever-greater competition between port terminals to attract more traffic have prompted new studies aimed at improving their quality of service while reducing costs. Most terminals implement operational planning to achieve more efficient usage of resources, and this poses new combinatorial optimization problems which have attracted increasing attention from the Operations Research community. One of the most important problems confronted at the quayside is the efficient allocation of quay space to the vessels calling at the terminal over time, also known as the Berth Allocation Problem. A closely related problem arising in terminals that specialize in container handling concerns the efficient assignment of quay cranes to vessels, which, together with quay space planning, leads to the Berth Allocation and Quay Crane Assignment Problem. These problems are known to be especially hard to solve, and therefore require designing methods capable of attaining good solutions in reasonable computation times. This thesis studies different variants of these problems considering well-known and new real-world aspects, such as terminals with multiple quays or irregular layouts. Mathematical programming and metaheuristics techniques are extensively used to devise tailored solution methods. In particular, new integer linear models and heuristic algorithms are developed to deal with problem instances of a broad range of sizes representing real situations. These methods are evaluated and compared with other state-of-the-art proposals through various computational experiments on different benchmark sets of instances. The results obtained show that the integer models proposed lead to optimal solutions on small instances in short computation times, while the heuristic algorithms obtain good solutions to both small and large instances. Therefore, this study proves to be an effective contribution to the efforts aimed at improving port efficiency and provides useful insights to better tackle similar combinatorial optimization problems

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

Bi-objective Recoverable Berth Allocation and Quay Crane Assignment Planning under Environmental Uncertainty

This study discusses the development of tactical-level integrated planning at seaport container terminals in an uncertain environment. The suggested approach seeks to strike a balance between the cost-effectiveness of a robust baseline schedule and recovery plan and the required quality of customer service in order to enhance the competitive edge of container ports. Integrated planning for a tactical level at the container terminal synchronizes the decisions of berth allocation and quay crane assignment planning by taking into account the unpredictability of the vessel's arrival time and handling time caused by a variety of unforeseen factors such as unfavorable weather conditions, instability in the productivity rate of the quay cranes, the uncertainty of the quantity of loading and discharging containers, and other unpredictable events. The proposed optimization model produces a robust and proactive baseline schedule with a recoverable reactive plan for each scenario that occurs by utilizing buffer times and quay cranes that anticipate fluctuations in uncertain parameters. The proposed bi-objective recoverable robustness optimization model is solved by applying a hybrid method, namely the Rolling Horizon-based Optimization Algorithm (RHOA) and the Preemptive Goal Programming approach, using Gurobi-Python Optimization. The proposed bi-objective recoverable robust optimization model demonstrates superior solution quality in terms of service level and total costs, as well as a more efficient computational time when compared to an optimization model that minimizes total costs for tactical level planning decisions in seaside container terminals

Genetic algorithm for integrated model of berth allocation problem and quay crane scheduling with noncrossing safety and distance constraint

Berth Allocation and Quay Crane Scheduling are the most important part of container terminal operations since berth and quay cranes are an interface of ocean-side and landside in any port container terminal operation. Their operations significantly influence the efficiency of port container terminals and need to be solved simultaneously. Based on the situation, this study focuses on an integrated model of Continuous Berth Allocation Problem and Quay Crane Scheduling Problem. A comprehensive analysis of safety distance for vessel and non-crossing constraint for quay crane is provided. There are two integrated model involved. For the first integrated model, non-crossing constraints are added wherein quay cranes cannot cross over each other since they are on the same track. The second integrated model is focused on the safety distance between vessels while berthing at the terminal and at the same time, quay crane remains not to cross each other. These two constraints were selected to ensure a realistic model based on the real situation at the port. The objective of this model is to minimise the processing time of vessels. A vessel's processing time is measured between arrival and departure including the waiting time to be berthed and servicing time. A new algorithm is developed to obtain the good solution. Genetic Algorithm is chosen as a method based on flexibility and can apply to any problems. There are three layers of algorithm that provide a wider search to the solution space for vessel list, berth list, and hold list developed in this study. The new Genetic Algorithm produced a better solution than the previous research, where the objective function decreases 5 to 12 percent. Numerical experiments were conducted and the results show that both integrated models are able to minimize the processing time of vessels and can solve problem quickly even involving a large number of vessels. Studies have found that the safety distance set as 5 percent of vessel length gives the best solution. By adding safety distance to the integrated model with non-crossing constraint, the result indicates no improvement in the model objective function due to increasing distance between vessels. The objective function increases in the range of 0.4 to 8.6 percent. However, the safety distance constraint is important for safety and realistic model based on the port’s real situation