Scheduling of Container Handling Equipment in Marine Container Terminals

To improve the competitiveness of marine container terminals, it is critical to minimize the makespan of a container vessel. The makespan is defined as the latest completion time among all handling tasks of the container vessel. Lower makespan (i.e. lower vessel turn time) can be achieved through better scheduling of the container handling equipment during vessel operations. The scheduling of terminal equipment is an operational problem, and a detailed schedule for each type of equipment operating in the terminal is necessary. Several studies have applied operations research techniques to optimize the processes of equipment in a terminal. This dissertation investigates three main operations in a marine container terminal, namely: quay crane scheduling, yard truck scheduling and yard crane scheduling. The first study in this dissertation addresses the quay crane scheduling problem (QCSP), which is known to be NP-hard. A genetic algorithm (GA) was developed and tested on several benchmark instances. An initial solution based on the S-LOAD rule, a new approach for defining the chromosomes, and new procedures for calculating tighter lower and upper bounds for the decision variables were used to improve the efficiency of the GA search. In comparison with best available solutions, our method was able to find optimal or near-optimal solution in significantly shorter time for larger problems. The second study of this dissertation addresses the quay crane scheduling problem with time windows (QCSPTW). A GA was developed to solve the problem. Unlike other works, the proposed solution approach allows quay cranes (QCs) to move in directions independent of one another, and in certain situations, the QCs are allowed to change their directions. Using benchmark instances, it was shown that the developed GA can provide near optimal solutions in a faster time for medium and large-sized instances and provides an improvement in the solution quality for instances with fragmented time windows. The equipment involved in each of three main operations of a container terminal are highly interrelated, and therefore, it is necessary to consider the operations of QCs, yard trucks (YTs), and yard cranes (YCs) in a holistic manner. The third study of this dissertation addresses the scheduling of QCs and YTs jointly. The integrated problem can be seen as an extension of the classical flow shop with parallel machines at each stage, which has been proved to be NP-hard. A mixed integer programming model was developed based on hybrid flow shop scheduling problem with precedence relationship between tasks, QC interference, QC safety margin, and blocking constraints. A GA combined with a greedy algorithm was developed to solve the problem. The GA solutions demonstrated that the developed integrated model is solvable within reasonable time for an operational problem. The fourth study of this dissertation developed a robust optimization model that considers all three equipment jointly. The unique difference between the fourth study and the existing literature is that it accounts for the non-deterministic nature of container processing times by the QCs, YTs, and YCs. To deal with the uncertainty in processing times, a model was formulated based on a recent robust optimization approach (p-robust). The objective function of the proposed model seeks to minimize the nominal scenario makespan, while bounding the makespan of all possible scenarios using the p-robustness constraints. To solve the robust integrated optimization model, a GA was developed. The experimental results demonstrated that the developed robust integrated model is solvable within reasonable time for an operational problem

A reclaimer scheduling problem arising in coal stockyard management

We study a number of variants of an abstract scheduling problem inspired by the scheduling of reclaimers in the stockyard of a coal export terminal. We analyze the complexity of each of the variants, providing complexity proofs for some and polynomial algorithms for others. For one, especially interesting variant, we also develop a constant factor approximation algorithm.Comment: 26 page

Rail Mounted Gantry Crane Scheduling Optimization in Railway Container Terminal Based on Hybrid Handling Mode

Rail mounted gantry crane (RMGC) scheduling is important in reducing makespan of handling operation and improving container handling efficiency. In this paper, we present an RMGC scheduling optimization model, whose objective is to determine an optimization handling sequence in order to minimize RMGC idle load time in handling tasks. An ant colony optimization is proposed to obtain near optimal solutions. Computational experiments on a specific railway container terminal are conducted to illustrate the proposed model and solution algorithm. The results show that the proposed method is effective in reducing the idle load time of RMGC

Mathematical Models of Seaside Operations in Container Ports and their Solution

Operational Research and Optimization are fundamental disciplines which, for decades, provided the real-world with tools for solving practical problems. Many such problems arise in container ports. Container terminals are important assets in modern economies. They constitute an important means of distributing goods made overseas to domestic markets in most countries. They are expensive to build and difficult to operate. We describe here some of the main operations which are faced daily by decision makers at those facilities. Decision makers often use Operational Research and Optimization tools to run these operations effectively. In this thesis, we focus on seaside operations which can be divided into three main problems: 1- the Berth Allocation Problem (BAP), 2- the Quay Crane Assignment Problem (QCAP), 3- the Quay Crane Scheduling Problem (QCSP). Each one of the above is a complex optimization problem in its own right. However, solving them individually without the consideration of the others may lead to overall suboptimal solutions. For this reason we will investigate the pairwise combinations of these problems and their total integration In addition, several important factors that affected on the final solution. The main contributions of this study are modelling and solving of the: 1- Robust berth allocation problem (RBAP): a new efficient mathematical model is formulated and a hybrid algorithm based on Branch-and-Cut and the Genetic Algorithm is used to find optimal or near optimal solutions for large scale instances in reasonable time. 2- Quay crane assignment and quay crane scheduling problem (QCASP): a new mathematical model is built to simultaneously solve QCASP and a heuristic based on the Genetic Algorithm is developed to find solutions to realistic instances in reasonable time. 3- Berth allocation, quay crane assignment and quay crane scheduling problem (BACASP): an aggregate model for all three seaside operations is proposed and to solve realistic instances of the problem, an adapted variant of the Genetic Algorithm is implemented. Keywords: berth allocation; quay crane assignment; quay crane scheduling; terminal operations; genetic algorith

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

Model and heuristic solutions for the multiple double-load crane scheduling problem in slab yards

This article studies a multiple double-load crane scheduling problem in steel slab yards. Consideration of multiple cranes and their double-load capability makes the scheduling problem more complex. This problem has not been studied previously. We first formulate the problem as a mixed-integer linear programming (MILP) model. A two-phase model-based heuristic is then proposed. To solve large problems, a pointer-based discrete differential evolution (PDDE) algorithm was developed with a dynamic programming (DP) algorithm embedded to solve the one-crane subproblem for a fixed sequence of tasks. Instances of real problems are collected from a steel company to test the performance of the solution methods. The experiment results show that the model can solve small problems optimally, and the solution greatly improves the schedule currently used in practice. The two-phase heuristic generates near-optimal solutions, but it can still only solve comparatively modest problems within reasonable (4 h) computational timeframes. The PDDE algorithm can solve large practical problems relatively quickly and provides better results than the two-phase heuristic solution, demonstrating its effectiveness and efficiency and therefore its suitability for practical use

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