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

Sea Container Terminals

Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

Simulation model to determine ratios between quay, yard and intra-terminal transfer equipment in an integrated container handling system

This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottle-necks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions

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

Developing New Methods for Efficient Container Stacking Operations

Containerized transportation has become an essential part of the intermodal freight transport. Millions of containers pass through container terminals on an annual basis. Handling a large number of containers arriving and leaving terminals by different modalities including the new mega-size ships significantly affects the performance of terminals. Container terminal operators are always looking for new technologies and smart solutions to maintain efficiency. They need to know how different operations at the terminal interact and affect the performance of the terminal as a whole. Among all operations, the stacking area is of special importance since almost every container must be stacked in this area for a period of time. If the stacking operations of the terminal are not well managed, then the response time of the terminal significantly increases and consequently the performance decreases. In this dissertation, we propose, develop, and test optimization methods to support the decisions of container terminal operators in the stacking area. First, we study how to sequence storage and retrieval containers to be carried out by a single or two automated stacking cranes in a block of containers. The objective is to minimize the makespan of the cranes. Finally, we study how to minimize the expected number of reshuffles when incoming containers have to be stacked in a block of containers. A reshuffle is the removal of a container stacked on top of a desired container. Reshuffling containers is one of the daily operations at a container terminal which is time consuming and increases a ship's berthing time

Space-sharing Strategies for Storage Yard Management in a Transshipment Hub Port

Ph.DDOCTOR OF PHILOSOPH

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