“…Ref. [45] offers an exact and computationally fast solution technique to solve the QCSP. The technique combines a partitioning heuristic with a branch and price algorithm.…”
The aim of the quay crane scheduling problem (QCSP) is to identify the best sequence of discharging and loading operations for a set of quay cranes. This problem is solved with a new hybrid estimation of distribution algorithm (EDA). The approach is proposed to tackle the drawbacks of the EDAs, i.e., the lack of diversity of solutions and poor ability of exploitation. The hybridization approach, used in this investigation, uses a distance based ranking model and the moth-flame algorithm. The distance based ranking model is in charge of modelling the solution space distribution, through an exponential function, by measuring the distance between solutions; meanwhile, the heuristic moth-flame determines who would be the offspring, with a spiral function that identifies the new locations for the new solutions. Based on the results, the proposed scheme, called QCEDA, works to enhance the performance of those other EDAs that use complex probability models. The dispersion results of the QCEDA scheme are less than the other algorithms used in the comparison section. This means that the solutions found by the QCEDA are more concentrated around the best value than other algorithms, i.e., the average of the solutions of the QCEDA converges better than other approaches to the best found value. Finally, as a conclusion, the hybrid EDAs have a better performance, or equal in effectiveness, than the so called pure EDAs.
“…Ref. [45] offers an exact and computationally fast solution technique to solve the QCSP. The technique combines a partitioning heuristic with a branch and price algorithm.…”
The aim of the quay crane scheduling problem (QCSP) is to identify the best sequence of discharging and loading operations for a set of quay cranes. This problem is solved with a new hybrid estimation of distribution algorithm (EDA). The approach is proposed to tackle the drawbacks of the EDAs, i.e., the lack of diversity of solutions and poor ability of exploitation. The hybridization approach, used in this investigation, uses a distance based ranking model and the moth-flame algorithm. The distance based ranking model is in charge of modelling the solution space distribution, through an exponential function, by measuring the distance between solutions; meanwhile, the heuristic moth-flame determines who would be the offspring, with a spiral function that identifies the new locations for the new solutions. Based on the results, the proposed scheme, called QCEDA, works to enhance the performance of those other EDAs that use complex probability models. The dispersion results of the QCEDA scheme are less than the other algorithms used in the comparison section. This means that the solutions found by the QCEDA are more concentrated around the best value than other algorithms, i.e., the average of the solutions of the QCEDA converges better than other approaches to the best found value. Finally, as a conclusion, the hybrid EDAs have a better performance, or equal in effectiveness, than the so called pure EDAs.
“…Quay crane scheduling and berth allocation problems are among the common issues typically investigated by researchers with regards to quay crane. [3][4][5][6][7][8][9] According to them, inadequate plannings are often the primary cause of bottlenecks of port operations.…”
Section: Introductionmentioning
confidence: 99%
“…The quay space and quay cranes (QC) are located on seashore, while the storage zones and the gate, which serve as an interchange point between the terminal and the major land transportation routes, are located on the landside of the terminal. Quay crane scheduling and berth allocation problems are among the common issues typically investigated by researchers with regards to quay crane 3–9 . According to them, inadequate plannings are often the primary cause of bottlenecks of port operations.…”
Reliability, availability, and maintainability (RAM) of quay cranes (QCs) are essential for an effective port operation. This study estimates operational RAM of QCs using the Stochastic Petri Net (SPN) modelling. Asset Performance Assessment (APA) was performed to conduct the study by a SPN model. Based on the operation and maintenance data, probability distributions of Mean Time Between Failure (MTBF) and Mean Time to Repair (MTTR) is determined by Goodness of Fit Test of Anderson Darling. Subsequently, these distributions are applied in APA to determine the frequencies of failure/breakdowns, duration of downtimes, availability, and reliability with the assistance of the SPN model and Monte Carlo Simulation. The availability of the analysed QC is 0.97 or 97%. The results of this work are verified by the comparison with historical data. The outcomes of this paper contribute to reliability, availability, maintainability, and risk management of QCs.
“…The literature regarding these processes mainly focuses on marine intermodal terminals. The literature on container terminals in seaports is very wide and concerns processes connected with container ship operations such as: berth allocation and scheduling [9][10][11], quay crane scheduling [12,13], ship stowage planning [14], storage yard processes optimization [15], and yard crane allocation and scheduling [16,17].…”
The paper presents the issue of container handling processes at a railroad intermodal terminal. The main purpose of this paper is the assessment of the handling equipment utilization and the associated energy consumption. The authors analyze how the road vehicle availability at the moment specified in the containers loading schedule influences the total handling equipment operation time as well as the necessary number of handling equipment. It is assumed that vehicles planned for loading of import containers may be late for loading, which causes some interruptions in the loading schedule. Such interruptions are identified with the necessity to handle the next container for which the road vehicle is already waiting, which influences the handling equipment utilization and, finally, energy consumption. The general mathematical model of the problem developed in the FlexSim simulation software was presented. Based on the simulation research, it pointed out that proper road vehicles loading sequencing can significantly reduce handling equipment operation time, and thus energy consumption, costs, and CO 2 emissions. The literature analysis presented in the paper indicates that most of the research in the field of intermodal transport is focused on operations optimization in container ports. There are differences between two types of intermodal terminals in operation procedures and rules. That is why the authors decided to undertake the problem of road vehicle sequencing including their random availability and its influence on handling device operation time, which has not been considered in the literature so far.
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