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Multi-agent Based Modeling of Container Terminal Operations
... Furthermore, simulations of queueing models can be used to illustrate, predict, and evaluate the performance of different policies for managing the flow of ships through a harbour. This can be useful for testing new strategies or for assessing the impact of changes to the harbour's infrastructure or capacity (Cahyono, 2021). concept that can be applied in marine vessel harbour management, and simulations can be used to evaluate the performance of different strategies (Oyatoye et al., 2011). ...
The effective management of marine vessels at ports is crucial for minimizing wait times, reducing congestion, and improving the overall efficiency of the shipping industry. This paper explores the use of queueing theory as a tool for optimizing the operations in harbours. The proposed system assigns priorities to vessels based on their type, size, and urgency, and continuously updates these priorities as vessels wait in the harbour. It leverages on Queueing Theory to model vessel arrivals, service times, and queue lengths to optimize the allocation of resources and minimize waiting times. The diminishing priority scheme ensures that vessels that have been waiting for longer receive a higher priority, thereby reducing overall waiting times and increasing efficiency. The effectiveness of the proposed system is evaluated through simulation and sensitivity analysis, which demonstrate its ability to significantly reduce average waiting times and improve the overall management of harbours. Simulations data are used to prove the performance of the algorithm, and the results are tested against a Gantt chart. It is found the proposed method is well-suited for situations where priorities are the most important factor, but less suited for situations where average waiting time is the most important factor. Thus, this research provides valuable insights and practical solutions to port authorities and harbour operators seeking to improve their operations and advance their service levels.
This paper develops a discrete-event multi-agent simulation model that may support robust and realistic well-to-wheel analysis to assess energy consumption and greenhouse gas emissions of greener technologies applied to container terminal activities. To this end, a discrete-event simulation multi-agent model was specifically developed and validated with real data for the port of Salerno (southern Italy), and a well-to-wheel analysis was carried out by comparing different scenarios with an increasing level of port electrification solutions. A comparison is proposed in terms of consumption and emissions between conventionally fueled and “green” vehicles, including cold ironing technology. The proposed modeling framework makes it possible to simulate highly complex logistics systems in which the environmental and energetic performance of the handling equipment may significantly differ and be significantly affected by human factors and external factors. Our results show that the greener scenario allows higher emissions reduction based on the energy sources used to produce electricity.
In this paper, we consider discrete event systems divided in a main system and a secondary system such that the innerdynamics of each system is ruled by standard synchronizations and the interactions between both systems are expressed bypartial synchronizations (i.e., event e2 can only occur when, not after, event e1 occurs) of events in the secondary system byevents in the main system. The main contribution consists in adapting model predictive control, developed in the literature for(max,+)-linear systems, to the considered class of systems. This problem is solved under the condition that the performance ofthe main system is never degraded to improve the performance of the secondary system. Then, the optimal input is selected torespect the output reference and the remaining degrees of freedom are used to ensure just-in-time behavior. The unconstrainedproblem is solved in linear time with respect to the length of the prediction horizon.
Predictive control is investigated as a paradigm for the allocation of handling resources to transfer containers inside intermodal terminals. The decisions on the allocation of such resources are derived from the minimization of performance cost functions that measure the lay times of carriers over a forward horizon basing on a model of the container flows. Such a model allows one to take advantage of the information available in real time on the arrival or departure of carriers with the corresponding amounts of containers scheduled for loading or unloading. The resulting strategy of resource allocation can be regarded as a feedback control law and is obtained by solving nonlinear programming problems online. Since the computation may be too expensive, a technique based on the idea of approximating offline such a law is proposed. The approximation is performed by using neural networks, which allow one to construct an approximate feedback controller and generate the corresponding online control actions with a negligible computational burden. The effectiveness of the approach is shown via simulations in a case study.
We discuss an old distributed algorithm for reaching consensus that has received a fair amount of recent attention. In this algorithm, a number of agents exchange their values asynchronously and form weighted averages with (possibly outdated) values possessed by their neighbors. We overview existing convergence results, and establish some new ones, for the case of unbounded intercommunication intervals.
A discrete event system (DES) is a dynamic system that evolves in
accordance with the abrupt occurrence, at possibly unknown irregular
intervals, of physical events. Such systems arise in a variety of
contexts ranging from computer operating systems to the control of
complex multimode processes. A control theory for the logical aspects of
such DESs is surveyed. The focus is on the qualitative aspects of
control, but computation and the related issue of computational
complexity are also considered. Automata and formal language models for
DESs are surveyed
In this paper, we study the problem of integrated berth and quay crane allocation (I-BCAP) in general seaport container terminals and propose the model predictive allocation (MPA) algorithm and preconditioning methods for solving the I-BCAP. First, we propose a dynamical modeling framework based on discrete-event systems (DESs), which describes the operation of a berthing process with multiple discrete berthing positions and multiple quay cranes. Second, based on the discrete-event model, we propose the MPA algorithm for solving the I-BCAP using the model predictive control (MPC) principle with a rolling event horizon. The validation and performance evaluation of the proposed modeling framework and allocation method are done using: 1) extensive Monte Carlo simulations with realistically generated datasets; 2) real dataset from a container terminal in Tanjung Priuk port, located in Jakarta, Indonesia; and 3) real life field experiment at the aforementioned container terminal. The numerical simulation results show that our proposed MPA algorithm can improve the efficiency of the process where the total handling and waiting cost is reduced by approximately 6%-9% in comparison with the commonly adapted method of first-come first-served (FCFS) (for the berthing process) combined with the density-based quay cranes allocation (DBQA) strategy. Moreover, the proposed method outperforms the state-of-the-art hybrid particle swarm optimization (HPSO)-based and genetic algorithm (GA)-based method proposed in the recent literature. The real life field experiment shows an improvement of about 6% in comparison with the existing allocation method used in the terminal.
Berth allocation problem (BAP) and quay crane assignment problem (QCAP) are two essential seaside operations planning problems faced by operational planners of a container terminal. The two planning problems have been often solved by genetic algorithms (GAs) separately or simultaneously. However, almost all these GAs can only support time-invariant QC assignment in which the number of QCs assigned to a ship is unchanged. In this study a hybrid particle swarm optimization (HPSO), combining an improved PSO with an event-based heuristic, is proposed to deal with two specific seaside operations planning problems. , the dynamic and discrete BAP (DDBAP) and the dynamic QCAP (DQCAP). In the HPSO, the improved PSO first generates a DDBAP solution and a DQCAP solution with time-invariant QC assignment. Then, the event-based heuristic transforms the DQCAP solution into one with variable-in-time QC assignment in which the number of QCs assigned to a ship can be further changed. To investigate its effeteness, the HPSO has been compared to a GA (namely GA1) with time-invariant QC assignment and a hybrid GA (HGA) with variable-in-time QC assignment. Experimental results show that the HPSO outperforms the HGA and GA1 in terms of fitness value (FV).
Container vessel stowage planning is a hard combinatorial optimization problem with both high economic and environmental impact. We have developed an approach that often is able to generate near-optimal plans for large container vessels within a few minutes. It decomposes the problem into a master planning phase that distributes the containers to bay sections and a slot planning phase that assigns containers of each bay section to slots. In this paper, we focus on the slot planning phase of this approach and present a Constraint Programming and Integer Programming model for stowing a set of containers in a single bay section. This so-called slot planning problem is NP-hard and often involves stowing several hundred containers. Using state-of-the-art constraint solvers and modeling techniques, however, we were able to solve 90% of 236 real instances from our industrial collaborator to optimality within 1 second. Thus, somewhat to our surprise, it is possible to solve most of these problems optimally within the time required for practical application.
As more and more container terminals open up all over the world, terminal operators are discovering that they must increase quay crane work rates in order to remain competitive. In this paper we present a simulation study that shows how a terminal's long-run average quay crane rate depends on (1) the length of the storage blocks in the terminal's container yard and (2) the system that deploys yard cranes among blocks in the same zone. Several different block lengths and yard crane deployment systems are evaluated by a fully dynamic, discrete event simulation model that considers the detailed movement of individual containers passing through a vessel-to-vessel transshipment terminal over a several week period. Experiments consider four container terminal scenarios that are designed to reproduce the multi-objective, stochastic, real-time environment at a multiple-berth facility. Results indicate that a block length between 56 and 72 (20-ft) slots yields the highest quay crane work rate, and that a yard crane deployment system that restricts crane movement yields a higher quay crane work rate than a system that allows greater yard crane mobility. Interestingly, a block length of 56–72 slots is somewhat longer than the average block in use today. The experiments provide the first direct connection in the literature between block length and long-run performance at a seaport container terminal. The simulator can be suitably customized to real, pure-transshipment ports and adequately tuned to get an appreciable prescriptive power.
Berth scheduling aims to optimally schedule vessels to berthing areas along a quay and is a complex optimization problem. In this paper we propose a lamda-optimal based heuristic as a resolution approach for the discrete space berth scheduling problem. The proposed heuristic can also be applied to validate optimality, in the case where other (meta)heuristics are applied as resolution approaches. A second internal Genetic Algorithms based heuristic is also proposed to reduce the computational time required for medium to large scale instances. Numerical experiments performed show that the proposed heuristic is adequate to produce near-optimal results within acceptable computational times.