Problem In Container Terminals Research Articles

Multi-Agent Deep Reinforcement Learning for Recharging-Considered Vehicle Scheduling Problem in Container Terminals

Multi-Agent Deep Reinforcement Learning for Recharging-Considered Vehicle Scheduling Problem in Container Terminals

Flexible storage yard management in container terminals under uncertainty

As the port bottleneck shifts from the shore side to the yard side, yard management is crucial to the operation efficiency of the container port. This paper studies the yard management problem in container terminals under the flexible storage strategy which does not fix the size of the storage templates in the yard. The problem considers the uncertainty of container demand and the flexibility of the block and jointly optimizes the storage space allocation, yard crane deployment, and container allocation problems. A two-stage stochastic optimization model under uncertainty is established to minimize the port operation cost. Two heuristic algorithms, a column generation-based heuristic algorithm and a tabu search algorithm, are proposed to solve the model. Numerical experiments validate the effectiveness of the proposed algorithms. Furthermore, some management implications for port operators are proposed according to the results of sensitivity analysis experiments.

An efficient heuristic algorithm for solving a multi-vessel quay crane scheduling problem in container terminals

As an important node of a multimodal container transport hub, the container terminal occupies a critical position in the whole process of container transportation and the global supply chain network. With the increasing size of container vessels, improving the handling efficiency of multiple vessels at berth in a given port becomes a complex problem to solve. We formulate a mixed-integer linear programming model to minimise the vessel services time, the total completion and travel times of quay cranes (QCs) among multiple vessels. To solve the proposed model, we develop a particle swarm optimisation (PSO) algorithm incorporated with a tailored initial-solution generation algorithm. Furthermore, a couple of acceleration strategies are designed to improve the solution quality and shorten computation time. The results of numerical experiments on real-life instances validate the proposed model and the efficiency of the proposed algorithm.

An efficient heuristic algorithm for solving a multi-vessel quay crane scheduling problem in container terminals

An efficient heuristic algorithm for solving a multi-vessel quay crane scheduling problem in container terminals

Machine Learning Guides the Solution of Blocks Relocation Problem in Container Terminals

The blocks relocation problem (BRP) is a well known and important combinatorial optimization problem, in which the initial storage state and retrieval priority of containers are known, and the containers should be picked up in the retrieval order with the goal of minimizing container relocations. This paper studied how machine-learning techniques guide the solution of this Non-deterministic Polynomial problem(NP-hard problem) NP-hard problem. Through our self-developed data generator, we generated initial state stacking matrices and extracted 22 influencing factors for container relocation operations. The supervised learning method and attribution technique were used to verify the relationship between significant container relocated influence features and the number of container relocations using the unrestricted BRP and restricted BRP models. We characterize the potential patterns in the data based on 22 container relocated influence features using four supervised learning models: random forest (RF), extra trees (ET), support vector machine (SVM), and logistic regression (LR). The experimental results demonstrate that RF has a classification accuracy rate of up to 94% on the restricted BRP model. The attribution technique identifies the most sensitive features to the number of container relocations. We organically integrate machine learning into the BRP problem and propose an interactive iterative framework that may provide a novel method for studying the BRP problem.

An exact algorithm for the unrestricted container relocation problem with new lower bounds and dominance rules

The container relocation problem, also known as the block(s) relocation problem, is one of the most studied optimization problems in container terminals. The problem aims at minimizing the total number of relocations for retrieving containers from a storage yard according to a specific order. The purpose of this study is to develop an efficient iterative deepening branch-and-bound algorithm for exactly solving one of the most practical variants of the problem, namely the unrestricted container relocation problem with duplicate priorities, which has received less attention in the literature. To improve the search efficiency of the proposed algorithm, we design two new lower bounds that can be computed quickly to incorporate them into the branch-and-bound algorithm. We also present a set of mutually consistent dominance rules to reduce the search space while avoiding over-pruning. The performance of the proposed algorithm is evaluated by extensive computational experiments on three commonly used benchmark datasets. The results show that the proposed algorithm outperforms the state-of-the-art exact algorithm for the unrestricted container relocation problem with distinct priorities, although our algorithm is applicable to a more general variant of the problem. Moreover, it can still provide competitive results for small- and medium-sized instances under a strict time limit of one second in comparison to existing metaheuristic approaches.

Particle swarm optimization algorithm with time buffer insertion for robust berth scheduling

This paper investigates the robust berth allocation problem in container terminals. To handle the uncertainties in vessel arrivals, the problem is formulated as a scenario-based two-stage stochastic programming model. Furthermore, we introduce the time buffers to the model. We then develop an algorithm for time buffer insertion, which accommodates the adaptive search procedure for the time buffer into the Particle Swarm Optimization (PSO) algorithm. Different from the traditional PSO algorithm, a core operator is designed with a modified version to take the intelligent time buffer insertion approach. The results of the numerical experiments show that the proposed method consistently provides an improved performance in terms of solution quality, compared with the previous studies and the one with a traditional operator in the PSO algorithm.

Constraint programming models for integrated container terminal operations

Although operations in container terminals are highly interdependent, they are traditionally optimized by decomposing the overall problem into a sequence of smaller sub-problems, each focusing on a single operation. Recent studies, however, have demonstrated the need and potential of optimizing these interdependent operations jointly. This paper proposes the Integrated Port Container Terminal Problem (IPCTP) that considers the joint optimization of quay crane assignment and scheduling, yard crane assignment and scheduling, yard location assignments, and yard truck assignment and scheduling. The IPCTP aims at minimizing the turnover times of the vessels and maximize terminal throughput. It also considers inbound and outbound containers simultaneously and models the safety distance and the interference constraints for the quay cranes. To solve the IPCTP, the paper proposes several constraint programming (CP) models. Computational results show that CP provides exact solutions in acceptable time to IPCTP instances derived from an actual (small) container terminal in Turkey. For hard IPCTP instances, the CP model can be generalized in a two-stage optimization approach to produce high-quality solutions in reasonable times.

Container reshuffling considered space allocation problem in container terminals

Space allocation problem (SAP) in container terminals is one of the yard management challenges in short-term planning, however, the impact of container reshuffling to the SAP is neglected in much of the literature as well as in real terminal planning. This study focuses on the SAP that accounts for container reshuffling, where the macro-level impact of reshuffling is derived from discrete event simulation and integrated into mix integer programming model. Empirical approaches are developed to achieve a trade-off between fast computation and good solutions. The result shows that ignoring reshuffling activities during planning will lead to overestimation on yard capacity.

Multi-objective bacterial colony optimization algorithm for integrated container terminal scheduling problem

This paper proposes a multi-objective integrated container terminal scheduling problem considering three key components: berth allocation, quay cranes assignment and containers transportation in port operation process. In the suggested problem, one of the objectives is to shorten service time of ships with by coordinating of quay cranes, and the other is to reduce operating costs of quay cranes and yard trucks. Then, a Multi-objective Bacterial Colony Optimization algorithm (MOBCO) incorporating concepts of multi-swarm, topology, personal best and global best, named Multi-objective BCO with ring topology (MORBCO), is designed to handle the resulting problem. The extension of standard MOBCO to the MORBCO involves the addition of three specialized strategies: global chemotaxis operation, elite reproduction strategy and personal best archive with neighborhood communication mechanism. In order to test the performance of the MORBCO, benchmark tests are performed and compared with traditional MOBCO and three other well-known multi-objective algorithms first. The computational results indicate that the proposed algorithm can outperform other rivals and efficiently solve a variety of multi-objective problems in most of cases. Subsequently, MORBCO and two best performing algorithms from the previous test are applied to three instances generated by the proposed model. Judging by quality and diversity of obtained non-dominant solutions, we find that MORBCO has superior performance, especially for large instances of the container terminal problem.

A decision support system for a capacity management problem at a container terminal

This paper aims to design an optimization-based decision support system (DSS) for a capacity management problem in container terminals. The capacity management problem at a container terminal seeks to balance supply (equipment assignment) and demand (arrivals of trucks) while improving customer service level and productivity indicators. Specifically, this paper addresses the problem of determining the appointment quota for each time slot on a one-day planning horizon, within a container terminal working on a truck appointment system (TAS) environment. The DSS is developed in cooperation with an industrial partner to help them with their operational decision making when determining the appointment quotas, considering yard resource availability. The underlying approach makes use of a tabu search metaheuristic algorithm to solve a related optimization problem that balance the supply and demand of moves in the yard. The DSS is flexible, allowing several parameters to be configured and read from external files. Experimental examples using real-world data were conducted to evaluate the effectiveness of the system, and the suitability of the DSS.

Mathematical formulation and heuristic algorithm for the block relocation and loading problem

AbstractThis study considers the block relocation and loading problem in container terminals. The optimal loading sequence and relocation location are simultaneously decided on the basis of the desired ship‐bay and initial yard space configuration. An integer linear programming model is developed to minimize the number of relocations in the yard space on the basis of no shifts in the ship bay. The accuracy of the model is tested on small‐scale scenarios by using CPLEX. Considering the problem size in the real world, we present a rule‐based heuristic method that is combined with a mathematical model for the removal, loading, and relocation operations. The influence of rules on algorithm performance is also analyzed, and the heuristic algorithm is compared with different types of algorithms in the literature. The extensive numerical experiments show the efficiency of the proposed heuristic algorithm.

A Dynamic and Discrete Berth Allocation Problem in Container Terminals Considering Tide Effects

This study aims to minimize vessels’ departure delay time at Shahid Rajaee (SHRA) shallow port of Iran. The problem of SHRA port is its low-depth channel; explaining that when the ebb happens, vessels with high draft are not able to pass through the access channel and thus have to wait for the flow. A mixed integer linear planning model, which is capable of considering tide effects in berth allocation problem, was utilized for the purpose of the study. Loading and unloading data of SHRA port’s current situation were compared with the model outputs to validate the model. The results showed that the proposed model can considerably reduce vessels’ delay time. Next, a sensitivity analysis was performed on the depth of the access channel. The results revealed that channel dredging can significantly reduce vessels’ departure delay time and improve the performance of SHRA container port. Finally, active and active–reactive procedures were suggested to consider parameters’ uncertainty. The findings indicated that active–reactive procedure outperformed active procedure.

Modeling and Solution of Joint Storage Space Allocation and Handling Operation for Outbound Containers in Rail-Water Intermodal Container Terminals

Storage space allocation and handling operation problems are two main crucial problems in container terminals. Early research efforts, however, are seldom devoted to studying them together. Therefore, this paper considers these two problems simultaneously for outbound containers in rail-water intermodal container terminals (RWICTs), where rail-mounted gantry cranes, inner trucks, and quay cranes are involved. A two-stage problem is proposed: Stage 1 is to determine locations of the containers and reduce the overlapping amount, considering container weight, departure time, destination ports, and containers left from earlier planning periods in railway container yards, according to the locations of containers from Stage 1; and Stage 2 aims at obtaining optimal job sequences of different types of equipment and minimizing makespans of handling operations, considering some operational constraints, particularly rehandling time and inner truck congestion. To solve the problem, a two-stage heuristic algorithm is proposed, where the rolling planning horizon and a new update strategy are introduced. A heuristic algorithm is introduced in Stage 1 and a novel two-layer genetic algorithm is proposed in Stage 2, which introduces proximity principles and the reselection operation. Afterward, the results from Stage 2 are used to resolve the first stage problem, while the problem in Stage 2 is also resolved using the new results from Stage 1. This iterative process continues until there are no more improvements in Stage 1. Finally, the results of the computational experiments indicate that the proposed model and solution approaches are effective and efficient in solving the two-stage problem for outbound containers in RWICTs.

The Joint quay crane scheduling and block allocation problem in container terminals

The Joint quay crane scheduling and block allocation problem in container terminals

New solution approaches for the train load planning problem

The present paper faces the train load planning problem in container terminals. The problem consists of assigning containers to rail wagons while maximizing the total priority of the containers loaded and minimizing the number of rehandles executed in the terminal yard. Two different heuristic approaches, based on an innovative way to compute weight limitations and on two 0/1 integer programming models, are proposed and compared on the basis of specific key performance indicators. The heuristic approaches are compared using random generated instances based on real-world data. An extensive computational analysis has been performed.

Improved formulations and an Adaptive Large Neighborhood Search heuristic for the integrated berth allocation and quay crane assignment problem

This paper focuses on the integrated berth allocation and quay crane assignment problem in container terminals. We consider the decrease in the marginal productivity of quay cranes and the increase in handling time due to deviation from the desired position. We consider a continuous berth, discretized in small equal-sized sections. A number of enhancements over the state-of-the-art formulation and an Adaptive Large Neighborhood Search (ALNS) heuristic are presented. Computational results reveal that the enhancements improve many of the best-known bounds, and the ALNS outperforms the state-of-the-art heuristics for many instances. We also conduct further analysis on a new larger benchmark.

A robust optimization approach to the integrated berth allocation and quay crane assignment problem

This paper investigates the integrated berth allocation and quay crane assignment problem in container terminals. A deterministic model is formulated by considering the setup time of quay cranes. However, data uncertainties widely exist, and it may cause the deterministic solution to be infeasible. To handle the uncertainties, a robust optimization model is established. Furthermore, to control the level of conservativeness, another robust optimization model with the price constraints is proposed. A genetic algorithm and an insertion heuristic algorithm are suggested to obtain near optimal solutions. Computational experiments indicate that the presented models and algorithms are effective to solve the problems.

Scheduling co-operating stacking cranes with predetermined container sequences

With the proliferation of multi-gantry automated stacking cranes, the already difficult crane scheduling problem in container terminals has become even more challenging. In this paper we present an efficient algorithm that can solve a sub-problem that arises in this context, namely the prioritization of crane gantry movements once transportation tasks have been assigned. We tackle this problem for both, twin crane setting and crossover crane setting, and develop graphical models and strongly polynomial algorithms accordingly. A series of experiments is carried out where it is shown that the method can produce optimum solutions with exceptionally small run times.

Priority control of berth allocation problem in container terminals

This paper presents a decision support system for the core problem of berth allocation decision in a container terminal. The allocation of berths to the calling vessels is complex with the fact that different service level requirements are required for different vessels. Terminal managers demand for effective decision support systems that would aid them with the allocation problem considering service priorities. Consequently, this study provides a DSS, built by a dynamic discrete-event simulation model embedded with an optimization tool that determines the priority controls for the berth allocation to the calling vessels. To show the practical application of the DSS, a comprehensive case study from a Turkish container terminal considering the current state and future expansion plans that also provides an indication of the usability aspect of the program on other ports around the world has been conducted. Further experiments are conducted based on data from the Port of Rotterdam. The DSS presented in this study may help port authorities in determining more efficient allocation decisions within a container terminal.