Container Handling System Research Articles

A 3D World Interpreter System for Safe Autonomous Crane Operation

In an effort to improve short-sea shipping in Europe, we present a 3D world interpreter (3DWI) system as part of a robotic container-handling system. The 3DWI is an advanced sensor suite combined with AI-based software and the communication infrastructure to connect to both the crane control and the shore control center. On input of LiDAR data and stereo captures, the 3DWI builds a world model of the operating environment and detects containers. The 3DWI and crane control are the core of an autonomously operating crane that monitors the environment and may trigger an emergency stop while alerting the remote operator of the danger. During container handling, the 3DWI scans for human activity and continuously updates a 3D-Twin model for the operator, enabling situational awareness. The presented methodology includes the sensor suite design, creation of the world model and the 3D-Twin, innovations in AI-detection software, and interaction with the crane and operator. Supporting experiments quantify the performance of the 3DWI, its AI detectors, and safety measures; the detectors reach the top of VisDrone’s leaderboard and the pilot tests show the safe autonomous operation of the crane.

Resilience measurement and dynamic optimization of container logistics supply chain under adverse events

Adverse events may cause chaotic phenomena such as uncertain delay, port congestion, and slow turnover efficiency of container ships, making the container logistics supply chain (CLSC) suffer a serious impact. In order to fully describe and model the adverse events, and further adjust and optimize the container logistics system, this study designs a two-stage container logistics supply chain model, mainly including a container pretreatment system (CPS) and a container handling system (CHS). Taking into account the real-time measurement of the overall CLSC resilience and the need to guide the adjustment method, a novel two-dimensional resilience index in terms of affordability and recovery ability is proposed to reveal the inherent resilience performance of the system. By decomposing the resilience index, the interaction mechanism between the internal elements can be further explored. An adaptive fuzzy double-feedback adjustment (AFDA) control structure is designed to optimize the two-stage CLSC system in order to alleviate the influence of adverse events, enhance the response and resilience performance, and stabilize the system as soon as possible. The simulation results show that under the influence of adverse events, the resilience performance and the ability to maintain stability is obviously weakened. Through feedback control of two-stage system responses, the above adverse effects can be effectively alleviated. Compared with other existing optimization strategies, the dynamic optimization method designed in is paper is more conducive to improving the response performance and enhancing the resilience of the system. The rationality and effectiveness are also verified by the simulation results.

A Genetic Algorithm for Integrated Scheduling of Container Handing Systems at Container Terminals from a Low-Carbon Operations Perspective

At container terminals, quay cranes, yard trucks, and yard cranes are mainly used to transfer containers. Driven by the demand for a green and low-carbon economy, an integrated scheduling problem considering three types of handling equipment of container handling systems is studied. As the task of transferring each container is completed by the three handling equipment sequentially, the optimal solution may not be found by only studying one type of equipment separately from a green operations perspective. The inter-dependency of different equipment should be considered to guarantee the overall performance of container handling systems with low-carbon operations so as to reduce energy consumption. In this paper, this integrated problem is formulated as a mixed integer linear programming (MILP). Since the MILP cannot be applied to solve large-sized practical problems, a genetic algorithm (GA) is developed. In the proposed GA, a three-dimension chromosome representation is proposed, which integrates the coordination of three handling equipment. A new mechanism including three pairs of crossover and mutation is used in parallel in GA with the aim of enhancing the efficiency of searching for good solutions. Each pair of crossover and mutation is specific to one dimension of a solution. Moreover, a novel heuristic mutation is developed to diversify solutions. The computational results indicate that the developed solution method for the integrated scheduling problem is promising and the heuristic mutation can highly improve the solution quality.

Resilience Regulation Strategy for Container Port Supply Chain under Disruptive Events

There are many inevitable disruptive events, such as the COVID-19 pandemic, natural disasters and geopolitical conflicts, during the operation of the container port supply chain (CPSC). These events bring ship delays, port congestion and turnover inefficiency. In order to enhance the resilience of the CPSC, a modified two-stage CPSC system containing a container pretreatment system (CPS) and a container handling system (CHS) is built. A two-dimensional resilience index is designed to measure its affordability and recovery. An adaptive fuzzy double-feedback adjustment (AFDA) strategy is proposed to mitigate the disruptive effects and regulate its dynamicity. The AFDA strategy consists of the first-level fuzzy logic control system and the second-level adaptive fuzzy adjustment system. Simulations show the AFDA strategy outperforms the original system, PID, and two pipelines for improved dynamic response and augmented resilience. This study effectively supports the operations manager in determining the proper control policies and resilience management with respect to indeterminate container waiting delay and allocation delay due to disruptive effects.

Hazard Analysis of an Autonomous Container Handling System – a Comparison of STPA and HAZOP Methods

Increasing automation is a major trend in container terminals. In automated container handling systems, safety has been previously ensured by segregating the automated machinery from other traffic and workers moving on foot. Currently, further increases in flexibility are sought by developing autonomous systems that are capable of mixed-traffic operations without the need for separate operating areas. This increases the complexity of the systems and introduces new safety hazards. In addition to traditional hazard analysis methods, new approaches are needed to address the emergent risks related to autonomous operations. This paper studies the applicability of the STPA (system-theoretic process analysis) method in hazard analysis of an autonomous machine system. To support the evaluation, we define evaluation categories for comparison of the analysis methods. We also compare STPA with an established method, HAZOP (hazard and operability study). To perform the comparison, both STPA and HAZOP are applied to an autonomous container handling system concept. The study suggests that both STPA and HAZOP are well suited to support the development of autonomous machinery. However, we also highlight some notable differences in the methods, mostly related to the different underlying accident models that they utilise. HAZOP is an established method with tools and standards available. STPA, on the other hand, provides a well-defined syntax to ensure the analysis quality and a system modelling approach that supports the system development.

Intelligent Operator Support Concepts for Shore Control Centres

This paper introduces three intelligent operator support functions to allow multiple operators to effectively and efficiently supervise multiple autonomous operations. The many-to-many concept assumes a stage in human-automation collaboration design where supervision of maritime autonomous surface ships is not permanently required anymore. Only in extreme and very rare situations the human may need to intervene. One of the challenges is balancing the task assignments and support functions over the operators to ensure the cognitive task load matches the operator’s mental capacity. For this purpose we introduce and described a dynamic task allocation algorithm. Also, human attention is limited and operators therefore must constantly shift attention resulting in moment-to-moment fluctuations in situation awareness. To overcome these reductions in situation awareness, operators must reassess the environment to recover situation awareness. We introduce the concept of continuous risk assessment to initiate the process of situation awareness recovery. Furthermore, the many-to-many ratio between supervising operators and autonomous ships implies that operators will not be able to supervise all ships in parallel. This makes current supervisory control interfaces less suitable. Instead we opt to apply the idea of progressive disclosure in the operator’s interface and interactions. The work described in this paper is directed towards developing an intelligent operator support system with which the operator support functions will be demonstrated as part of a Robotic Container Handling System, an innovation of the European MOSES research project.

Container Terminal Berth and Yard Collaborative Allocation for Import and Export Synchronous Operations by Computational Logistics

Container terminal operators have practical and clear requirements on improving operational efficiency and service level to address running challenges. The resource allocation and collaborative scheduling of quayside berth and storage yard are vital issues in the optimization of container terminal handling systems (CTHSs). The allocation of berth in quayside and blocks on storage yard directly affects the running efficiency of CTHS. Consequently, we focus on the berth and yard collaborative allocation for import and export synchronous operations (BYCA-IESOs) and propose the BYCA-IESO research model based on synchronous or asynchronous integrated scheduling strategy by computational logistics. The BYCA-IESO model is a mixed integer quadratic programming model to minimize multiple time consumption of calling liners and the total horizontal transferring distances of yard trailers at container terminals. Furthermore, we design other two allocation policies for comparison on the BYCA-IESO, which are both multistage scheduling strategy profiles. CPLEX 20.1.0 is applied to implement and solve the model and tactics of BYCA-IESO. Accordingly, the feasibility, validity, and relative merits of the models and strategies are obtained and verified by analyzing and comparing multiple numerical experimental results.

Multiobjective Scheduling for Cooperative Operation of Multiple Gantry Cranes in Railway Area of Container Terminal

Railway station plays an important role in improving the operation efficiency of rail-sea intermodal container terminal. The cooperative scheduling of multiple gantry cranes (GCs) can reduce the production and operation cost of railway station. Most studies look into discarding the conflict schemes of GCs, which may obtain excellent scheduling results. These existing conflict-free strategies can not contribute to enhancing the cooperative scheduling performance of multiple GCs. This study integrates container trucks into multiple GCs scheduling environment to eliminate those conflicts, and proposes a mixed-integer programming model considering cooperation between multiple GCs and trucks. The model aims to simultaneously minimize the makespan of the container handling system in station, the total empty travel time of GCs and the total energy consumption of both cranes and trucks. To solve the proposed model, a conflict-free operation strategy for multiple GCs based on hybrid indirect loading and unloading (CFHI) is proposed. CFHI strategy is implemented according to the cooperation between GCs and trucks. An effective multi-objective artificial bee colony algorithm (EMOABC) based on CFHI and fuzzy correlation entropy (FCE) is developed. Within the developed algorithm, an encoding/decoding method based on CFHI is designed to represent and decode the population solutions. The FCE is adopted to evaluate and select the better solutions for next iteration evolution. The effectiveness of the proposed CFHI strategy is verified by comparing it with two popular equipment allocation strategies. Extensive experimental results show that proposed EMOABC is effective to the proposed model. Our findings here have significant implications for the cooperative operation of multiple GCs considering energy consumption in container terminal.

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

The synergy of computational logistics and deep learning provides a new methodology and solution to the operational decisions of container terminal handling systems (CTHS) at the strategic, tactical, and executive levels. Above all, the container terminal logistics tactical operational complexity is discussed by computational logistics, and the liner handling volume (LHV) has important influences on a series of terminal scheduling decision problems. Subsequently, a feature-extraction-based lightweight convolutional and recurrent neural network adaptive computing model (FEB-LCR-ACM) is presented initially to predict the LHV by the fusion of multiple deep learning algorithms and mechanisms, especially for the specific feature extraction package of tsfresh. Consequently, the container-terminal-oriented logistics service scheduling decision support design paradigm is put forward tentatively by FEB-LCR-ACM. Finally, a typical large-scale container terminal of China is chosen to implement, execute, and evaluate the FEB-LCR-ACM based on the terminal running log around the indicator of LHV. In the case of severe vibration of LHV between 2 twenty-foot equivalent units (TEUs) and 4215 TEUs, while forecasting the LHV of 300 liners by the log of five years, the forecasting error within 100 TEUs almost accounts for 80%. When predicting the operation of 350 ships by the log of six years, the forecasting deviation within 100 TEUs reaches up to nearly 90%. The abovementioned two deep learning experimental performances with FEB-LCR-ACM are so far ahead of the forecasting results by the classical machine learning algorithm that is similar to Gaussian support vector machine. Consequently, the FEB-LCR-ACM achieves sufficiently good performance for the LHV prediction with a lightweight deep learning architecture based on the typical small datasets, and then it is supposed to overcome the operational nonlinearity, dynamics, coupling, and complexity of CTHS partially.

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 bottlenecks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions.

Combining mixed integer programming and constraint programming to solve the integrated scheduling problem of container handling operations of a single vessel

In the container terminals of seaports, the container handling system consists of a variety of container handling machines such as quay cranes, internal yard trucks, and yard cranes. This study applies a holistic approach to the integrated scheduling of these machines for the container handling operations of a single vessel. We formulate this special hybrid flow shop scheduling problem through both mixed integer programming (MIP) and constraint programming (CP) techniques. Then we develop an easily-implemented approach that combines the strengths of MIP and CP. First, the MIP model, which only considers quay crane scheduling, is solved by an MIP solver, and a quay crane allocation plan is retrieved from the MIP solution. Then, this quay crane allocation plan is fed to the CP model, warm-starting the branch-and-prune algorithm built in a CP optimizer. Our numerical experiments reveal that this hybrid MIP/CP approach can solve the large-sized instances with up to 1000 containers, 6 quay cranes, 36 yard trucks, and 15 yard cranes to optimality with a gap of less than 3.31%, within a solution time of 2 minutes. If we increase the solution time to 5 minutes, this hybrid approach solves larger instances with up to 1400 containers to optimality with a gap of less than 1.41%. The state-of-the-art dedicated algorithms reported in the literature (which address an easier version of the same problem by ignoring non-crossing constraints and safety margins between quay cranes) are only able to find solutions for real-life instances with up to 500 containers within the solution time of 2930 or 5221 seconds, leaving a 4% or an unknown optimality gap. Thus, this study improves the solution of this integrated scheduling problem in terms of the instance size, solution efficiency, and solution optimality.

Relative Efficiencies of ASEAN Container Ports based on Data Envelopment Analysis

ABSTRACT Ports play a critical role in the economies of many countries in the ASEAN region. Efficient ports facilitate a country's imports and exports, and the “relative efficiency” of a port is evaluated based on its efficiency compared to others in its group. This study analyzes the relative efficiencies of 50 ASEAN container ports and terminals. These ports are categorized according to their container handling system and location (those located in a riverbank connected to the sea are called “inland seaports” and those by the seaside are called “seaports”). The traditional output-oriented data envelopment analysis method is applied, and measures of super-efficiency constant returns to scale are estimated in order to compare the units situated on the efficiency frontier. The findings may support port managers in the ASEAN region to make decisions on whether to increase container traffic. In addition, policymakers may consider the evaluation results in deciding whether to improve the trans-ASEAN transport network and ASEAN trade competitiveness.

Control of interacting machines in automated container terminals using a sequential planning approach for collision avoidance

The control of automated container terminals is complex since Quay Cranes (QCs), Automated Guided Vehicles (AGVs) and Automated Stacking Cranes (ASCs) interact intensively for transporting containers, while collision avoidance of equipment must be ensured. This paper proposes a methodology to generate collision-free trajectories of free-ranging AGVs in automated container terminals, while minimizing the makespan of the whole container handling system. A hierarchical control architecture is proposed to integrate the scheduling of interacting machines and trajectory planning of AGVs. Following a so-called overall graph sequence by a scheduler, the collision-free trajectories of AGVs are determined by solving a collection of mixed integer linear programming problems sequentially. Simulation results illustrate the potential of the proposed methodology.

Integrated scheduling of a container handling system with simultaneous loading and discharging operations

The integrated scheduling of container handling systems aims to optimize the coordination and overall utilization of all handling equipment, so as to minimize the makespan of a given set of container tasks. A modified disjunctive graph is proposed and a mixed 0–1 programming model is formulated. A heuristic algorithm is presented, in which the original problem is divided into two subproblems. In the first subproblem, contiguous bay crane operations are applied to obtain a good quay crane schedule. In the second subproblem, proper internal truck and yard crane schedules are generated to match the given quay crane schedule. Furthermore, a genetic algorithm based on the heuristic algorithm is developed to search for better solutions. The computational results show that the proposed algorithm can efficiently find high-quality solutions. They also indicate the effectiveness of simultaneous loading and discharging operations compared with separate ones.

Simulation-Based Optimization for Storage Allocation Problem of Outbound Containers in Automated Container Terminals

Storage allocation of outbound containers is a key factor of the performance of container handling system in automated container terminals. Improper storage plans of outbound containers make QC waiting inevitable; hence, the vessel handling time will be lengthened. A simulation-based optimization method is proposed in this paper for the storage allocation problem of outbound containers in automated container terminals (SAPOBA). A simulation model is built up by Timed-Colored-Petri-Net (TCPN), used to evaluate the QC waiting time of storage plans. Two optimization approaches, based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are proposed to form the complete simulation-based optimization method. Effectiveness of this method is verified by experiment, as the comparison of the two optimization approaches.

Rescheduling of interacting machines in automated container terminals

The current scheduling scheme of container terminals is typically determined offline. This may result in delays of the complete operation, when disturbances (e.g., the operation delays or the breakdown of a machine) appear. This paper provides a method for rescheduling interacting machines in automated container terminals. The rescheduling is carried out using the current state measurements of the machines. These measurements are used to update the processing time of ongoing operations. The effect of rescheduling on both a time-efficient schedule and for an energy-efficient schedule is illustrated in a simulation study. The delay in the container handling system is reduced both for the time-efficient schedule and the energy-efficient schedule. A simulation study illustrates that the energy-efficient schedule is more sensitive to disturbances due to delays of machines than the time-efficient schedule.

Optimising container storage processes at multimodal terminals

Multimodal Container Terminals (MMCT) require comprehensive planning, programming and control in order to operate an efficient storage system. In this paper, a model has been developed for the storage system of the MMCT. It is an extension of the Blocks Relocation Problem, with incoming as well as outgoing containers, or ‘blocks’. The model deals with assigning containers to positions within the storage area, rehandling of containers to be retrieved, and calculation of the processing times in order to perform these actions. A number of constructive heuristics are presented in order to produce good initial solutions for this problem. Meta-heuristics are also used to improve on these solutions. Results from the various heuristics on a few case study problems are compared and discussed. This research will also provide a core piece of technology for the development of autonomous container handling systems for terminals.

A scheduling problem for a novel container transport system: a case of mobile harbor operation schedule

Mobile Harbor (MH) is a movable floating platform with a container handling system on board so that it can load/discharge containers to/from an anchored container ship in the open sea. As with typical quay crane operation, an efficient schedule for its operation is a key to enhancing its operational productivity. A MH operation scheduling problem is to determine a timed sequence of loading/discharging tasks, assignment of MH units to each task, and their docking position, with an objective of minimizing the makespan of a series of incoming container ships. A mixed integer programming model is formulated to formally define the problem. As a practical solution method to the problem, this paper proposes a rule-based algorithm and a random key based genetic algorithm (rkGA). Computational results show that the rkGA method produces a better-quality solution than the rule-based method, while requiring longer computation time.

A genetic algorithm for the integrated scheduling model of a container-handling system in a maritime terminal

In this paper, a genetic algorithm (GA) is proposed for the integrated scheduling problem of loading and unloading operations on handling and transportation equipment in a container terminal. This problem is a class of hybrid flow shop scheduling with unrelated parallel machines, precedence relationships, sequence-dependent set-up times, blocking, and machine eligibility as additional constraints. Considering these constraints, the proposed GA assigns jobs to machines at every stage through the evaluation function of the chromosomes. In addition to assignment, sequencing, timing, and makespan, the blocking times between stages and the congestion rate of routes are calculated in this model. A full factorial experimental design, using the analysis of variance (ANOVA) technique, is developed to calibrate different parameters and operators of the algorithm. The results indicate that some levels of parameters and operators lead to considerably better solutions. The experiments are based on data collected from Shaheed Rajaee Port (a port in the south of Iran) in order to analyse the performance of the algorithm.

모바일하버 선박의 계류안정화시스템 및 의장장치 개념설계

Mobile Harbor(MH) is a new paradigm for maritime transport system introduced in Korea, the target of which is to carry out ship-to-ship cargo operation rapidly and effectively even under a condition of sea state 3. A MH ship is moored alongside a large container vessel anchored at the defined anchorage and also equipped with gantry cranes for handling containers. The MH study concerned includes rapid container handling system, optimum design for floating structure, hybrid berthing & cargo operation system, design for cargo handling crane, etc. This paper is to deal with a conceptual design of a stabilized mooring system and mooring equipment under a condition of ship-to-ship mooring. In this connection, we suggest a positioning control winch system in order to control heave motions of the MH ship which is to add constant brakepower and stabilized function to an auto-tension winch and mooring equipment used currently in large container ships.