Straddle Carriers Research Articles

Integrated scheduling of multiple heterogeneous equipment and maintenance operations during discharging process in a container terminal under uncertainty

The integrated scheduling problem of container-handling equipment has been gaining prestige in recent years instead of decomposing the problem into a succession of subordinate sub-problems. This is due to decreasing operational costs, waiting time, completion time, and increasing efficiency. This study introduces an unprecedented integrated scheduling problem in a container terminal, wherein a broad range of heterogeneous container-handling equipment are considered, including gantry cranes, straddle carriers, transtainers, trailers, and outbound trucks. In this regard, an optimization model is developed for planning the discharging of heterogeneous containers, including storage and clearance, in three distinctive operational yards, including quay, storage, and clearance. Additionally, a wide range of realistic concerns are reflected, including maintenance operations, transport efficiency, and non-interference constraints. Furthermore, since maintenance operations’ times undeniably impact scheduling programming and involve various uncertainties, a new two-stage data-driven approach is offered to estimate reliable maintenance times. In the first stage, a hybrid machine learning approach is devised to estimate maintenance times, including the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the Takagi–Sugeno–Kang (TSK) fuzzy system, and the minibatch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA). In the second stage, since estimated maintenance operation times have uncertainty, the distributionally robust optimization (DRO) method with φ-divergence is utilized to tackle this challenge. Finally, a case study and various simulation experiments are investigated to illustrate the validation and applicability of the suggested framework.

Algorithms in the Margins: Organized Community Resistance to Port Automation in the Los Angeles Harbor Area

Public deliberations on artificial intelligence and machine learning (AI/ML) provoke strong interest in automation, or the perceived displacement of human labor due to general technological advances. Social science scholarship on well-compensated technology workers and a growing global underclass suggests automation remains largely a myth, with emerging technologies generating new forms of computational and emotional work. But little research has examined automation from the vantage point of community members at perceived greatest risk of displacement, such as those sustained by generations of logistics labor and physically located in racialized, underserved areas. In the United States and around the globe, logistics work is directly targeted by “automation engineers” to reduce labor costs on behalf of industry interests, posing a severe threat to port workers and their respective communities. This article examines organized community resistance to automation within the Los Angeles Harbor Area, drawing on four public hearings following the Port of LA’s planned integration of autonomous vehicles for the movement of cargo (i.e., automated straddle carriers). We find community mobilizations widely sought to enlarge public consideration of AI/ML to encompass matters of societal well-being and the collective good. These include shifting from the legality of AI integration to underscore public morality in light of anticipated harm, in addition to emphasizing the invisible social arrangements surrounding the Port and nearby community life. Our research reveals how lay people may reassert their own importance when confronted by technoscientific injustice, such as by reframing the future of work toward an insistence on “the future of our communities.”

Distributed set-membership estimation for automated straddle carriers using smart sensors

Considering the harsh environment of the port, automated straddle carriers, characterized by their large size, tall frame, and high center of gravity, may experience instability during steering and transportation due to inaccurate state estimation. Thus, this paper explores state estimation techniques for automated straddle carriers utilizing smart sensors which are capable of data measurement and processing. First, using the steering principles and lateral characteristics of automated straddle carriers, a dynamic linear model is established based on Newton’s second law of motion. Then, in order to enhance the reliability and flexibility of state estimation, a distributed smart sensor network structure is introduced. In addition, considering the challenge of unknown-but-bounded noise and the precision demands of the considered automated straddle carrier, a modified distributed set-membership estimation algorithm is proposed and is derived sufficient conditions for the existence of the estimation set for the considered automated straddle carriers. Finally, the effectiveness and superiority of the proposed method are demonstrated by performance analyses.

A VNS-based approach for solving the Manhattan metric straddle carrier routing problem with buffer areas

This paper presents a metaheuristic approach for solving an optimization problem that arises at container terminals where straddle carriers (SCs) transport containers between the stacking areas and the seaside. In such container terminals, operational efficiency depends mainly on SC routing. SCs routes should consider the order in which containers are unloaded and loaded at the quay cranes (QCs), taking into account the limited capacity of the buffer area of each QC where containers are temporarily stored after being handled by a QC or an SC. Besides the precedence relations (i.e., container sequences) and buffer capacities, the solution framework considers safety constraints. Efficient routing of SCs directly contributes to minimizing the idle time of QCs, thereby improving their overall productivity and minimizing the turnaround time of vessels, which is the objective of the problem. Specifically, we present two different variants of the Variable Neighborhood Search (VNS) algorithm. Each variant is initialized in both a greedy and a random manner. These algorithms address the problem by incorporating four LS operators commonly utilized in vehicle routing problems. We perform a comparative analysis of the results of these four approaches against each other and against solutions generated by an exact solver. Our numerical experiments show that the proposed algorithms perform better than the used solver, especially for bigger instances. A comparison with the results from the literature is also given and shows that the proposed VNS-based approach provides competitive results.

Automated rail-water intermodal transport container terminal handling equipment cooperative scheduling based on bidirectional hybrid flow-shop scheduling problem

The importance of rail-water intermodal transport is becoming more and more prominent in recent years. As a key node of rail-water intermodal transportation, the operation efficiency of automated container terminal directly affects the overall transportation efficiency. However, there are few studies on the collaborative scheduling of automated container terminal equipment in rail-water intermodal transportation. Therefore, this paper focuses on the ‘Automated Quay Crane (AQC)-Automated Straddle Carrier (ASC)-Automated Rail Mounted Gantry Crane (ARMG)’ operation system and addresses the operational efficiency requirements of the Automated Rail-Water Intermodal Transport Container Terminal (ARWITCT), Based on the bidirectional Hybrid Flow-shop Scheduling Problem (HFSP), a scheduling optimization model for the ARWITCT is established with the objective of minimizing the maximum completion time. To solve this complex scheduling problem, Particle Swarm Optimization based on Adaptive Tent Chaos Mapping (PSO-ATCM) is designed. In this algorithm, a parameter adaptive adjustment strategy and a Tent Chaos Mapping Optimization Strategy are introduced to help PSO jump out of local optimum. Problem-feature-based encoding and decoding methods are designed for the problem. An ‘insertion-translation’ mechanism is used to judge and resolve the conflicts between import and export containers during the decoding process. The effect of proposed scheduling model and the performance of the proposed algorithm are validated via comprehensive comparison experiments. To validate the effectiveness of proposed scheduling model, we compare the unidirectional HFSP model with the bidirectional HFSP model. The proposed PSO-ATCM is compared with standard Genetic Algorithm, Particle Swarm Optimization algorithm and Adaptive Genetic Algorithm. Computational results demonstrate that the proposed model is effective and PSO-ATCM performs better than other three compared algorithms.

Improved Kalman-Filter-Based Model-Predictive Control Method for Trajectory Tracking of Automatic Straddle Carriers

This paper considers the trajectory tracking problem for a class of automatic straddle carriers (ASCs) with external interferences and the overturning constraints. First, the steering and the dynamics of the ASC are analyzed and the mathematical model of the ASC is established. Then, considering the impact of external interferences on the trajectory tracking accuracy, an improved dynamic Kalman filter is designed in order to obtain the state estimation of the ASC. Based on the obtained state estimation, a model-predictive control method is proposed which takes the anti-overturning constraints into account. In addition, the improved Kalman-filter-based model-predictive control (iKFMPC) algorithm is summarized for the considered ASC to travel smoothly along the given trajectory while meeting the overturning resistance. Finally, simulation analyses demonstrate the effectiveness and superiority of the proposed method.

Informed simulation for dynamic path finding in human-operated container terminals

Path finding for human-operated vehicles in a maritime container terminal can be very demanding, especially when pursued under dynamic traffic conditions and based on decisions that are self-governing. Preventing collision and interference among internal transfer vehicles is a major task for operations managers in organizing container transfers from the quay to the yard at minimal congestion. To this matter, we propose a novel simulation framework as an effective tool for managing conflicting goals among straddle carrier (SC) drivers. The framework embeds both local and global search strategies that are used to determine the best path between any two vertices of the grid-like transfer network. Furthermore, it also allows to make path finding decisions based on either dynamic or static data. Numerical experiments prove the effectiveness of this informed simulation-based approach. Specifically, when critical extraordinary events arise and force, for instance, SCs to cover greater distances during container transfer, the difference in performance achieved by using the local/global path finding strategy in conjunction with static/dynamic data becomes more significant.

Proposal of a module-driven architecture for building simulation models devoted to container terminals: dilemmas in applying generic, flexible, and modular principles

Container terminals are complex systems where containerized cargo undergoes a set of processing and handling operations to be delivered to their outgoing modes. A pool of decision support methods and simulation models has been developed to assist planners and managers in making decisions about daily operations. Nevertheless, most are designed for a particular terminal and not generic types. Indeed, a generic model serves as a conceptual factory to create specific models which greatly reduces the time and efforts of development; however, building such a model is no mean feat. To this aim, the paper on hand discusses the complexity of applying genericity, flexibility, and modularity in system modeling and proposes a generic architecture to build modular and flexible simulation models for container terminals. This architecture is split into a set of smaller, manageable, well-connected, and generic modules that facilitate the creation of highly parametrized specific models. An illustrative example of the architecture usage is presented in a case study, the new container terminal of Stockholm, and the resulting models were validated by subject matter experts. Finally, to prove its efficiency, a numerical study fed with real data is conducted to investigate the handling capacity of the studied system under different handling and flow scenarios. The obtained results show that the terminal handling capacity can be increased by around 50% if three to four more straddle carriers are added to the existing fleet.

Research on coordinated scheduling of straddle carriers and quay cranes in automated container terminals based on reinforcement learning

Aiming at the coordination and scheduling problem between Automated Straddle Carrier (Automated Straddle Carrier) and Quay Crane (QC) in automated container terminals, consider that the quay crane cannot cross the straddle carrier, and the straddle carrier and the quay crane cannot be in adjacent lanes at the same time. A mixed-integer optimization model with the objective function of minimizing the final completion time of the quay crane is established under the constraints of different operating speeds and different speeds of straddle carriers in different states. A genetic algorithm based on reinforcement learning is designed, and the initial population is generated by the Q-learning algorithm, and the genetic algorithm (GA) is iterated to increase the diversity of the initial population. Finally, taking 5 groups of experiments as examples, the model is compared and solved by GAMS solver, genetic algorithm and genetic algorithm based on reinforcement learning. The results of an example show that the genetic algorithm based on reinforcement learning can solve the model and make the value of the objective function smaller, thus verifying the feasibility of the modified algorithm.

A MATHEMATICAL MODEL AND SOLUTION METHOD FOR THE BERTH ALLOCATION PROBLEM WITH VARIABLE HANDLING TIME AND CONTINUOUS TIME HORIZON

In this paper, we present the integration of two problems related to the operations in a port terminal: the Berth Allocation Problem (BAP) integrated with the Machine Assignment Problem. We present a mixed-integer linear programming (MILP) formulation, capable of assigning and scheduling incoming vessels to berthing positions and the assignment of machines for handling the vessels. The machines can be quay cranes, mobile cranes, straddle carriers, forklifts, trucks, and any other machine. The problem aims to minimize the waiting time plus the handling time of the vessels. To solve the problem, we developed a heuristic algorithm, capable of solving a problem instance in seconds. To compare the results, we generate several instance problems based on real data and solve them with our MILP formulation implemented in a solver, our heuristic, and a First In First Out (FIFO) algorithm. The solver was able to find solutions only in small-scale instances, and the heuristic was able to find good solutions for all instances.

Investigating the Effects of Introducing Automated Straddle Carriers in Port Operations with a System Dynamics Model

Port automation has been in the forefront of maritime innovation in the last decade. On that front, Automated Straddle Carriers (ASCs) are increasingly used to move containers efficiently. However, the introduction of ASCs in port operations can be disruptive if not handled properly, especially since the field can face many uncertainties such as increased container trade. The purpose of the paper is to investigate whether the introduction of Automated Straddle Carriers in port operations can improve the overall efficiency. To achieve the objective, a System Dynamics model was developed and tested under different scenarios. The results indicate that the introduction of ASCs is accompanied by an increase in productivity of the vehicles which results in more TEUs serviced. One of the most interesting results of the various scenarios is that for all rates of incoming TEUs, berth productivity is superior when operations are performed with 5 ASCs than with 10 manned vehicles. Finally, another issue that port authorities should always have in mind is the need for coordination among the various sub-processes and optimization of the necessary vehicles in order to avoid under-utilization of resources.

BIG DATA ANALYSIS FOR CONTAINER STAYING TIME PROBLEM OF CONTAINER STORAGE YARD

The increasing demand for goods transportation inherent the risks of the terminal congestion, consequently decreasing the operational efficiency. Maritime transportation containerization has forced container terminals to carefully evaluate how to manage their yard operations. Nowadays, with the Internet of Things (IoT) and the development of data technologies the complexity of data within and around the terminal has been increased, and a lack of data-driven approaches in this matter can still be identified. Using data mining techniques, some effects that are producing inefficiency might be discovered and fixed. This paper presents a data-mining approach to extract useful information from the terminal daily operations report. Additionally, it is shown the container's length of stay of a Straddle Carrier system container terminal.

Multi-agent Based Modeling of Container Terminal Operations

The existing research about multi-agent systems (MAS) in container terminal have not yet fully depicted the entire operations processes. These past works tend to use static (linear programming-based) modeling methods to model the systems. This method has a weakness because decision optimality is only guaranteed for the agents that are directly involved in the discussion. While the other agents whose performance are affected by the decision, but not directly taking part in discussion, are not considered in the decision-making process. The utilization of MAS-based dynamics negotiation method is also expected to cope lack of the sequential communication methods. Eight agents that depict entities in the terminal are ship, port captain, terminal manager, stevedore, quay crane, straddle carrier, customs, and truck. The agents interact in the processes of ship arrival sequencing, determination of ship’s service time and container picking. Based on the simulation result, agents’ behavior is able to depict the real system. The agents tend to maximize profit in the early period and gradually decrease it to attain common agreement. The velocity of utility function attainment is about 2-5%, but because of the descend monotonic function, negotiation agreement is guaranteed to be attained. Moreover, we provide the detail MAS-based dynamical model as well as the proof that the consensus will be attained by the entire agents.

Performance evaluation of compact automated parking systems with mobile application and customer service priority

We study a recently introduced smart compact automated parking system (CAPS), deploying straddle carriers, which require little footprint area. The straddle carriers can elevate and move a car over other cars in the horizontal direction in deep storage lanes, thereby considerably increasing the car storage capacity. Customers can reserve a parking space with a mobile application, or just drive in. Customers with booking by mobile application receive priority over the drive-in customers. We build priority queueing network models to evaluate the performance of a CAPS with priority booking under two storage policies. We then develop approximation methods to solve the analytical models and validate them through simulation. Numerical experiments show that the waiting time of a customer with booking can be reduced by 9.2% and 3.4%, respectively, for the dedicated and shared storage policies, compared with systems without priority booking. Then we compare the dedicated and shared storage policies and conduct a sensitivity analysis on the arrival rates of two types of cars. To minimise the expected retrieval time, the optimal ratio of width to height should be around 1.3. Finally, we calculate the investment cost of a CAPS and compare it with a competitive cylindrical automated parking tower.

Development of an Autonomous Light Control Algorithm with a Simulation Model of a Container Terminal

In the ongoing decade, port logistics in Europe have been exposed to intense competition. Especially container ports experience competition not only for ships but also for supply chains. To adapt to this trend, terminal operators are improving the efficiency in port logistics through, among other things, automation of port operations. Most of these efforts concern processes handling shipped goods. Instead of concentrating on these well-researched improvements, we explored the feasibility and economic potential of an intelligent lighting control system. Often automating handling processes need high investments in hard- and software. This smart lighting system is supposed to control demand-actuated outdoor lights in a harbour environment. In this research, movement profiles of straddle carriers and infrastructural data were gathered and refined to build a model of the observed container terminal. The modelled infrastructure includes the position of every lighting tower and its illuminant as well as their specific power consumption and the respective area, which those lights illume. To control these lights, we developed a control algorithm. We investigated through simulation experiments the power savings for this autonomous lighting control system. Multiple influential factors of this control concept were evaluated by varying the parameterization of the algorithm. A gathered data set, containing trajectories of operating straddle carriers during full terminal utilization, is the basis for the mentioned experiments. The results of varying the utilization rate of the terminal have identified a relationship between capacity and percental power consumption, in the form of a root function. The more straddle carriers run, the less the overall power consumption is increasing. Using the intelligent light control system on full utilization saves 14.67% of the total energy. In the case of 80 % terminal utilization, 18.22 % of total energy is subject for saving effects. Besides these results, we were able to explore the influences of the algorithm parameterization on the energy savings and could assess these solutions economically.

Big Data Utilization and Analysis to Improve Operational Performance of Hybrid Straddle Carrier in Container Terminal

Big Data Utilization and Analysis to Improve Operational Performance of Hybrid Straddle Carrier in Container Terminal

Straddle carrier routing at seaport container terminals in the presence of short term quay crane buffer areas

We address an optimization problem that arises at seaports where containers are transported between stacking areas and small buffer areas of restricted capacity that are located within the reach of quay cranes. The containers are transported by straddle carriers that have to be routed such that given unloading and loading sequences of the containers at the quay cranes are respected. The objective is to minimize the turnaround times of the vessels. We analyze the problem’s computational complexity, present an integer program, and propose a heuristic framework that is based on decomposing the problem into its routing component and a component that handles the time variables and buffer capacities. The framework is analyzed in computational tests that are based on real-world data. Based on these tests, we analyze the question of whether or not it pays off to deviate from the approach of permanently assigning a fixed number of straddle carriers to each quay crane, which is the strategy that is currently implemented at the port.

Automation of Handling Systems in the Container Terminals of Maritime Ports

Automation of handling systems in the container terminals of maritime ports has become one of the most important changes that happened in maritime transport since the first voyage of a container vessel in 1956. Nowadays, new automated terminals are being built in the world. Most of them are located in Europe, then in North America and the Far East. Automated guided vehicles, automated straddle carriers or automated stacking cranes have replaced handling devices that were manipulated and were controlled by port workers in the container terminals. The basic goal of the paper is to focus on the advantages of this progressive system that is based on the increase of the output of the container terminals, the reduction of downtimes or accidents during handling of containers.

Optimizing Container Loading With Autonomous Robots

In this paper, we investigate a problem associated with transferring a set of containers from the storage to the loading area of a warehouse using autonomous robots. In addition to assigning robots to containers, the special topology considered in this paper requires coordinated planning of the robots’ movement to avoid conflicts. We formulate the joint problem of robot assignment and movement coordination with the objective of minimizing the time required for all robots to carry their assigned containers to the destination, subject to conflict-free movement of all robots. We use the concept of abstract time windows (ATWs) to represent the movement of robots. The conditions for detecting conflicts in the ATW representation are introduced along with the necessary operations for resolving conflicts. For the solution of the problem, two approaches are developed. The first is a mathematical programming approach that formulates the problem as a mixed-integer linear program that allows optimal solution using appropriate solvers, while the second is a heuristic approach that allows fast, close-to-optimal solutions. Even though the proposed approaches focus on the case where the number of robots is equal to the number of containers, we also discuss how to solve the problem when having unequal number of robots and containers. Simulation results show that the heuristic approach provides a solution within 5% of the optimal solution with the minimum time of completion of all tasks being the performance metric, and executes six orders of magnitude faster than a state-of-the-art mathematical programming solver. Note to Practitioners —Robotic systems are increasingly used in logistics facilities, such as warehouses and container terminals. Nonetheless, achieving improved efficiency in this context poses several challenges in terms of real-time execution, coordinated resolution of robot conflicts, and adaptation to unpredictable events in operational environments. This paper examines a problem related to the coordination of a team of autonomous robots operating in a container handling facility, where containers need to be transferred from the storage area to a loading station. The considered topology involves containers arranged in lanes for efficient storage, but imposes limitations on the movement of autonomous robots. Such a topology is commonly found in container terminals where straddle carriers have to transfer containers from a storage yard to quay cranes or trucks and vice versa, but have limitations on moving on the same or even adjacent container lanes. To address this problem, we develop a novel method that efficiently assigns containers to robots and defines appropriate timings for collective coordination of robot movements. We illustrate that the proposed method provides very efficient results (within 5% from the optimal) for multiple configuration scenarios. Even more importantly, the method is suitable for real-time execution as it solves problems with hundreds of robots/containers within a few milliseconds; this further offers the ability for solution adaptation in dynamically varying environments, e.g., in situations of robot delays or breakdowns.

Multi-objective optimization of the integrated problem of location assignment and straddle carrier scheduling in maritime container terminal at import

Maritime terminals need more efficiency in their handling operations due to the phenomenal evolution of world container traffic, and to the increase of the container ship capacity. In this work, we propose a new integrated modeling considering the optimization of maritime container terminals using straddle carriers. The problem is considered at import. We study a combination between two known problems, the first is the storage location assignment problem, and the second is the straddle carrier scheduling problem. This approach, which combines between two chronologically successive problems, leads to the use of multi‐objective optimization. In fact, we study the multi‐objective integrated problem of location assignment and Straddle carrier Scheduling (IPLASS) in maritime container terminal at import. We prove that the problem is NP‐Complete. The objective is to minimize the operating cost which we evaluate according to eight components: the date of last task called makespan, the total vehicle operating time, the total storage bay occupation time, the number of vehicles used, the number of storage bays used, the number of storage locations used, and two different costs of storage location assignment. The location assignment costs are evaluated in order to facilitate the containers transfer for deliveries. We assume that the operating cost is a function of these components and that the influence of each component is variable and dependent on different parameters. These parameters are essentially: the number of quays in the terminal, the straddle carrier traffic layout, the number of container ships to serve in the terminal, the influence of concurrent operations in the terminal, the storage space configuration, the number of free storage bays, the number of free straddle carriers, the number of free quay cranes, the mobility of quay cranes, etc. To solve IPLASS efficiently, we propose an adapted multi‐objective Tabu Search algorithm. Lower‐bound evaluations are introduced to perform approximation of Pareto Front. To explore efficiently the non‐convex Pareto Front Region, we evaluate also a maximized distance adapted to the set of objectives. Indicators of efficiency are developed to propose distinguished solutions to operator. 2D‐projections of approximated Pareto Frontier are given to more understand the efficiency of proposed solutions.