Outbound Containers Research Articles

Battery-powered automated guided vehicles scheduling problem in automated container terminals for minimizing energy consumption

Battery-Powered Automated Guided Vehicles (B-AGVs) are important equipment used to transfer containers between the seaside and the landside in automated container terminals. Due to their limited battery capacity, the B-AGVs require battery recovery when their battery reaches the threshold. However, the processing capacity of electrical battery swapping stations is limited, it is crucial to avoid congestion in swapping station. Considering that the scheduling problem of B-AGV in the terminal is time intensive, this paper proposes a set partitioning method based on breadth-first and depth-first search to solve the established mixed integer programming model. Combined with the operational characteristics of the terminal, this paper adopts the flexible pooling strategy to effectively matches the inbound and outbound containers to form dual-cycle operation of B-AGVs, which can reduce the computational complexity and thus improving the solution efficiency. In addition, the record-to-record travel algorithm is applied to improve the quality of the solution. Finally, the effectiveness of the proposed model and solution method is evaluated through numerical experiments. Besides, the applicability of the flexible pooling strategy to different container terminals is studied, and the impact of the number of B-AGVs and the capacity of swapping stations on the energy consumption of B-AGVs is investigated, and some managerial insights are given accordingly.

Improving the stockpiling sites for inbound, outbound containers by timing the place of stacking

Improving the stockpiling sites for inbound, outbound containers by timing the place of stacking

Storage strategy of outbound containers with uncertain weight by data-driven hybrid genetic simulated annealing algorithm.

It is necessary to ensure the ship's stability in container ship stowage and loading and unloading containers. This work aims to reduce the container dumping operation at the midway port and improve the efficiency of ship transportation. Firstly, the constraint problem of the traditional container ship stacking is introduced to realize the multi-condition mathematical model of the container ship, container, and wharf. Secondly, a Hybrid Genetic and Simulated Annealing Algorithm (HGSAA) model is proposed for the container stacking and loading stacking in the yard. The specific container space allocation and multi-yard crane adjustment scheme are studied. Finally, the effectiveness of the multi-condition container ship stowage model is verified by numerical experiments by changing the number of outbound containers, storage strategies, storage yards, and bridges. The experimental results show that the HGSAA mode converges to 106.1min at the 751st iteration. Of these, the non-loading and unloading time of yard bridge 1 is 3.43min. The number of operating boxes is 25. The non-loading and unloading time of yard bridge 2 is 3.2min, and the operating box volume is 25 boxes. The objective function of the genetic algorithm converges when it iterates to generation 903 and 107.9min. Among them, the non-loading and unloading time of yard bridge 1 is 4.1min. The non-loading and unloading time of yard bridge 2 is 3.1min. Therefore, the proposed HGSAA has a faster convergence speed than the genetic algorithm and can obtain relatively good results. The proposed container stacking strategy can effectively solve the specific container allocation and multi-yard crane scheduling problems. The finding provides a reference for optimizing container scheduling and improving shipping transportation efficiency.

Container Terminal Yard Optimisation: A Case In Turkey

Due to the ever-changing nature of container terminals, fluctuations in storage capacity, and updates to vessel loading lists, the container yard can often become a hindrance to efficient terminal operations. One specific bottleneck frequently encountered in the stacking yard is referred to as yard clash. This phenomenon results in longer loading times for containers and is caused by the stacking of containers with the same loading time for different vessels within the same limited yard block and the limited availability of yard equipment. To address this issue, a binary integer optimization model was developed and implemented at a major container terminal in Turkey to minimize yard clashes. The results indicated a significant decrease of 92% in yard clashes during the loading of outbound containers, which in turn led to an increase of 2% in the total number of containers handled per vessel per hour.

Novel models and algorithms for location assignment for outbound containers in container terminals

In this paper, we investigate the stacking problem of outbound containers. It focuses on developing an optimal stacking strategy to allocate the arriving containers in a preassigned yard bay, with the aim of reducing extra rehandles during later retrieval. Some studies employ dynamic programming models to tackle the problem, which can offer exact solutions. Following this line of research, we propose an alternative dynamic programming model by redefining the bay representation and adjusting the formulations accordingly. The results show that the new model strikes a good balance between stacking quality and computational time. Considering that the dynamic programming model encounters difficulty in solving large-scale instances, we design two neural network algorithms that adopt a self-attention mechanism to enhance the fitting ability of the network. The neural network algorithms learn the decision patterns from the optimal results for small-scale instances obtained by dynamic programming and then are applied to solve large-scale instances. In addition, we propose a data-driven rollout algorithm by integrating the branching strategy and the aforementioned neural network algorithms to prevent the myopic drawback of using solely the neural network. To the best of our knowledge, this is the first paper that integrates the optimization method and machine learning techniques to tackle the container stacking problem. Finally, extensive computational experiments are performed to verify the effectiveness of the proposed models and algorithms.

The dynamic programming model for optimal allocation of laden shipping containers to Nigerian seaports

In highly competitive shipping market environment, container network operators-Freight forwarders, shipping companies etc. are concerned about design, development and deployment of optimized allocation model to achieve cost savings through improved container storage yard operations, crane productivity, outbound container allocation/distribution to seaport terminals and hence reduction in ships’ waiting times. In this paper, we developed two models, the Dynamic programming model and optimal allocation policy (model), for the optimal allocation of units of outbound laden cargo containers of sizes: 20ft and 40ft to six (6) major seaports in Nigeria. The distributions of the laden containers were allocated as follows: Port-Harcourt, Tincan Island, Onne, and Calabar seaports were allocated with 1,064 units of stuffed containers each. Apapa seaport was allocated with 2,128 units of laden containers, and zero allocation was made to Warri seaport. These results were arrived at through the implementation of the optimal allocation policy. The zero units allocation made to Warri seaport could be attributed to poor shipper patronage and hence the low frequency of ship visits. Apapa seaport was allocated double the number of containers moved to the remaining ports because it attracted more shipper patronage and hence more ship visits. Hence, freight forwarding companies will be assured of cargo spaces and make more profit by allocating more containers. Policy implications of the developed models were discussed.

Storage Space Allocation and Twin Automated Stacking Cranes Scheduling in Automated Container Terminals

To solve the problem of storage space allocation for both inbound and outbound containers and twin automated stacking cranes scheduling in the automated container terminals, a new repositioning strategy is designed and a cooperative optimization model is established. The model considers the constraints of safety distance and handshake area capacity. The objective of the model is to minimize the makespan of all storage requests and the total rehandling time during the retrieval process, which is calculated according to the priority of inbound and outbound containers defined by the early retrieval and early loading rule respectively. A variable neighborhood search based hybrid genetic algorithm is designed to solve the model. Numerical experiments show that the sum of the makespan of all storage requests and the total rehandling time during the retrieval process is reduced by approximately 10% compared to the traditional repositioning strategy by optimizing the sequence of container repositioning from the handshake area to the seaside or landside designated storage space. The research is beneficial to improve the operation efficiency of automated container terminal yards.

Mixed-Integer Nonlinear Programming for Energy-Efficient Container Handling: Formulation and Customized Genetic Algorithm

Energy consumption is expected to be reduced while maintaining high productivity for container handling. This paper investigates a new energy-efficient scheduling problem of automated container terminals, in which quay cranes (QCs) and lift automated guided vehicles (AGVs) cooperate to handle inbound and outbound containers. In our scheduling problem, operation times and task sequences are both to be determined. The underlying optimization problem is mixed-integer nonlinear programming (MINLP). To deal with its computational intractability, a customized and efficient genetic algorithm (GA) is developed to solve the studied MINLP problem, and lexicographic and weighted-sum strategies are further considered. An <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -constraint algorithm is also developed to analyze the Pareto frontiers. Comprehensive experiments are tested on a container handling benchmark system, and the results show the effectiveness of the proposed lexicographic GA, compared to results obtained with two commonly-used metaheuristics, a commercial MINLP solver, and two state-of-the-art methods.

Simultaneous Allocation and Scheduling of Quay Cranes, Yard Cranes, and Trucks in Dynamical Integrated Container Terminal Operations

We present a dynamical modeling of integrated (end-to-end) container terminal operations using finite state machine (FSM) framework where each state machine is represented by a discrete-event system (DES) formulation. The hybrid model incorporates the operations of quay cranes (QC), internal trucks (IT), and yard cranes (YC) and also the selection of storage positions in container yard (CY) and vessel bays. The QC and YC are connected by the IT in our models. As opposed to the commonly adapted modeling in container terminal operations, in which the entire information/inputs to the systems are known for a defined planning horizon, in this research we use real-time trucks, crane, and container storage operations information, which are always updated as the time evolves. The dynamical model shows that the predicted state variables closely follow the actual field data from a container terminal in Tanjung Priuk, Jakarta, Indonesia. Subsequently, using the integrated container terminal hybrid model, we proposed a model predictive algorithm (MPA) to obtain the near-optimal solution of the integrated terminal operations problem, namely the simultaneous allocation and scheduling of QC, IT, and YC, as well as selecting the storage location for the inbound and outbound containers in the CY and vessel. The numerical experiment based on the extensive Monte Carlo simulation and real dataset show that the MPA outperforms by 3-6% both of the policies currently implemented by the terminal operator and the state-of-the-art method from the current literature.

A Physical Internet (PI) based inland container transportation problem with selective non-containerized shipping requests

The imbalance of inbound and outbound containers generates large numbers of idle runs of container trucks in inland container transportation. In tradition, the trucking company acquires shipping requests from the spot market to increase profitability, with the precondition that both origin and destination locations of the acquired request are the same as the idle travel. Such precondition undoubtably leads to limited benefits and poor stability, given that shipping requests from the spot market are of high randomness. This paper proposes to adopt the idea of Physical Internet (PI) and assign an acquired request to several trucks, allowing transfers from one truck to another at logistics hubs. In this way, temporally and spatially dispersed idle resources could be consolidated to achieve the shipments of selective shipping requests, in a relay manner. To our knowledge, this paper is among the first to study the new paradigm of inland container transportation and formally model it as a PI-based selective vehicle routing problem (PI-SVRP). A mixed integer program (MIP) is presented and validated with CPLEX on small instances. In order to tackle real world instances, several novel heuristics have been designed particularly according to the special problem structure and embedded into an adaptive large neighborhood search (ALNS) framework. Finally, substantial instances are generated based on real data and a series of numerical studies are performed to derive managerial implications for practitioners.

Twin-crane scheduling during seaside workload peaks with a dedicated handshake area

To enable the efficient division of labor in container yards, many large ports apply twin cranes, two identical automated stacking cranes each dedicated to one of the transfer zones on the seaside and landside. The use of a handshake area, a bay of containers that separates the dedicated areas of the two cranes, is a simple means to avoid crane interference. Inbound containers arriving in the transfer zone of one crane and dedicated to a stacking position of the other crane’s area are placed intermediately in the handshake area by the first crane and then taken over by the second crane, and vice versa for outbound containers. Existing research only evaluates simple heuristics and rule-based approaches for the coordination of twin cranes interconnected by a handshake area. For this setting, accounting for precedence constraints due to stacking containers in the handshake area, we derive exact solution procedures under a makespan minimization objective. In this way, a comprehensive computational evaluation is enabled, which benchmarks heuristics with optimal solutions and additionally compares alternative crane settings (i.e., without workload sharing and cooperation with flexible handover). We further provide insights into where to position the handshake area. Our results reveal that when planning is too simple (i.e., with a rule-based approach), optimality gaps become large, but with sophisticated optimization, the price of a simplified crane coordination via a handshake area is low.

A methodological framework to allocate storage space for outbound containers

The staking of containers on ideal locations within the yard is a tactical decision that affects the productivity of container terminals. The goal is to improve posterior loading and retrieval operations, to get better use of terminal resources. In this paper, we study how to allocate storage space for outbound containers in container terminals. A two-phase methodological framework is proposed. The first phase groups outbound containers into clusters of similar operational loading conditions. Then in a second phase, a bi-objective storage space assignment model is solved to determine the set of block-bays where groups of similar containers will be stored during the planning horizon. This study presents a double contribution. On one hand, it proposes a new methodological framework that combines operations research and data mining techniques to solve a storage space assignment problem for outbound containers. On the other hand, it analyzes the impact of three factors on four performance metrics used to evaluate the quality and quantity of alternative solutions to the problem of allocation of storage space for outbound containers. The experimental framework is composed of an experimental design study to assess the impact of three factors on four performance metrics used to assess the quality of the storage space assignment solutions, and a case study to validate the proposed approach. The experimental results reveal that the storage yard's capacity and the number of clusters used to group the containers destined to a vessel are the main factors that affect the number and quality of alternative solutions.

A comparison forecast volume of outbound containers in case of The Bangkok port Between Exponential Smoothing and ARIMA Model

This research aims to compare the forecast of container consumption between Exponential Smoothing and ARIMA methods and make a forecast volume of outbound containers from Bangkok Port. Using time series data of the quantity of containers departing from Bangkok ports from January 2011 to December 2020 from Port authority of Thailand. By using the criterion of the root mean squared error (RMSE), it was found that the ARIMA forecasting method was more suitable for this set of time series data than the Exponential Smoothing method. The ARIMA forecasting method was root mean square error (RMSE) is 3310.72 and the exponential smoothing method was root mean square error (RMSE) is 3381.09 However, both methods could be used combined for forecasting to serve as an appropriate forecast model.

Inbound and outbound flow integration for cross-docking operations

We consider the optimization of the cross-docking operations at three INtermodal LOgistics Platforms (INLOPs) of a large European car manufacturer (ECM). The planning horizon is a week and the time bucket is a day. An inbound flow of products is gradually received over the week by truck from inland suppliers, and has to be loaded into containers which are then shipped to offshore production plants. The full content of a container must be available at the INLOP to enable its loading operations to start, hence temporary storage is needed. The objective is to minimize an inventory penalty, computed as the largest daily volume of temporary product storage observed over the planning horizon. The current practice at ECM is to first optimize the content of the inbound trucks and of the outbound containers independently, and then determine the loading day of each container to be shipped based on these fixed contents. We propose to integrate, within the same optimization framework, the decisions on both truck and container contents, which involve complex loading constraints related to the dimensions and weights of the products, with those on the scheduling of container loading. We model the resulting problem as a mixed integer linear program, and we develop a decomposition scheme for it, as well as a fix-and-optimize matheuristic. We perform extensive computational experiments on real instances provided by ECM. Results show that a combination of these two matheuristics is able to generate solutions that reduce the average inventory penalty by 40%.

Development of agile management approaches towards intermodal operations upon cargo flows imbalance

This paper deals with intermodal operations optimization methods to be implemented by the Block Train Operator upon cargo flows asymmetries at the hinterland. The algorithm of containerized cargo flows analysis and mathematical model were developed based on the relevant intermodal operation system. Inland leg of inbound containers from seaport to customer door arranged by truck is dominating within the emerging markets environment. Also imbalance in in- and outbound container flows as far as volumes, container size and payload is the case for largest inland destinations. Hence, the issue of rail-road transport prioritization and operational manageability is becoming of utmost importance. Centralizing those operations under a holistic service company – block train operator has been proven feasible. Last mile deliveries prioritization approach is offered to achieve the highest number of inbound containers processing with their further utilization for outbound export shipments. Mathematical modeling was conducted for distinct sets of operational scenarios that might take place. The scenario that allows the block train operator to achieve the highest revenue numbers and emptied inbound containers utilization for exports was selected. The number of truck heads, chassis and truck driver mitigation has become a secondary objective. The optimal scenario selected helps to reduce the overheads risk at the time of weekly cargo volumes fluctuations. The optimization approach represented can be applied to intermodal operations within markets where volume imbalance is rather possible

Economic Return Quantity Model for a Multi-type Empty Container Management with Possible Storage Constraint and Shared Cost of Shipping

Containerization has been said to be one of the most significant innovations of port management in the last century. Some ports usually tend to be more export driven while some others are import driven. This imbalance in the number of inbound and outbound containers necessitates movement of containers from locations or surplus to those of deficit. A port needs to cater for not only the level of containers required, but also the variety. Many factors affect the cost efficiency of container movement, and hence, that of port operations, and these need to be managed in an integrated manner to optimize cost in a port. We studied the management of a multi-type container system in a port where there can be savings from joint movement of containers, both for empty return and for procurement of replacement containers, and where there is limited storage capacity. We developed a model to determine the optimal integrated return and purchase cycle times and quantities. We illustrated the solution approach with a numerical example and performed sensitivity analysis. We believe this problem is rife and the model can guide the management of container return and replenishment in ports operations.

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.

The Outbound Containers’ Storage Location Assignment Problem for a Maritime Terminal

Seaway is important for supply chain management in order to be carried for manufactured products and logistics activities. However, this logistics process does not only consist of transportation. Storing transported goods temporarily or permanently in ports according to an appropriate plan is vital in order to whole process is carried on properly. This paper describes the storage location assignment problem for outbound containers in a maritime terminal and hybrid sequence stacking algorithm (HSSA) which is used to solve this problem. In this problem; yard bay consists of 6 horizontally and 4 vertically units long (6x4) totally 24 units of squares. 18 containers which are come in random weight and sequence are wanted to be placed into these slots optimal. Considering the fact that ships are loaded first with the heaviest containers and then with lighter containers; the presence of lightweight containers in the upper spaces necessitates a rehandle operation for the lightweight containers above to reach heavy containers. The main purpose of our method is to minimize the number of rehandle operations. For this purpose, according to the basic logic of HSSA algorithm; the heaviest containers are tried to be placed on the top and leftmost slots, on the other hand the lightest containers are tried to be placed on the bottom and rightmost slots as much as possible. However, the fact that incoming containers come at different weights in a different number and in a previously unknown sequence is the biggest obstacle to this method always achieving the best solution. At this point, although the method is a heuristic algorithm, it is important that it gives satisfactory results. In this study, a sample was solved by using HSSA and the results were presented in the study. These results show that the proposed algorithm is effective and efficient in solving the storage location assignment problem.

Using path optimum algorithm to solve slot selection problems for outbound containers of different attributes in container terminals

The outbound container slot selection problem (OCSSP) is a hot topic in the field of port research and of great significance to improving the operational efficiency of container yards. With the initial stacking state and extractive sequence of containers being known, the goal is to accomplish the task of extracting all containers in the container block with a minimum number of container relocations. OCSSP is an NP-hard problem. To obtain more practical research results, this paper proposes an improvement to the existing model and presents an improved version of the path optimum algorithm that considers container attributes. With the objective of improving the overall operational efficiency of container terminal yards, this study analyzed some practical issues related to container extraction by taking into consideration the influences of extraction priority levels, movements of obstruct containers, the Manhattan distances of gantry crane, and other factors on the model. Finally, the paper distills some scheduling strategies applicable to OCSSP in the hope of providing a theoretical basis and practical guidance to container port operators for the better allocation and optimization of coastal container ports resources.

Optimal strategies for the yard truck scheduling in container terminal with the consideration of container clusters

In container loading and unloading operations, container clusters are more suitable for large-scale operation and coupling scheduling of loading and unloading equipment. Taking a container cluster as a work unit, this study analyzes the multi-vessel large container clusters operation and researches the optimal scheduling strategy of the yard truck. A multi-objective mathematical programming model is developed through the pre-distribution of inbound container clusters and outbound container clusters, where the objectives are to minimize the non-loaded traveling distance and obtain the shortest completion time to finish the loading and unloading of containers in multiple vessels. The modeled problem is analyzed and solved by a heuristic-adaptive genetic algorithm. Fuzzy membership function and Pareto optimal solution are used to transform the multi-objective mathematical model into a single objective. The two methods are compared with results from the multi-objective programming problem of the model proposed. The result from the experimental case proved that the result of the Pareto optimal solution is better than that of the fuzzy membership function with regard to the problems of minimizing the distance of non-loaded traveling and finding the shortest completion time to finish the loading and unloading of containers in multiple vessels.