Crane Deployment Research Articles

An accelerated Benders decomposition algorithm for the integrated storage space assignment, berth allocation, and yard crane deployment problem

In this paper, storage space assignment problem, berth allocation problem, and yard crane deployment problem are integrated with due attention to traffic congestion in the passing lines of storage yards. The paper provides a mixed-integer programming model for the integrated problem with the objective of minimizing the movement and operating costs of yard cranes, the cost of moving container flow between the storage yard and berths, and the delay cost of vessels. The planning horizon is considered to be one day and the berth layout is assumed to be discrete. Since the integrated storage space assignment and yard crane deployment problem is NP-hard and so is berth allocation problem, the integration of these three problems will be at least equally complex. Thus, an Accelerated Benders Decomposition Algorithm is also developed for solving the integrated problem. This is done by introducing a combinatorial Benders cut to the classical Benders decomposition algorithm to improve its performance and also adding 8 valid inequalities derived from the problem properties and assumptions to the algorithm formulation to accelerate the solution process. The proposed mathematical model and solution approach are evaluated by generating instances of different sizes for the problem and assessing the solution results. The results show the good performance of the proposed accelerated Benders decomposition algorithm. Taking the integrated approach to the modeling of the three problems rather than the non-integrated approach improved the cost of terminal operations by 11.15% on average.

Integrated optimization of container allocation and yard cranes dispatched under delayed transshipment

Due to variations in carrying capacity and visiting periods of vessels calling at terminals, certain transshipped containers may not be able to be transported immediately after arrival. Instead, they must be temporarily stored in the yard and scheduled for later transportation. Increased container storage time in the yard as a consequence of transit delays not only incurs additional costs for shipping companies, but also introduces new issues for terminal yard management. Therefore, this study investigates the daily storage yard management challenges that arise in marine container terminals that integrate space allocation and yard crane deployment decisions while considering the delayed transshipment of containers. A mixed-integer linear programming model is proposed to describe the problem, whose objectives are to minimize the transportation costs for container loading and discharge operations, penalty costs associated with the delayed transshipment of containers, transportation costs associated with containers not transshipped within the planning horizon, and yard crane inter-block movement costs. The Benders decomposition algorithm is applied to address this problem, and the traditional decomposition approach is improved by adding characteristics such as the development of strong optimality cuts and the generation of a powerful initial solution to the master problem. Extensive experiments are performed to validate the effectiveness of the model and efficiency of the proposed improved Benders decomposition algorithm.

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.

Optimization for Green Container Shipping: A Review and Future Research Directions

Maritime freight transportation is one of the least emissions-producing transportation alternatives in terms of transported tonnage per distance. However, it produces a high amount of emissions as around 80% of international freight transportation is conducted through seas and 20% of maritime transportation is conducted through container shipping. This makes it crucial to reduce emissions in container shipping. In this regard, this study reviewed previous studies on the environmental optimization of container shipping and identified various future research directions. The results showed that in the sea segment of environmental optimization of container shipping, decisions which require further attention include resource allocation, emission reduction technology choice, disruption recovery, freight rate optimization, and shipment scheduling. The decisions that require future research in the port segment are related to internal transportation and handing operations in container terminals (i.e., yard crane deployment, yard truck deployment, yard truck scheduling, yard container stack allocation, yard container retrieval), renewable energy source installation, and emission reduction technology choice. Vessel scheduling and speed optimization decisions are the most frequently studied decisions in the sea segment, but they are rarely considered for inland shipping of containers. In the sea-port combined segment of container shipping, future studies are required in quay crane scheduling, vessel scheduling, container route allocation, ship route allocation vessel deployment, and emission reduction technology choice. The least studied decision in the door-to-door segment of container shipping includes hub location-allocation, empty container relocation, ship route allocation, vessel deployment, environmental taxation and subsidy scheme, emissions reduction technology choice, and speed optimization. It was also demonstrated that modeling of future studies should more frequently consider uncertainties and social sustainability parameters.

Collaborative Optimization of Yard Crane Deployment and Inbound Truck Arrivals with Vessel-Dependent Time Windows

Due to mega-ships, increasing container throughput, and nonuniform truck arrivals, many container terminals face challenges of unbalanced workloads of yard equipment, shortage of equipment resources in peak hours, and congestion problem. To solve such issues, we propose a mixed-integer bilevel programming model to optimize the vessel-dependent time windows for inbound trucks and yard crane deployment simultaneously. In the proposed bilevel model, the upper level aims to minimize the total truck waiting time at the container terminal gate and yard, while the lower level is formulated to minimize the total workload overflow to next shift in the whole container yard. The optimal yard crane deployment obtained in the lower level will transfer to the upper level problem to determine the waiting time of trucks in the yard and then affect the truck arrivals pattern. To solve the model, a hybrid algorithm—called hybrid genetic algorithm, based on collective decision optimization—is put forward by combining the genetic algorithm and the collective decision optimization algorithm. Numerical experiments are conducted to validate the proposed approach is effective to simultaneously flatten truck arrivals and improve the efficiency of yard cranes. The proposed approach can significantly reduce container terminals’ truck waiting time.

Joint optimization of space allocation and yard crane deployment in container terminal under uncertain demand

The fluctuation of container transportation volume caused by trade policy, epidemic disease and other factors has a significant impact on the terminal yard management. Especially, the decision of yard space problem and yard crane deployment problem is depending on the arriving container volume. Due to the limited space and mechanical resources of the terminal yard, the uncertain container demand will lead to the waste or shortage of yard resources. In order to improve the efficiency of yard management, this paper studies the integrated optimization of yard resources considering the impact of uncertain container demand, and a stochastic optimization model is established to improve the robustness of the daily yard management. Due to the complexity of the model, a harmony search algorithm based on two-stage decomposition strategy is designed to solve the proposed model. To accelerate the solving speed, a scoring method is developed to generate the initial space allocation plan, and a Tabu list is added to improve the solution quality. The numerical experiments are conducted to validate effectiveness and efficiency of the method.

A cluster-based yard template design integrated with yard crane deployment using a placement heuristic

The increasing demand for container transportation calls for higher terminal storage capacity and greater handling efficiency. A good yard template design will facilitate both space and yard crane utilization. However, most of the existing research designs the template based on the parallel layout and caters only to destination vessels. This study proposes a cluster-based yard template design method that reserves storage space based on the container destination and origin, which helps avoid yard crane conflict and is therefore applicable to various yard designs. A mixed integer programming model is proposed to assign clusters considering yard crane deployment, with the objective to ensure the yard space is used intensively most of the time. To solve this problem in large-scale cases, three heuristics are developed based on the best-fit method in solving the bin packing problem. Numerical experiments under different yard layouts are conducted to validate the efficiency and effectiveness of the model and algorithms.

The effects of evolving port technology and process optimisation on operational performance: The case study of an Australian container terminal operator

Grounded on the Resource Based View (RBV) theory, this paper argues that technological advances and process optimisation through standardisation and centralisation could been used to improve various aspects of port operational performance. Using information and secondary data from the case of Patrick Terminals in Australia, the impacts of the Patrick's Brisbane AutoStrad™ container terminal and National Operations Centre (NOC) on yard design and operations, quay crane deployment, operational productivity, safety, manpower, vessel planning, customer service delivery, invoicing, and process alignment and standardisation, were examined. It was found that the deployment of advanced technology i.e. Brisbane AutoStrad™, bundled with the optimisation of business processes through standardisation and centralisation at the NOC, are valuable and costly-to-imitate tangible and intangible resources which can translate into positive operational performance in terms of better equipment and facility utilisation, higher productivity, reduced costs, and more satisfied customers. This research contributes to strengthen the RBV theory in the context of container terminals management while also provides managerial insights to port managers and policy makers not only in Australian container terminals but also those in Asia, given their growth and competitive position on the global basis.

Modelling the problem of yard crane deployment for resource-limited container terminal

With the bottleneck of container port has moved from the quay side to the yard area, the operational efficiency of yard crane is becoming increasingly significant to the container terminal. This article studies on the modelling of yard crane (YC) deployment for resource-limited container terminal. Firstly, the models for the basic yard crane deployment are proposed, which include the basic network flow model and the basic mathematical model. Secondly, the extended models are formulated, which can be utilised to deal with the deployment of multi-type yard cranes. Finally, numerical experiments are conducted to verify the effectiveness of the proposed models, e.g., functional test, performance analysis and scenario analysis.

Modelling the problem of yard crane deployment for resource-limited container terminal

Modelling the problem of yard crane deployment for resource-limited container terminal

Collaborative optimization of container allocation and yard crane deployment based on truck appointment system

Due to the increase of container transport volume and the limitation of yard resources, yard congestions in container terminal have become increasingly serious. Therefore, it is necessary to optimize the problem of container allocation and yard crane deployment for import containers. With the extensive application of truck appointment system, yard resources allocation could be performed based on the appointment application submitted by shippers or trucking companies. Taking the information of truck appointment system and arrival period of vessels into account, a two-stage programming model is proposed. The container allocation problem is solved to balance the workload among different blocks in first stage, while the second stage aims to minimize the total shifting distance of yard cranes. Then, solver CPLEX is used to provide optimal solution for numerical experiments. It is concluded from experiments that the proposed mode is efficient in reducing the yard congestion by balancing the workload among blocks and decreasing the shifting distance of yard cranes, thus improving both the operational efficiency of yard and the accuracy of resources allocation.

Joint Deployment of Quay Cranes and Yard Cranes in Container Terminals at a Tactical Level

This paper discusses tactical joint quay crane (QC) and yard crane (YC) deployment in container terminals. The deployments of QCs and YCs are critical for the efficiency of container terminals. Although they are closely intertwined, the deployments of QCs and YCs are usually sequential. This paper proposes a mixed-integer programming model for the joint deployment of QCs and YCs in container terminals. The objective of the model is to minimize the weighted vessel turnaround time and the weighted delayed workload for external truck service in yard blocks, both of great importance for a container terminal but rarely considered together in the literature. This paper proves that the studied problem is NP-hard in the strong sense. Case studies demonstrate that the proposed model can obtain better solutions than the sequential method. This paper also investigates the most effective combinations of QCs and YCs for a container terminal at various demand levels.

Storage yard management based on flexible yard template in container terminal

With the bottleneck of port operation moving from the quay side to the yard area, storage yard management is becoming increasingly important in the container terminal. This paper studies on storage yard management in container terminal, a flexible yard template strategy is proposed instead of the fixed yard template strategy. Based on the strategy, an integrated optimization model simultaneously considering space allocation and yard crane deployment for the tactical storage yard management is formulated. Besides, Numerical experiments are conduced to verify the effectiveness of the proposed strategy and mathematical model.

A branch-and-price method for integrated yard crane deployment and container allocation in transshipment yards

With the trend towards mega-vessels and shipping alliance, the importance of transshipment activities keeps increasing. In transshipment yards, a “yard template” is often used to stack containers in dedicated areas (sub-blocks) pre-reserved for their own destination vessels. At short-term planning level, the yard template is given, but the containers going to a specific vessel still have high flexibility to be allocated among many pre-reserved sub-blocks. The amount of containers allocated to each sub-block, i.e. “container allocation”, not only affects the traffic congestion, but more importantly determines the number of yard cranes (YCs) required in each block. The limited YCs have to switch blocks to fit the needs of container allocation in different periods, i.e. “YC deployment”. This study integrated these two closely related problems and formulated a MIP model. Since the model has a nice block-diagonal structure, column generation under Dentzig Wolfe decomposition was proposed to get lower bounds. A novel branch-and-price (B&P) method was proposed to find near-optimal solutions. To reduce the searching tree size, our B&P method branched on YC paths during the planning horizon, instead of branching on decision variables directly. Numerical experiments under both small and large scale problems showed that our B&P method could efficiently solve the integrated planning problem. The results also showed that YC movements could be reduced effectively without sacrificing operational efficiency or using more yard cranes.

Storage Yard Management in Maritime Container Terminals

This paper studies the daily storage yard manage problem arising in maritime container terminals, which integrates the space allocation and yard crane deployment decisions together with the consideration of container traffic congestion in the storage yard. The integrated problem is formulated as an integer linear programming model with the objective of minimizing the yard crane operating cost and the yard crane interblock movement cost. A divide-and-conquer solution approach is designed to solve the problem in an efficient manner in which harmony search and constraint satisfaction techniques are employed. Numerical experiments are conducted to validate the performance of the solution approach and the improvement from the integrated optimization method.

4D CAD-based evaluation system for crane deployment plans in construction of nuclear power plants

Abstract To optimize a crane deployment plan is an important problem to reduce construction cost. Previous researches focused on developing methods to find an optimal crane deployment plan automatically, which minimizes total operating time under physical constraints such as load capacity. These researches did not focus on evaluating transitions of crane utilization rates during construction period. Unlike a general tall building, construction schedules of a nuclear power plant (NPP) tend not to take the form of a series of construction cycles in a typical floor. Therefore, transitions of crane utilization rates need to be evaluated in construction of a NPP. The purpose of this paper is to develop a 4D CAD-based evaluation system, which calculates transitions of utilization rates automatically, for planning of crane-deployment in construction of a NPP. To calculate utilization rates automatically and accurately, two functions were developed. The first function maps data between 3D objects and carry-in tasks by using a fast geometric interference checking method based on “Axis Aligned Bounding Box.” The second one estimates installation material quantities in early construction-planning phases by using a data mining technique called “random forests.” The result of verification tests based on previous construction data indicates that the proposed system will significantly reduce the time required to create crane-deployment plans and improve their accuracies.

Integrated Yard Space Allocation and Yard Crane Deployment Problem in Resource-Limited Container Terminals

Yard storage space and yard crane equipment are the core resources in the container terminal yard area. This paper studies the integrated yard space allocation (outbound container space) and yard crane deployment problem in resource-limited container terminals where yard space and yard cranes are extremely scarce. Two corresponding counterstrategies are introduced, respectively, and the integrated problem is solved as mixed integer programming. The model this paper formulated considers the container volume fluctuation of the service line, and the objective is a trade-off between yard sharing space and terminal operation cost. In numerical experiments, this paper tries to reveal the management meaning in practical operation of container terminal and provides decision support for terminal managers; therefore a series of scenarios are presented to analyze the relations among the yard sharing space, the number of yard cranes, the size of yard subblock, and the cost of terminal operation.

Deployment Optimization of Rail Gantry Cranes in Railway Container Center Stations

Rail gantry cranes are the core resources of railway container center stations which affect the operating efficiency and costs of stations. In this paper, a rail gantry crane deployment model is proposed which considers the operation time as well as balanced utilization of equipment. Further, this multi-objective function is transformed to a single objective model with a weight coefficient method. Finally, this paper puts forword optimization ideas for the deployment of core resources in a railway container center station.

A container yard storage strategy for improving land utilization and operation efficiency in a transshipment hub port

This paper studies the storage yard management problem in a busy transshipment hub, where intense loading and unloading activities have to be considered at the same time. The need to handle huge volumes of container traffic and the scarcity of land in the container port area pose serious challenges for the port operator to provide efficient services. A consignment strategy with a static yard template has been used to reduce the level of reshuffles in the yard, but it sacrifices on land utilization because of exclusive storage space reservation. Two space-sharing approaches are proposed to improve on the land utilization through dynamic reservation of storage space for different vessels during different shifts. Meanwhile, workload assignment among reserved spaces will also satisfy the high-low workload balancing protocol to reduce traffic congestion in the yard. A framework which integrates space reservation and workload assignment is proposed. Experimental results show that the framework is able to provide solutions for containers handling within much less storage space, while guarantee the least yard crane deployment.

Evaluation of operational plans in container terminal yards using Discrete-Event Simulation

As the world container volumes are soaring, container terminals form a very important chain in the global transportation network. It is a generally accepted view that the modelling of the dynamic and stochastic nature of the container terminal systems using analytical or closed form models is a complicated process and the use of such models is very limited. Hence, this paper presents a simulation model for complete terminal operations which is developed and implemented with the view to validate/evaluate the operational results obtained from a new optimization model of the combined container assignment and yard crane deployment problem. The various aspects of the container terminal operations are modelled using discrete-event simulation. Computational results from the simulation model are reported for a UK container terminal using operational data.