Stowage Planning Research Articles

Improving container terminal efficiency through emulation

Container terminals are struggling with a continuously increasing volume and increasing performance demands. As space is typically limited, terminals are searching for solutions to increase throughput capacity without expanding their physical footprint. Furthermore, they aim to increase their productivity on vessels in order to be able to handle bigger ships with larger call sizes in the same time frame. A terminal operating system (TOS) plays a major role in today's terminal operations, as it supports planning, scheduling and equipment control. Recently more and more tasks are performed by the TOS. These tasks need to be well-tuned to the operation (such as stowage planning, grounding decisions and equipment dispatching) in order to reduce cost and risk. In this paper, we present a meanwhile proven, safe and inexpensive approach to test and tweak the TOS and train operators on an emulated virtual terminal. This novel approach in the field of container terminals has been successfully applied during the previous 3 years at over 15 container terminals.

A Constraint Programming model for fast optimal stowage of container vessel bays

Container vessel stowage planning is a hard combinatorial optimization problem with both high economic and environmental impact. We have developed an approach that often is able to generate near-optimal plans for large container vessels within a few minutes. It decomposes the problem into a master planning phase that distributes the containers to bay sections and a slot planning phase that assigns containers of each bay section to slots. In this paper, we focus on the slot planning phase of this approach and present a Constraint Programming and Integer Programming model for stowing a set of containers in a single bay section. This so-called slot planning problem is NP-hard and often involves stowing several hundred containers. Using state-of-the-art constraint solvers and modeling techniques, however, we were able to solve 90% of 236 real instances from our industrial collaborator to optimality within 1second. Thus, somewhat to our surprise, it is possible to solve most of these problems optimally within the time required for practical application.

TOPS: Advanced Decision Support System for Port and Maritime Chemical Logistics

This paper presents a decision support system named the Tanker Operations Planning System (TOPS). It is a user-friendly software with an advanced routing and scheduling algorithm to automate and aid the operational decision making process. It considers the key operations constraints faced by the tanker owners. The advanced routing and scheduling algorithm embedded in the decision engine uses heuristics to solve industrial scale problems under actual operating conditions. Besides, TOPS can easily generate the routing, scheduling, stowage plan and the financial reports, and it can process a large number of orders online as well. In addition, TOPS can be conveniently modified for evaluation purposes or to suit the preference of the tanker owners. In essence, TOPS is an effective optimization-based decision support system to assist the parcel tanker carriers to systematically and objectively plan vessel routes and schedules with the goal of maximizing profits and fleet utilization in a structured manner. Drawing from actual data provided by a tanker company operating in the Asia Pacific, our simulation results show that TOPS can generate a cost effective routing and scheduling plan of a large scale problem to within a practically acceptable time of around 10–20 minutes.

Improving Safety and Stability of Large Containerships in Automated Stowage Planning

Stowage planning is an important task in the container transportation business. The quality of stowage plans generated for ships on a multiport voyage greatly affects a shipping line's operating cost which also directly affects the competitiveness of supply-chain companies that rely on it. This paper describes ongoing research and development of an automated system for generating quality stowage plans for large containerships. The system consists of three modules: stowage plan generator, stability module and optimization engine. The focus of the study is on the stability module, which automatically generates feasible stowage plans that conform to safety and stability requirements for large-scale vessels. The module can greatly reduce the amount of ballast required by the ship and lower the cost of shipping. The proposed system is tested on a real-world large containership with more than 7000 TEUs on a multiport voyage. Experimental results show that the proposed system is able to efficiently generate feasible stowage plans using only a fraction of the time required by human planners (<;2 min per plan versus 2 hours per plan). The quality of the generated plans is also comparable to human planners in terms of the number of rehandles, crane intensity, safety and stability, all of which are crucial to shipping operations.

Optimization of stowage plans for RoRo ships

International vehicle transportation is primarily conducted using Roll-on/Roll-off (RoRo) ships, which are specialized to transport cargo on wheels such as cars, farming equipment, and military equipment. RoRo ships operate by going between different regions of the world according to predefined plans. In this work we focus on operational decisions that are required when operating a fleet of RoRo ships: given a ship set to travel according to a given route, we consider decisions such as which cargoes to carry, how many vehicles to carry from each cargo, and how to stow the vehicles carried during the voyage. A mathematical model is made describing the problem, and both a standard MIP solver and a specially designed heuristic method are used to solve the problem. Computational tests are conducted to analyze the difficulty of solving several variations of the problem. For certain types of instances the MIP solver works well, while for other types the heuristic is very fast and more accurate than the MIP solver.

RFID 와 웹서비스를 이용한 선박과 항구 간의 선적정보 교환 프레임워크

Efficient stowage plan is required for handling large number of containers in a container terminal. Before a ship enters port, most of container terminals prepare a stowage plan based on information received from the shipping company. If this information does not have the accurate information of containers in the vessel, the stowage plan might be often changed or sometimes it becomes useless. In order to avoid this problem, we apply RFID system in the container ship and we propose a framework for communication between ships and terminals using RFID and Web Services technology.

A decision support system using soft computing for modern international container transportation services

A software system has been developed for modern maritime container transportation. This system contains six modules: the demand forecasting, the stowage planning, the shipping line optimization, the slot pricing and allocation, the container distribution, and the contribution analysis. The first three modules will be presented in this paper. The system constructs problem models and uses exponential smoothing, regression analysis, neural network, linear programming, genetic algorithm and sequence alignment methods to solve relevant issues. The reliability and practicality of the software and the algorithms included are confirmed by practical applications or analyzing their estimation accuracy. The system has been tested with the actual transportation and proves to be an effective decision-making tool in maritime transport coordination. The proposed system may help companies significantly increase profit.

A new three-step heuristic for the Master Bay Plan Problem

In this work, we are looking at the problem of determining stowage plans for containerships. This problem, denoted in the literature as the Master Bay Plan Problem (MBPP), is computationally difficult to solve, that is NP-hard. We start from the optimal solution of subsets of bays related to independent portions of the ship, which are determined by a previously proposed decomposition approach for the MBPP; then, we look for the global ship stability of the overall stowage plan by using a tabu search (TS) meta-heuristic approach. Note that at the same time the proposed TS algorithm allows us to further reduce the handling time of the containers to be loaded on the ship. The proposed heuristics has been implemented within a software support system that helps the planning management in the visualisation of the stowage plans of each bay of the ship. Preliminary computational experimentations performed on some real-life test cases related to a terminal located at the port of Genoa, Italy are provided.

Review of containership stowage plans for full routes

Containership stowage plans are a pivotal teaches in the system of container transportation. With the increasing containers shipping, planning containership stowage has become more and more complicated. So intelligent stowage planning for containerships is of great significance. An effective stowage plan may improve efficiency of transportation system. First, the progress of containership stowage plan at home and abroad is reviewed, including the latest developments, such as the application of various optimization methods and computer techniques to the problem. Then, the complexities of the problem are discussed and areas where investigations are still needed are pointed out. This will provide a reference for further research on the subject.

Simulation Studies of Heuristic Approaches for Dynamic Scheduling of Container Terminal Operations

The growth of marine containers transportation along with increasing complex container terminal logistic system (CTLS) has called for more effective approaches for scheduling container terminal resources. This paper discusses the problem of scheduling quay crane, yard crane and container truck for improving the container terminal productivity. One dynamic scheduling method and specific heuristic scheduling rules for dynamically assigning quay crane, yard crane and container trucks are proposed according to stowage plans and storage plans. A simulation model of a container terminal in Shanghai is built by the application of WITNESS2002, a simulation software package, to evaluate the efficiency of the dynamic scheduling method. From simulation results, the production efficiency increased about 8% by the dynamic scheduling method than static one previously discussed in CTLS.

An Investigation into Effectiveness of Container Arrangements on Optimizing a Containership Stowage Plan

An Investigation into Effectiveness of Container Arrangements on Optimizing a Containership Stowage Plan

Path finding with stowage planning consideration in a mixed pickup–delivery and specified-node network

This paper considers the mixed pickup–delivery shortest path problem with specified nodes and stowage planning (mixed-PDSPSNSP), which combines path finding and three-dimensional stowage planning. The mixed-PDSPSNSP appears in many operational problems in various industries. However, to the best of our knowledge, there is no study in the literature for such a problem. The mixed-PDSPSNSP is NP-hard. Our proposed solution algorithm is shown to be effective and efficient when applied to a real-life mixed-PDSPSNSP.

A 3D-BPP approach for optimising stowage plans and terminal productivity

This paper addresses the problem of determining stowage plans for containers in a ship, that is the so-called master bay plan problem (MBPP). MBPP is NP-complete [Botter, R.C., Brinati, M.A., 1992. Stowage container planning: A model for getting an optimal solution. IFIP Transactions B (Applications in Technology) B-5, 217–229; Avriel, M., Penn, M., Shpirer, N., 2000. Container ship stowage problem: Complexity and connection to the colouring of circle graphs. Discrete Applied Mathematics 103, 271–279]. We present a heuristic method for solving MBPP based on its relation with the three-dimensional bin packing problem (3D-BPP), where items are containers and the only bin is the ship. We look for stowage plans that take into a proper account structural and operational constraints, related to both the containers and the ship, and maximise some important terminal performance indexes, such as the effective and mean net crane productivity. Our aim is to evaluate how stowage plans can influence the performance of the quay. A validation of the proposed approach with some test cases related to containership docks at the port of Genoa (Italy) is given. The results of real instances of the problem and the comparison with a validated heuristic for MBPP, show the effectiveness of the proposed approach in producing stowage plans that minimise the total loading time and allow an efficient use of the quay equipment.

A decomposition heuristics for the container ship stowage problem

In this paper we face the problem of stowing a containership, referred to as the Master Bay Plan Problem (MBPP); this problem is difficult to solve due to its combinatorial nature and the constraints related to both the ship and the containers. We present a decomposition approach that allows us to assign a priori the bays of a containership to the set of containers to be loaded according to their final destination, such that different portions of the ship are independently considered for the stowage. Then, we find the optimal solution of each subset of bays by using a 0/1 Linear Programming model. Finally, we check the global ship stability of the overall stowage plan and look for its feasibility by using an exchange algorithm which is based on local search techniques. The validation of the proposed approach is performed with some real life test cases.

A Case-based Stowage Planning System

The computer support in stowage planning today is limited to the calculation of the state of the ship, e.g. concerning the ship’s stability and strength or hazardous cargoes check. Stowage planners have to perform the core of the planning process manually. The growing size of container ships and the tight shipping schedule illustrate the increasing burden to the planners and its risk. A new method for semi-automating the process of stowage planning, called Casestow, is proposed. Casestow encompasses the difficulty of knowledge acquisition of stowage planning. It assists the planner in creating a stowage plan by remembering how a similar planning problem was solved.

Impact of Yard Organisation on the Master Bay Planning Problem

In this paper we analyse the impact of the yard organisation on container stowing operations. We deal with the Master Bay Plan problem (MBPP), that consists in stowing containers of different types into available locations on a containership; the aim is to minimise the berthing time in such a way that structural and operational constraints, related to both the containers and the ship, are satisfied. In particular, we study how the total stowage time changes due to possible reloading operations, when different picking sequences are considered. We use a binary linear programming model for MBPP that has been recently proposed in literature considering two main scenarios. First, we assume to have all containers ready to be loaded on board in the quay independently of their stack position in the yard. In this case, we solve MBPP as it is having as objective function just the minimisation of the loading time. Then, we take into account the yard constraints following the directions of the planning office, which makes the bay plans according to the stocking area requirements and the picking list for the containers to be loaded. In the third case, we present a procedure that enables us to consider different lot arrivals and opportunely relax some constraints. Moreover, we assume that the containers are stored in the yard into different stacks on the basis of their size, destination and weight, depending on the storage strategy chosen by the yard managers. We evaluate alternative yard storage strategies with real size stowage plans of a containership located at a maritime terminal in Genoa. The results show that, when we look for the berthing time minimisation, it is quite important to think about the optimisation of the flow of containers from the yard to the quay.

The master bay plan problem: a solution method based on its connection to the three-dimensional bin packing problem

This paper addresses the problem of determining stowage plans for containers in a ship, referred to as the Master Bay Plan Problem (MBPP). ; MBPP is NP‐complete. We present a heuristic method for solving MBPP based on its relation with the three‐dimensional bin packing problem (3D‐BPP), where items are containers and bins are different portions of the ship. Our aim is to find stowage plans, taking into account structural and operational constraints related to both the containers and the ship, that minimize the time required for loading all containers on board. ; A validation of the proposed approach with some test cases is given. The results of real instances of the problem involving more than 1400 containers show the effectiveness of the proposed approach for large scale applications.

A Genetic Algorithm with a Compact Solution Encoding for the Container Ship Stowage Problem

The purpose of this study is to develop an efficient heuristic for solving the stowage problem. Containers on board a container ship are stacked one on top of the other in columns, and can only be unloaded from the top of the column. A key objective of stowage planning is to minimize the number of container movements. A genetic algorithm technique is used for solving the problem. A compact and efficient encoding of solutions is developed, which reduces significantly the search space. The efficiency of the suggested encoding is demonstrated through an extensive set of simulation runs and its flexibility is demonstrated by successful incorporation of ship stability constraints.

A hybrid genetic algorithm for the container loading problem

This paper presents a hybrid genetic algorithm (GA) for the container loading problem with boxes of different sizes and a single container for loading. Generated stowage plans include several vertical layers each containing several boxes. Within the procedure, stowage plans are represented by complex data structures closely related to the problem. To generate offspring, specific genetic operators are used that are based on an integrated greedy heuristic. The process takes several practical constraints into account. Extensive test calculations including procedures from other authors vouch for the good performance of the GA, above all for problems with strongly heterogeneous boxes.

Container stowage planning: a methodology for generating computerised solutions

The container stowage problem concerns the suitable placement of containers in a container-ship on a multi-port journey; it requires consideration of the consequences each placement has on decisions at subsequent ports. A methodology for the automatic generation of computerised solutions to the container stowage problem is shown; objective functions that provide a basis for evaluating solutions are given in addition to the underlying structures and relationships that embody this problem. The methodology progressively refines the placement of containers within the cargo-space of a container ship until each container is specifically allocated to a stowage location. The methodology embodies a two stage process to computerised planning, that of a generalised placement strategy and a specialised placement procedure. Heuristic rules are built into objective functions for each stage that enable the combinatorial tree to be explored in an intelligent way, resulting in good, if not optimal, solutions for the problem in a reasonable processing time.