An integrated simulation-optimization framework for the operational planning of seaport container terminals

In this article the operational planning of seaport container terminals is considered by defining a suitable integrated framework in which simulation and optimization interact. The proposed tool is a simulation environment (implemented by using the Arena software) representing the dynamics of a container terminal. When the system faces some particular conditions (critical events), an optimization procedure integrated in the simulation tool is called. This means that the simulation is paused, an optimization problem is solved and the relative solution is an input for the simulation environment where some system parameters are modified (generally, the handling rates of some resources are changed). For this reason, in the present article we consider two modelling and planning levels about container terminals. The simulation framework, based on an appropriate discrete-event model, represents the dynamic behaviour of the terminal, thus it needs to be quite detailed and it is used as an operational planning tool. On the other hand, the optimization approach is devised in order to define some system parameters such as the resource handling rates; in this sense, it can be considered as a tool for tactical planning. The optimization procedure is based on an aggregate representation of the terminal where the dynamics is modelled by means of discrete-time equations.     

Optimising container storage processes at multimodal terminals

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

Integrated scheduling of handling equipment at automated container terminals

To improve the productivities of an automated container terminal, it is important to schedule different types of handling equipment in an integrated way. A mixed-integer programming model, which considers various constraints related to the integrated operations between different types of handling equipment, is formulated. A heuristic algorithm, called multi-layer genetic algorithm (MLGA) is developed with a view to overcome the computation difficulty for solving the mathematical model. A numerical experimentation is carried out in order to evaluate the performance of the algorithm.     

Scheduling crossover cranes at container terminals during seaside peak times

This paper deals with crane scheduling in a block of a container terminal equipped with two crossover cranes. We concentrate on the situation when all requests in this block refer to the seaside. This seaside peak time occurs when a container ship is berthed and has to be served as quickly as possible because berthing time is a major cost driver for both, the shipping company and the port operator. In this case, the two cranes highly interfere. We formulate this problem as a MIP, prove its \(\mathcal {NP}\)-hardness and introduce a lower bound. Further, we provide several heuristic solution procedures based on priority rules. A comparison of all procedures concludes this paper and gives insights on how to solve this problem in practice.     

Yard crane scheduling in port container terminals

Yard cranes are the most popular container handling equipment for loading containers onto or unloading containers from trucks in container yards of land scarce port container terminals. However, such equipment is bulky, and very often generates bottlenecks in the container flow in a terminal because of their slow operations. Hence, it is essential to develop good yard crane work schedules to ensure a high terminal throughput. This paper studies the problem of scheduling a yard crane to perform a given set of loading/unloading jobs with different ready times. The objective is to minimize the sum of job waiting times. A branch and bound algorithm is proposed to solve the scheduling problem optimally. Efficient and effective algorithms are proposed to find lower bounds and upper bounds. The performance of the proposed branch and bound algorithm is evaluated by a set of test problems generated based on real life data. The results show that the algorithm can find the optimal sequence for most problems of realistic sizes.     

Increasing the operational efficiency of container terminals in Australia

This paper discusses the major factors influencing the transfer efficiency of rail container terminals, as measured by the throughput time of containers. An analytically based simulation model is designed to describe container progress in the system. Cyclic heuristic rules for equipment assignment are applied and a new heuristic rule is developed to dispatch trains to tracks. The simulation model combined with the heuristic rules is used to address a number of specific objectives of the study. Different performance measures are applied and the impact that the train-to-track despatching and the handling equipment assignment can have on the measures is established. Validation and testing of models make use of data from Acacia Ridge Terminal, Brisbane, Australia.     

Conservative allocation models for outbound containers in container terminals

This paper examines location assignment for outbound containers in container terminals. It is an extension to the previous modeling work of Kim et al. (2000) and Zhang et al. (2010). The previous model was an “optimistic” handling way and gave a moderate punishment for placing a lighter container onto the top of a stack already loaded with heavier containers. Considering that the original model neglected the stack height and the state-changing magnitude information when interpreting the punishment parameter and hid too much information about the specific configurations for a given stack representation, we propose two new “conservative” allocation models in this paper. One considers the stack height and the state-changing magnitude information by reinterpreting the punishment parameter and the other further considers the specific configurations for a given stack representation. Solution qualities for the “optimistic” and the two “conservative” allocation models are compared on two performance indicators. The numerical experiments indicate that both the first and second “conservative” allocation models outperform the original model in terms of the two performance indicators. In addition, to overcome computational difficulties encountered by the dynamic programming algorithm for large-scale problems, an approximate dynamic programming algorithm is presented as well.     

Benchmark optimization and attribute identification for improvement of container terminals

The aim of this paper is to optimize the benchmarks and prioritize the variables of decision-making units (DMUs) in data envelopment analysis (DEA) model. In DEA, there is no scope to differentiate and identify threats for efficient DMUs from the inefficient set. Although benchmarks in DEA allow for identification of targets for improvement, it does not prioritize targets or prescribe level-wise improvement path for inefficient units. This paper presents a decision tree based DEA model to enhance the capability and flexibility of classical DEA. The approach is illustrated through its application to container port industry. The method proceeds by construction of multiple efficient frontiers to identify threats for efficient/inefficient DMUs, provide level-wise reference set for inefficient terminals and diagnose the factors that differentiate the performance of inefficient DMUs. It is followed by identification of significant attributes crucial for improvement in different performance levels. The application of this approach will enable decision makers to identify threats and opportunities facing their business and to improve inefficient units relative to their maximum capacity. In addition, it will help them to make intelligent investment on target factors that can improve their firms’ productivity.     

An application of cooperative game among container terminals of one port

In this paper a two-stage game that involves three container terminals located in Karachi Port in Pakistan is discussed. In the first stage, the three terminals have to decide on whether to act as a singleton or to enter into a coalition with one or both of the other terminals. The decision at this stage should presumably be based on the predicted outcome for the second stage. The second stage is here modelled as a Bertrand game with one outside competitor, the coalition and the terminal in Karachi Port (if any) that has not joined the coalition. Furthermore, three partial and one grand coalition among the three terminals at Karachi Port are investigated. The concepts of “characteristic function” and “core” are used to analyse the stability of these coalitions and this revealed that one combination does not satisfy the superadditivity property of the characteristic function and can therefore be ruled out. The resulting payoffs (profits) of these coalitions are analysed on the basis of “core”. The best payoff for all players is in the case of a “grand coalition”. However, the real winner is the outsider (the terminal at the second port) which earns a better payoff without joining the coalition, and hence will play the role of the “orthogonal free-rider”.     

Novel constraints satisfaction models for optimization problems in container terminals

The growth of containerization and transporting goods in containers has created many problems for ports. In this paper, we systematically survey a literature over problems in container terminals. The operations that take place in container terminals are described and then the problems are classified into five scheduling decisions. For each of the decisions, an overview of the literature is presented. After that, each of the decisions is formulated as Constraint Satisfaction and Optimization Problems (CSOPs). The literature also includes simulations and performance in container terminals. To evaluate any solution methods to the decisions and to measure its efficiency, several indicators are suggested.     

Optimal berth allocation and time-invariant quay crane assignment in container terminals

Due to the dramatic increase in the world’s container traffic, the efficient management of operations in seaport container terminals has become a crucial issue. In this work, we focus on the integrated planning of the following problems faced at container terminals: berth allocation, quay crane assignment (number), and quay crane assignment (specific). First, we formulate a new binary integer linear program for the integrated solution of the berth allocation and quay crane assignment (number) problems called BACAP. Then we extend it by incorporating the quay crane assignment (specific) problem as well, which is named BACASP. Computational experiments performed on problem instances of various sizes indicate that the model for BACAP is very efficient and even large instances up to 60 vessels can be solved to optimality. Unfortunately, this is not the case for BACASP. Therefore, to be able to solve large instances, we present a necessary and sufficient condition for generating an optimal solution of BACASP from an optimal solution of BACAP using a post-processing algorithm. In case this condition is not satisfied, we make use of a cutting plane algorithm which solves BACAP repeatedly by adding cuts generated from the optimal solutions until the aforementioned condition holds. This method proves to be viable and enables us to solve large BACASP instances as well. To the best of our knowledge, these are the largest instances that can be solved to optimality for this difficult problem, which makes our work applicable to realistic problems.     

Berth allocation in container terminals that service feeder ships and deep-sea vessels

This paper treats a berth allocation problem (BAP) in dedicated container terminals where feeder ships and container vessels are jointly served. When assigning quay space and a service time to each calling ship particular focus is put on the container exchange between feeder ships and mother vessels, so that the weighted number of containers delivered by feeder missing their intended mother vessel (and vice versa) does not exceed a given upper bound. The resulting BAP is formalized, complexity proofs are provided, and suited optimization procedures are presented and tested.     

Storage yard operations in container terminals: Literature overview,trends, and research directions

Inbound and outbound containers are temporarily stored in the storage yard at container terminals. A combination of container demand increase and storage yard capacity scarcity create complex operational challenges for storage yard managers. This paper presents an in-depth overview of storage yard operations, including the material handling equipment used, and highlights current industry trends and developments. A classification scheme for storage yard operations is proposed and used to classify scientific journal papers published between 2004 and 2012. The paper also discusses and challenges the current operational paradigms on storage yard operations. Lastly, the paper identifies new avenues for academic research based on current trends and developments in the container terminal industry.     

A simulation-based genetic algorithm approach for reducing emissions from import container pick-up operation at container terminal

Emissions from idle truck engines are a main source of pollution at container terminals. In this study, we focus on reducing such emission from waiting trucks as well as the related crane operations with a new truck arrival control method that gives individual truck limited time slots for entry. We develop a method to optimize the time slot assignment for individual trucks, aiming at minimizing total emissions from trucks and cranes at import yards. The method applies discrete event simulation to estimate total truck waiting times and crane moving distance, and then applies a genetic algorithm to minimize the generated emissions from these trucks and cranes. The experiment result shows that the truck arrivals should be controlled based on the stacking of import containers, and that such control is necessary for reducing truck idling emissions at a congested container terminal.     

Mixed integer programming models for dispatching vehicles at a container terminal

This paper presents scheduling models for dispatching vehicles to accomplish a sequence of container jobs at the container terminal, in which the starting times as well as the order of vehicles for carrying out these jobs need to be determined. To deal with this scheduling problem, three mixed 0–1 integer programming models, Model I, Model II and Model III are provided. We present interesting techniques to reformulate the two mixed integer programming models, Model I and Model II, as pure 0–1 integer programming problems with simple constraint sets and present a lower bound for the optimal value of Model I. Model III is a complicated mixed integer programming model because it involves a set of non-smooth constraints, but it can be proved that its solutions may be obtained by the so-called greedy algorithm. We present numerical results showing that Model III is the best among these three models and the greedy algorithm is capable of solving large scale problems.     

Optimization of two-step investment in a production process

We consider the problem of optimizing the control of a production process. The control parameters are the capacity utilization and the investment in the growth of the production capacity. We assume that the investments are divided into two parts: initial investment aimed at creating production facilities, and investment aimed at increasing the capacity during the production process. The initial and increased capacities and the moment of changing the capacity are variable parameters to be specified. The price of the product is assumed to be a random process. The problem is to optimize the production process and to construct a control strategy that maximizes the average discounted profit. We propose an approach to the construction of an optimal adaptive strategy for controlling the production. The approach is based on the dynamic programming method.     

Determination of the number of automated guided vehicles required at a semi-automated container terminal

This paper describes the development of a minimum flow algorithm to determine the number of automated guided vehicles (AGVs) required at a semi-automated container terminal. At such a terminal the containers are transported by AGVs from the quay cranes to the automated stacking cranes and vice versa. A model and a strongly polynomial time algorithm are developed to solve the case in which containers are available for transport at known time instants.     

Optimization of the injection molding process for short-fiber-reinforced composites

A fast and effective methodology integrating the finite-element and Taguchi methods is presented to determine the optimal design conditions of the injection molding process for short-fiber-reinforced polycarbonate composites. The finite-element-based flow simulation software, M-flow, was employed to simulate the molding process to obtain the fiber orientation distributions required. The Taguchi optimization technique was used to identify the optimal settings of injection molding parameters to maximize the shear layer thickness. The effects of four main parameters — the filling time, melt temperature, mold temperature, and injection speed — on the fiber orientation or the shear layer thickness were investigated and discussed. It is found that the dominant parameter is the filling time. The best levels of the four parameters to acquire the thickest shear layer are also identified.