Online rules for container stacking

Container stacking rules are an important factor in container terminal efficiency. In this paper, we investigate two concepts to increase efficiency and compare them to several benchmark algorithms, using a discrete-event simulation tool. The first concept is to use knowledge about container departure times, in order to limit the number of reshuffles. We stack containers leaving shortly before each other on top of each other. The second concept is the trade-off between stacking further away in the terminal versus stacking close to the exit points and accepting more reshuffles. It is concluded that even the use of imperfect or imprecise departure time information leads to significant improvements in efficiency. Minimizing the difference in departure times proved to be important. It was also found that the trade-off between stacking further away in the terminal versus stacking close by the exit points and accepting more reshuffles leads to improvements over the benchmark

On Rearrangement of Items Stored in Stacks

There are $n \ge 2$ stacks, each filled with $d$ items, and one empty stack. Every stack has capacity $d > 0$. A robot arm, in one stack operation (step), may pop one item from the top of a non-empty stack and subsequently push it onto a stack not at capacity. In a {\em labeled} problem, all $nd$ items are distinguishable and are initially randomly scattered in the $n$ stacks. The items must be rearranged using pop-and-pushs so that in the end, the $k^{\rm th}$ stack holds items $(k-1)d +1, \ldots, kd$, in that order, from the top to the bottom for all $1 \le k \le n$. In an {\em unlabeled} problem, the $nd$ items are of $n$ types of $d$ each. The goal is to rearrange items so that items of type $k$ are located in the $k^{\rm th}$ stack for all $1 \le k \le n$. In carrying out the rearrangement, a natural question is to find the least number of required pop-and-pushes. Our main contributions are: (1) an algorithm for restoring the order of $n^2$ items stored in an $n \times n$ table using only $2n$ column and row permutations, and its generalization, and (2) an algorithm with a guaranteed upper bound of $O(nd)$ steps for solving both versions of the stack rearrangement problem when $d \le \lceil cn \rceil$ for arbitrary fixed positive number $c$. In terms of the required number of steps, the labeled and unlabeled version have lower bounds $\Omega(nd + nd{\frac{\log d}{\log n}})$ and $\Omega(nd)$, respectively

Evaluating impact of truck announcements on container stacking efficiency

Container stacking rules are an important factor in container terminal efficiency. We build on prior research and use a discrete-event simulation model to evaluate the impact of a truck announcement system on the performance of online container stacking rules. The information that is contained in the announcement, i.e., the expected departure time for an import container, can be used to schedule pre-emptive remarshall moves. These moves can then be performed when the workload is low in order to decrease the export time and the crane workload at peak times

An Improved Fuzzy Knowledge-Based Model For Long Stay Container Yards

This paper considers the problem of allocating newly arrived containers to stacks of existing containers in a yard when the departure date/time for containers is unknown. Many factors and constraints need to be considered when modelling this storage allocation problem. These constraints include the size, type and weight of the containers. The factors are the number of containers in a stack and the duration of stay of the topmost container in the stack. This paper aims to develop an improved Fuzzy Knowledge-Based ‘FKB’ model for best allocation practice of long-stay containers in a yard. In this model, the duration of stay factor does not need to be considered in the allocation decision if the duration of stay for the topmost containers in a stack is similar; hence, a new ‘ON/OFF’ strategy is proposed within the Fuzzy Knowledge-Based model to activate/deactivate this factor in the stacking algorithm whenever is required. Discrete Event Simulation and Fuzzy Knowledge-Based techniques are used to develop the proposed model. The model’s behaviour is tested using three real-life scenarios, including allocating containers in busy, moderately busy and quiet yards. The total number of re-handlings, the number of re-handlings per stack, and the number of re-handlings for containers were considered KPIs in each scenario

Research into container reshuffling and stacking problems in container terminal yards

Container stacking and reshuffling are important issues in the management of operations in a container terminal. Minimizing the number of reshuffles can increase productivity of the yard cranes and the efficiency of the terminal. In this research, the authors improve the existing static reshuffling model, develop five effective heuristics, and analyze the performance of these algorithms. A discrete-event simulation model is developed to animate the stacking, retrieving, and reshuffling operations and to test the performance of the proposed heuristics and their extended versions in a dynamic environment with arrivals and retrievals of containers. The experimental results for the static problem show that the improved model can solve the reshuffling problem more quickly than the existing model and the proposed extended heuristics are superior to the existing ones. The experimental results for the dynamic problem show that the results of the extended versions of the five proposed heuristics are superior or similar to the best results of the existing heuristics and consume very little time

A Soft Optimization Model to Solve Space Allocation Problems in Breakbulk Terminals

In recent decades, freight transportation systems have been developed rapidly. This development leads to using various policies to enhance system utilization. The studies show that an optimized policy related to space allocation benefits the shareholders in freight transportations. The objective of space allocation problems is to find the best arrangement of cargos in warehouse cells to meet the problem aims. In this paper, inspired by the Office Space Allocation problem, we developed a novel model to minimize the handling costs and to maximize available spaces for the next arriving cargo. We first formulate the optimization model and discuss various constraints. We then present an approach to solve the proposed model. Lastly, we analyze a numerical example derived from the data of Port of Beaumont to illustrate the efficiency of the model

A Decision Support System for the Storage Space Allocation Problem under the Effect of Disturbances: a Case of the Port of Arica (Chile)

Lo scopo del lavoro è provare a risolvere il container allocation problem: come disporre i container in maniera più efficace all'interno di un terminal? Dopo un attento esame della letteratura, sono state sviluppate diverse strategie di allocazione basate sulla fuzzy logic. Tali strategie sono poi state combinate per dare vita ad un sistema che sia in grado di reagire a eventi che possono alterare le normali operazioni all'interno del terminal.ope