The Economic Impact of Container-loading Problem

Thousands of containers with different types of cargo are loaded every day in multiple manufacturing and logistics centres in the world. The main problem arising from these handlings is how to make the maximum use of all the available container capacities, while keeping the overall costs of transport per cargo unit as low as possible. The previous research mostly focuses on studying different algorithms for optimising container loading with cargo that has already been assigned based on its dimensions and weight. However, this paper will emphasise the importance of using algorithms in the planning and preparation of the cargo itself during the manufacturing processes before it is dispatched for loading into containers. Besides the length, width, height, and weight of the cargo itself, a fifth component influencing the overall transport costs will be considered, i.e. the manner of loading a container. The research will be carried out on an example of a container shipment of wooden sawn timber materials

Novel approaches to container loading: from heuristics to hybrid tabu search

A thesis submitted for the degree of Doctor of Philosophy of the University ofBedford shireThis work investigates new approaches to the container loading problem which address the issue of how to load three-dimensional, rectangular items (e.g. boxes) into the container in such a way that maximum utilisation is made of the container space. This problem occurs in several industry sectors where the loading approach places cargo effectively into aeroplanes, ships, trailers or trucks in order to save considerable cost. In carrying out this work, the investigation starts by developing a new heuristic approach to the two-dimensional bin packing problem, which has lower complexity than container loading in the aspects of constraints and geometry. A novel approach, including the heuristic strategies and handling method for remaining areas, is developed that can produce good results when testing with benchmark and real world data. Based on the research for two-dimensional bin packing, a novel heuristic approach is developed to deal with the container loading problem with some practical constraints. The heuristic approach to container loading also includes heuristic strategies and the handling of remaining spaces. The heuristic strategies construct effective loading arrangements where combinations of identical or different box types are loaded in blocks. The handling method for remaining spaces further improves the loading arrangements through the representation, partitioning and merging of remaining spaces. The heuristic approach obtains better volume utilisation and the highest stability compared with other published heuristic approaches. However, it does not achieve as high a volume utilisation as metaheuristic approaches, e.g. genetic algorithms and tabu search.To improve volume utilisation, a new hybrid heuristic approach to the container loading problem is further developed based on the tabu search technique which covers the encoding, evaluation criterion and configuration of neighbourhood and candidate solutions. The heuristic strategies as well as the handling method for remaining spaces developed in the heuristic approach are used in this new hybrid tabu search approach. It is shown that the hybrid approach has better volume utilisation than the published approaches under the condition that all loaded boxes with one hundred per cent support from below. In addition, the experimental results show that both the heuristic and hybrid tabu search approaches can also be applied to the multiple container loading problem

A heuristic for the container loading problem: A tertiary-tree-based dynamic space decomposition approach

Increasing fuel costs, post-911 security concerns, and economic globalization provide a strong incentive for container carriers to use available container space more efficiently, thereby minimizing the number of container trips and reducing socio-economic vulnerability. A heuristic algorithm based on a tertiary tree model is proposed to handle the container loading problem (CLP) with weakly heterogeneous boxes. A dynamic space decomposition method based on the tertiary tree structure is developed to partition the remaining container space after a block of homogeneous rectangular boxes is loaded into a container. This decomposition approach, together with an optimal-fitting sequencing and an inner-right-corner-occupying placement rule, permits a holistic loading strategy to pack a container. Comparative studies with existing algorithms and an illustrative example demonstrate the efficiency of this algorithm

A Genetic Algorithm Approach to the Container Loading Problem

The problem considered in this work is the Container Loading Problem. In this problem a set of rectangular boxes has to be packed in one rectangular container so that the available container space usage is maximized. The orientation constraints and the distinction between homogeneous and heterogeneous types of cargo are considered. We present the results obtained with a Genetic Algorithm approach. The good performance of this algorithm is shown by comparing them with well-known algorithms and results from the literature

An effective placement method for the single container loading problem

© 2016 Elsevier Ltd. All rights reserved. This study investigates a three-dimensional single container loading problem, which aims to pack a given set of unequal-size rectangular boxes into a single container such that the length of the occupied space in the container is minimized. Motivated by the practical logistics instances in literature, the problem under study is formulated as a zero-one mixed integer linear programming model. Due to the NP-hardness of the studied problem, a simple but effective loading placement heuristic is proposed for solving large-size instances. The experimental results demonstrate that the developed heuristic is capable of solving the instances with more than two hundred boxes and more efficient than the state-of-the-art mixed integer linear program and existing heuristic methods

A Hybrid Meta-heuristic for the Container Loading Problem

It is very common in an enterprise daily operation to solve Container Loading Problem (CLP). Especially, it is an important issue in the logistic management. The problem aims to determine the arrangement of objects with the best utilization ratio in a container. It belongs to the combinatorial optimization problem. In this paper, a two-phased method focusing on the improvement of the efficiency and on the reducing of the problem size is proposed. In the first phase, a constructive method incorporated with a decision rule borrowing from ant colony optimization is used to construct tower set. The pheromone updating mechanism is useful in choosing proper object while constructing tower using decision rule. In the second phase, an improvement method based on genetic algorithm is used. First, the method sorts the towers by the utilization ratio and then assigns a number to each tower accordingly. The chromosome is a sequence of tower numbers which represents the arrangement of towers in the container’s bottom plane. The fitness function is defined as the utilization ratio. A new structure to store the pheromone is proposed which can help the ant in choosing the appropriate object while constructing tower. In this way, the efficiency of the method and the utilization of the container are improved

Simulation-optimization models for the dynamic berth allocation problem

Container terminals are designed to provide support for the continuous changes in container ships. The most common schemes used for dock management are based on discrete and continuous locations. In view of the steadily growing trend in increasing container ship size, more flexible berth allocation planning is mandatory. The consideration of continuous location in the container terminal is a good option. This paper addresses the berth allocation problem with continuous dock, which is called dynamic berth allocation problem (DBAP). We propose a mathematical model and develop a heuristic procedure, based on a genetic algorithm, to solve the corresponding mixed integer problem. Allocation planning aims to minimise distances travelled by the forklifts and the quay crane, for container loading and unloading operations for each ship, according to the quay crane scheduling. Simulations are undertaken using Arena software, and experimental analysis is carried out for the most important container terminal in Spain