Relevant Boxes Research Articles

Sample of Groups: A New Strategy to Find a Representative Point for Each Undisclosed Cluster

Some problems involving the selection of samples from undisclosed groups are relevant in various areas such as health, statistics, economics, and computer science. For instance, when selecting a sample from a population, well-known strategies include simple random and stratified random selection. Another related problem is selecting the initial points corresponding to samples for the K-means clustering algorithm. In this regard, many studies propose different strategies for choosing these samples. However, there is no consensus on the best or most effective approaches, even when considering specific datasets or domains. In this work, we present a new strategy called the Sample of Groups (SOG) Algorithm, which combines concepts from grid, density, and maximum distance clustering algorithms to identify representative points or samples located near the center of the cluster mass. To achieve this, we create boxes with the right size to partition the data and select the representatives of the most relevant boxes. Thus, the main goal of this work is to find quality samples or seeds of data that represent different clusters. To compare our approach with other algorithms, we not only utilize indirect measures related to K-means but also employ two direct measures that facilitate a fairer comparison among these strategies. The results indicate that our proposal outperforms the most commonly used algorithms.

A GRASP ALGORITHM FOR THE CONTAINER LOADING PROBLEM WITH MULTI-DROP CONSTRAINTS

This paper studies a variant of the container loading problem in which to the classical geometric constraints of packing problems we add other conditions appearing in practical problems, the multi-drop constraints. When adding multi-drop constraints, we demand that the relevant boxes must be available, without rearranging others, when each drop-off point is reached. We present first a review of the different types of multi-drop constraints that appear in literature. Then we propose a GRASP algorithm that solves the different types of multi-drop constraints and also includes other types of realistic constraints such as full support of the boxes and load bearing strength. The computational results validate the proposed algorithm, which outperforms the existing procedures dealing with multi-drop conditions and is also able to obtain good results for more standard versions of the container loading problem.

Australian films at large: expanding the evidence about Australian cinema performance

International markets have in recent years become a critical component of the business model for Hollywood cinema, opening up a renewed interest in the global dimensions of film diffusion. Smaller film-producing nations such as Denmark have similarly emphasised global distribution as a key component of the industry's success. Typically, however, claims for Australian film industry success rely almost exclusively on a film's domestic box office performance. This paper considers the possibilities for an expanded approach to measuring success and failure in the Australian film industry. Adopting analytic methods from cinema studies, cultural economics and geo-spatial sciences, this paper will examine the international theatrical circulation of Australian films using a unique global database of cinema showtimes. This data set captures all formal film screenings in 47 countries over an 18-month period ending 1 June 2014 and enables detailed empirical study of the locations visited by Australian-produced films. In conjunction with relevant box office data and contextual critical commentary, we propose a revised and expanded ‘film impact rating’ for assessing the reported performance of Australian films.

Container loading with multi‐drop constraints

AbstractIn this paper, an algorithm for the container‐loading problem (CLP) with multi‐drop constraints is presented. When adding multi‐drop constraints, we demand that the relevant boxes must be available, without rearranging others, when each drop‐off point is reached. To make the solutions feasible in the real world, it is further demanded that all boxes are placed in a feasible manner with respect to load‐bearing strength and with proper support from below. This makes it possible to load consignments originating from builder merchants. A heuristic based on a tree search framework is proposed. It uses greedy solutions to evaluate each choice taken. To make the framework more generic, a dynamic breadth is proposed. Based on problem characteristics and the time limit imposed, it will choose the breadth of the tree, making sure that the time is utilised most profitably. The algorithm is tested on new real‐world data from a Danish company distributing construction products. For the solutions to these problems to be feasible in a real‐world setting, both multi‐drop and load‐bearing strength constraints are essential. The obtained results show that the proposed model and algorithm are able to solve the new real‐world problems in fractions of a second. Furthermore, results obtained on benchmark problems indicate that the algorithm performs comparably with other more specialised methods.