Supporting Your Basic Needs - A Base Support Approach for Static Stability Assessments in Air Cargo

Static stability is one of the most important constraints in the design and efficient calculation of safe air cargo pallets. To calculate the static stability of a cargo layout, base-focused methods such as full or partial base support are often used. Compared to mechanical or simulation-based methods, they offer high performance and simplicity. However, these methods currently reach their limits when dealing with the practical complexity of air cargo, making them difficult to apply in practice. In this research, we extend and generalize these support point methods by modeling irregular and multilevel cargo shapes, which enables improved practical applications. We follow a design-oriented approach to capture air cargo requirements, design an artifact, and evaluate its performance. Our results show a generalized approach that covers a greater practical complexity while maintaining its efficiency

A unified race algorithm for offline parameter tuning

This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods

TS2PACK: A Two-Level Tabu Search for the Three-dimensional Bin Packing Problem

Three-dimensional orthogonal bin packing is a problem NP-hard in the strong sense where a set of boxes must be orthogonally packed into the minimum number of three-dimensional bins. We present a two-level tabu search for this problem. The first-level aims to reduce the number of bins. The second optimizes the packing of the bins. This latter procedure is based on the Interval Graph representation of the packing, proposed by Fekete and Schepers, which reduces the size of the search space. We also introduce a general method to increase the size of the associated neighborhoods, and thus the quality of the search, without increasing the overall complexity of the algorithm. Extensive computational results on benchmark problem instances show the effectiveness of the proposed approach, obtaining better results compared to the existing one