Joshua D. Summers

Shear compliant hexagonal meso-structures having high shear strength and high shear strain

A shear layer for a shear band that is used in a tire is provided that has multiple cells or units having an auxetic configuration and that are constructed from aluminum or titanium alloys. The cells may have an angle of −10°.

Three-Dimensional Packing by a Heuristic-based Sequential Genetic Algorithm

This paper presents a hybrid Genetic Algorithm for three-dimensional packing optimization. It is applicable to a class of packing problems where the final number of packed items is unknown. Examples include the bin packing problem and the luggage capacity problem where the compactness or packing efficiency must be maximized by selecting an appropriate subset of items from a predefined set and packing them in a predefined container. Many algorithms, which are reviewed in this paper, have been developed in various engineering fields for specific applications. Packing algorithms can be categorized into two types according to the definition of the design variables. In the first type, the design variables are the spatial coordinates of the packed items. In this case, a constraint specifying zero collision between items must be considered and evaluated for each trial set of design variables. In the second type, the design variables correspond to the sequential order in which the items are considered for packing. In this case, the location of an item is unknown until it is successfully packed inside the container based on a sequence. The advantage of the latter approach is that the zero-collision constraint is implicitly defined in the packing procedure, thereby reducing the need for collision detection and the overall computation effort. The present algorithm is of the second type. The algorithm was developed in two phases. In the first phase, the algorithm was extensively studied and improved using a simplified packing problem where the container and all items are modeled as rectangular prisms with discrete 90-degree rotations. This phase allowed the identification of appropriate values for the problem-dependent genetic parameters such as crossover and mutation rates. In the second phase, sets of heuristic rules were implemented and tested. The use of appropriate heuristics led to an increase in computational efficiency without significant loss in optimality. This paper describes the algorithm and its multiple variants defined by the heuristic rules imbedded in the packing strategy. Several heuristics and packing strategies are presented and the issues related to computational efficiency are discussed. The algorithm is demonstrated on several test problems.