This paper tackles the 2-Dimensional Guillotine Cutting Stock Problem with Stack Constraints. The problem asks for the cutting of a set of items with the minimum amount of raw material. The cutting patterns are subject to a number of constraints, including a new realistic constraint, regarding item precedence, which has just been introduced in the literature. In this case, the items are organized in stacks, where each stack represents a customer request and defines the order in which the items must be cut. That is, if item i precedes item j within a stack, then i must be cut before j. However, there is no precedence constraint between items in different stacks. This constraint comes from applications where items must be stacked and shipped in the exact order that they will be used by the customer, thus avoiding the risk of damaging fragile items (as is the case in the glass industry) or the cost of moving heavy items (as is the case in the steel industry). We propose two constructive heuristics extended from the literature for the problem, in addition to a dynamic programming based heuristic that uses as a subroutine an exact pseudo-polynomial time algorithm developed for the Rectangular Knapsack Problem with Batch Constraints. Computational experiments, performed on three sets of realistic instances, showed that the dynamic programming based heuristic found solutions with smaller optimally gaps in all instances evaluated.

Moore's law provoked decades of advances in computer's performance due to transistor's evolution. Despite all success in its improvement, current technology is reaching its physical limits and some replacements are the focus of investigations, such as the Field-Coupled Nanocomputing devices. These devices achieve information transfer and computation via local field interactions, reaching ultra-low power consumption. Nevertheless, there exists a hard energy limit related to the Laws of Thermodynamics that bounds any digital evaluation. To reduce the impact of this restriction, we propose a fitness function to improve the energy efficiency on a given circuit implementation. We embed our fitness function on an evolutionary method known as Cartesian Genetic Programming to iteratively modify the circuit, searching for a new valid configuration that dissipates less energy. To assess our method, we use it on pre-optimized circuits from benchmarks and compare our results with the ones from the classic Cartesian Genetic Programming. Based on the outcome, we show that our method outperforms the latter, achieving, on average, 15% gain in energy efficiency.

The Caloric-Restricted Diet Problem (CRDP) aims at finding diets with a reduced caloric count that also respects the nutritional needs of an individual. Thus, it is possible to achieve weight loss without compromising the individual's health. However, due to the small amount of energy contained in such diets, one may not be fully satisfied after a meal. It is possible to overcome this drawback by inserting a larger amount of protein in the diet, as it was shown to be the most effective macronutrient that provides satiety. Thus, this work presents a multi-objective mathematical formulation for the CRDP that minimizes the calorie count of the diet and maximizes the number of proteins ingested. Besides that, a Generalized Differential Evolution algorithm (GDE3) is proposed to solve the resulting problem. Computational experiments are performed with both mono-objective and multi-objective CRDP and two example diets are presented. It shows that it is possible to achieve a diet with a large amount of proteins, while restricting the caloric number.