A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem

The 0-1 knapsack problem is a well-known combinatorial optimisation problem. Approximation algorithms have been designed for solving it and they return provably good solutions within polynomial time. On the other hand, genetic algorithms are well suited for solving the knapsack problem and they find reasonably good solutions quickly. A naturally arising question is whether genetic algorithms are able to find solutions as good as approximation algorithms do. This paper presents a novel multi-objective optimisation genetic algorithm for solving the 0-1 knapsack problem. Experiment results show that the new algorithm outperforms its rivals, the greedy algorithm, mixed strategy genetic algorithm, and greedy algorithm + mixed strategy genetic algorithm

Analysis of Solution Quality of a Multiobjective Optimization-based Evolutionary Algorithm for Knapsack Problem

Multi-objective optimisation is regarded as one of the most promising ways for dealing with constrained optimisation problems in evolutionary optimisation. This paper presents a theoretical investigation of a multi-objective optimisation evolutionary algorithm for solving the 0-1 knapsack problem. Two initialisation methods are considered in the algorithm: local search initialisation and greedy search initialisation. Then the solution quality of the algorithm is analysed in terms of the approximation ratio

Moldable Items Packing Optimization

This research has led to the development of two mathematical models to optimize the problem of packing a hybrid mix of rigid and moldable items within a three-dimensional volume. These two developed packing models characterize moldable items from two perspectives: (1) when limited discrete configurations represent the moldable items and (2) when all continuous configurations are available to the model. This optimization scheme is a component of a lean effort that attempts to reduce the lead-time associated with the implementation of dynamic product modifications that imply packing changes. To test the developed models, they are applied to the dynamic packing changes of Meals, Ready-to-Eat (MREs) at two different levels: packing MRE food items in the menu bags and packing menu bags in the boxes. These models optimize the packing volume utilization and provide information for MRE assemblers, enabling them to preplan for packing changes in a short lead-time. The optimization results are validated by running the solutions multiple times to access the consistency of solutions. Autodesk Inventor helps visualize the solutions to communicate the optimized packing solutions with the MRE assemblers for training purposes

Dezentrales Lastmanagement zum Ausgleich kurzfristiger Abweichungen im Stromnetz

In dieser Arbeit wird ein vollständig dezentrales Konzept zum Last- und Erzeugungsmanagement vorgestellt, mit dem sich Stromverbraucher und dezentrale Stromerzeugungsanlagen selbst organisieren können, um kurzfristig präzise Laständerungen durchzuführen. Dabei sollen nicht nur größere Stromverbraucher in Industriebetrieben, sondern auch Haushaltsgeräte (z. B. Gefrierschränke) und dezentrale Stromerzeugungsanlagen einbezogen werden

Dezentrales Lastmanagement zum Ausgleich kurzfristiger Abweichungen im Stromnetz

In dieser Arbeit wird ein vollständig dezentrales Konzept zum Last- und Erzeugungsmanagement vorgestellt, mit dem sich Stromverbraucher und dezentrale Stromerzeugungsanlagen selbst organisieren können, um kurzfristig präzise Laständerungen durchzuführen. Dabei sollen nicht nur größere Stromverbraucher in Industriebetrieben, sondern auch Haushaltsgeräte (z. B. Gefrierschränke) und dezentrale Stromerzeugungsanlagen einbezogen werden