Productivity improvement based on a decision support tool for optimization of constrained delivery problem with time windows

Nowadays there is more and more competition in the industrial sector. Globalization makes fierce rivalry in the market between the different stakeholders at all levels offering to the customers a wide choice of cheaper products. Therefore, it is crucial to adopt efficient strategies to do the right things better with less resources and more benefit. The choice of the best techniques and methods is important and often tools need to be implemented. The purpose of this work is to introduce how productivity can be improved through delivery schedule optimization based on a decision support tool. This work is driven from an industrial case study. The results show a productivity improvement with a better use of resources up to 10% and effortless logistics management. Moreover, a comparison study is conducted between Genetic Algorithm and Ant Colony Optimization showing that our approach outperforms them in efficiency (approximate to 36% and approximate to 25% respectively) and in computation time.