Optimal Fleet Assignment Using Ant Colony Algorithm

Abstract

Demand for Iogistical services is highly dynamic, due to its high market growth. In Bangladesh, air logistics is evolving at a fast pace, both in terms of passenger and cargo transportation. As a result, several local private and international airlines are trying to capture market share in this promising sector. For survival in such tough competition, optimization in operations is indispensable. This research focuses on the optimal fleet assignment with Ant Colony algorithm. In this rapidly expanding market, short to mid-term demands of passenger were estimated using regression analysis. Then, from a database of routes and aircraft capacity, a model was developed to estimate profitability from fleet assignment with relative ease. Finally, ant colony algorithm was used to find the optimal assignment. Although previous researches on fleet assignment were done using Genetic algorithm, only current-level of demand for Iogistical service was considered. This research considers it as dynamic and projected into the future. The results obtained in this research show that dynamic demand consideration gives much better results as well as better utilization of resources. And these contributed to the reduction of operational cost as well as the increase in revenue. As a result, profit was optimized. For midterm and long-term projection, demand becomes gradually probabilistic. However, this research considered it as deterministic; otherwise, the problem becomes an NP-hard problem, which is difficult to solve. This research is expected to be of immense help to the air industry of Bangladesh. Because of similarity in business nature, this can be marginally adapted in other countries as well.