An Enhanced Self-Organizing Genetic Algorithm for Optimizing Path in Container Transportation Routing under Time Windows

Abstract International trade and economic development of a region are basically facilitated by efficient container transport planning. To determine optimal routes considering different factors such as distance, cost, and time, this study combines mixed integer linear programming and enhanced Self-Organizing Genetic Algorithms (SOGA) along with Dijkstra's Algorithm. A case study examining transport from Dar es Salaam (Tanzania) to Bujumbura (Burundi), illustrates the model's potential to deliver substantial cost savings and improved delivery times. Policymakers and logistics managers can benefit greatly from these findings, which highlight the significance of multimodal transport options in increasing the effectiveness and sustainability of container logistics within the East African Community. Consequently, more aspects such as the effects on the environment, the state of the roads, and seasonal fluctuations should be taken into account in future studies.