Container terminal scheduling and decision-making using simulation based optimization and business intelligence

Abstract

Scheduling and decision-making in container terminal logistics system (CTLS) has been the focus of research and application. This paper remodels the operation of CTLS through advancing an innovative multilevel control and feedback framework and methodology. That utilizes the knowledge engine which lies in business intelligence platform to drive the simulation and optimization engine indwelling in the simulation based optimization tool which drives the operation of port ultimately. At the same time, the whole CTLS is modeled by agent-based computing, and the production and scheduling section is based on the Harvard architecture, which serves the turn to solve scheduling and decision-making in the complex system and makes the modeling to process of outstanding agility and robustness. This methodology roots in pervasive computing deeply and helps the executors and managers in port to constitute the scheduling plan and support important decision. Finally, an applied instance on the berth allocation and an equipment layout analysis to support decision are demonstrated to validate the feasibility and creditability of the methodology.