A Gang Scheduling Computational Paradigm for Container Terminal Logistics with Processor Affinity

Existing scheduling and decision solutions to container terminal logistics systems (CTLS) are falling short of peoples' wishes both in theory and in practice. This paper proposes a container terminal gang scheduling computational paradigm (GSCP) for planning and control at container terminals based on computational thinking. GSCP is built on the computing perspective and architecture of multi-processor system-on-chip. GSCP applies principles of pervasive computing to evaluate load conditions of core resources, and then integrates the fundamental principles of gang scheduling, processor affinity and load balancing to define the flexible and robust decision framework and scheduling algorithm set. Those are intended to improve the performance of CTLS, and strive for balance among through capacity for vessels and containers, task latency and load leveling as well. GSCP is demonstrated and validated by a typical container hub logistics service case with intensive computational experiments.