Rescheduling policies for large-scale task allocation of autonomous straddle carriers under uncertainty at automated container terminals

• We present RNJ & RCJ replanning policies for straddle carriers under uncertainty.
• We use exact BBCG and approximate auction algorithms for each short-term planning.
• Results demonstrate the superiority of RCJ policy as compared with RNJ policy.
• Long-term tests show the need of an improved solution algorithm for each planning.

This paper investigates replanning strategies for container-transportation task allocation of autonomous Straddle Carriers (SC) at automated container terminals. The strategies address the problem of large-scale scheduling in the context of uncertainty (especially uncertainty associated with unexpected events such as the arrival of a new task). Two rescheduling policies–Rescheduling New arrival Jobs (RNJ) policy and Rescheduling Combination of new and unexecuted Jobs (RCJ) policy–are presented and compared for long-term Autonomous SC Scheduling (ASCS) under the uncertainty of new job arrival. The long-term performance of the two rescheduling policies is evaluated using a multi-objective cost function (i.e., the sum of the costs of SC travelling, SC waiting, and delay of finishing high-priority jobs). This evaluation is conducted based on two different ASCS solving algorithms–an exact algorithm (i.e., branch-and-bound with column generation (BBCG) algorithm) and an approximate algorithm (i.e., auction algorithm)–to get the schedule of each short-term planning for the policy. Based on the map of an actual fully-automated container terminal, simulation and comparative results demonstrate the quality advantage of the RCJ policy compared with the RNJ policy for task allocation of autonomous straddle carriers under uncertainty. Long-term testing results also show that although the auction algorithm is much more efficient than the BBCG algorithm for practical applications, it is not effective enough, even when employed by the superior RCJ policy, to achieve high-quality scheduling of autonomous SCs at the container terminals.