Optimization for the management performance of drivers at automotive Ro–Ro terminals: Consider efficiency and fairness

This research aims to improve the management performance of drivers by comparing operation method one (OMI) and operation method two (OMII), i.e., to increase the work efficiency of drivers and balance the workload among drivers in ship-loading operations at automotive roll on roll off (Ro–Ro) terminals. It is processed with the cooperative optimization of the storage location assignment problem (SLAP) and shuttle bus scheduling problem (SBSP) under OMI and OMII, respectively. For the SBSP under both OMI and OMII, to calculate the work time or quantify the efficiency of drivers and ensure operation safety, the stop point of the shuttle bus at each ship-loading round is considered. For the SLAP: OMI proposes an innovative assignment strategy, which addresses the deadlock situations caused by blocked car routes and the conflict situations resulting from no backward operation of cars (reversing); OMII considers an empirical assignment rule, which allows backward operation and keeps no more than two types of cars assigned to the same column in the storage zone. Furthermore, two 0-1 mixed integer programming models (model-I and model-II) are formulated based on OMI and OMII, respectively. A meta-heuristic algorithm based on particle swarm optimization and adaptive large neighborhood search (TS-MH) is developed for model-I. According to the features of model-II, a dynamic programming (DP) algorithm is adopted. A series of numerical experiments verify the rationality of the models; indicate that the TS-MH algorithm can search management solutions for the large-scale instances that are no worse than the DP algorithm; and demonstrate that OMI has more advantages in the management performance of drivers than OMII, especially in workload fairness.