Job assignment and scheduling optimisation of multiple-load mobile transport systems

Automated material-handling systems (AMHS) increasingly adopt multiple-load mobile transports (MLMTs) to improve the efficiency of work-in-process movements in modern manufacturing environments. However, the assignment and scheduling of MLMTs remain challenging due to complex interactions between travel time, batch formation, vehicle capacity, and workload variability. This paper develops a novel optimisation-based decision-aid model grounded in an adaptive large neighbourhood search (ALNS) framework that simultaneously determines job-to-vehicle assignments and schedules for MLMT systems. The proposed model captures the trade-offs between capacity utilisation and cycle-time performance, and provides a unified framework for evaluating different fleet configurations and operating policies. Computational experiments show that the proposed approach reduces average delivery time by up to 14.4% relative to the best-performing baseline, particularly under high-load production scenarios. From a practical perspective, the results offer actionable insights for the design and operation of AMHS in semiconductor, electronics, and general manufacturing facilities. Overall, the results highlight the importance of integrated assignment and scheduling decisions and effective congestion management in improving the performance of MLMT systems.