Research on the Integration and Scheduling of AGVs and Machines in Sample Testing Laboratory

In this paper, the problem of integration and scheduling of automated guided vehicles (AGVs) and inspection machines in medical sample-testing laboratories is addressed with the objective of minimizing the maximum turnaround time (TAT). A genetic algorithm (GA) with a sliding time window (STW) heuristic algorithm (GA-STW) is proposed for the studied problem while incorporating the vehicle assignment algorithm (VAA) for decoding operations. A mathematical model for AGV and inspection machine integration scheduling is established, and a dual-coding method for the inspection process and machine is used to solve the model. VAA was used to assign suitable AGVs to each transportation task during the decoding stage. The STW heuristic algorithm was used to further optimize the initial solution and allocate samples to appropriate inspection machines while scheduling the shortest transportation time for AGVs. The crossover operators, mutation operators, and elite numbers were experimentally optimized. A numerical simulation of standard examples demonstrated the superiority of the proposed algorithm.