In this paper we address the problem of simultaneous scheduling of machines and vehicles in flexible manufacturing systems. The studied problem is a job shop where the jobs have to be transported between the machines by automatic guided vehicles. In addition to the processing of jobs, we consider transportation aspect as an integral part of the optimization process. To deal with this problem, we propose a new solution representation based on vehicles rather than machines. Each solution can thus be evaluated using a discrete event approach. An efficient neighbouring system is then described and implemented into three different metaheuristics: iterated local search, simulated annealing and their hybridisation. Computational results are presented for a benchmark of 40 literature instances. New upper bounds are found for 11 of them, showing the effectiveness of the presented approach.