Vehicle Routing Problem Using Simulated Annealing
To solve a combinatorial optimisation problem such as this by a method such as Simulated Annealing, you need 3 basic ingredients:


A representation for a 'Candidate Solution'. For vehicle routing, this is typically a permutation of the depots, i.e. a list of depots in the order to be visited.
A measure of the quality of a candidate solution. Various methods exist for penalising solutions with deliveries outside their time windows or fail to meet other constraints.
One or more 'perturbation operators' for transforming a candidate solution into another (hopefully better) one. For example, swapping two randomly-chosen depots in the permutation is one of the simplest approaches, but of course more informed operators as possible.


Given these three, it's then possible to plug your problem into a range of metaheuristics, such as iterated local search, Tabu search, various Evolutionary Algorithms or Tabu search.

As regards annealing schedule, there's a range of literate on choosing parameters, but one simple option is to obtain initial and final temperatures from the statistics of a sequence of random perturbations, then experiment with the choice of cooling rate in a geometric annealing schedule.

The excellent (and freely-available) book "Essentials of Metaheuristics" offers a more detailed background on the above and some guidelines for what techniques to apply in what circumstances.