  Logistics and transport are core of many industrial and business processes.
One of the most promising segments in the field is optimisation of vehicle
routes. Scientific effort is focused primarily on algorithms developed in
simplified environment and cover a fraction of real industrial application due
to complex combinatorial algorithms required to be promptly executed. In this
paper, a real-world case study in all its complexity is observed and formulated
as a real-world vehicle routing problem (VRP). To be able to computationally
cope with the complexity, we propose a new procedure based on adaptive memory
metaheuristic combined with local search. The initial solution is obtained with
Clarke-Wright algorithm extended here by introducing a dropout factor to
include a required stochastic attribute. The procedure and corresponding
algorithms are tested on the existing benchmarks and further on the real
industrial case study, which considers capacities, time windows, soft time
windows, heterogeneous vehicles, dynamic fuel consumption, multi-trip delivery,
crew skills, split delivery and, finally, time-dependent routes as the most
significant factor. In comparison with the current state-of-the-art algorithms
for vehicle routing problem with a large number of constraints, we obtain an
average savings of 2.03% in delivery time and 20.98% in total delivery costs.
