  The Generalised Travelling Salesman Problem (GTSP) is a well-known problem
that, among other applications, arises in warehouse order picking, where each
stock is distributed between several locations -- a typical approach in large
modern warehouses. However, the instances commonly used in the literature have
a completely different structure, and the methods are designed with those
instances in mind. In this paper, we give a new pseudo-random instance
generator that reflects the warehouse order picking and publish new benchmark
testbeds. We also use the Conditional Markov Chain Search framework to
automatically generate new GTSP metaheuristics trained specifically for
warehouse order picking. Finally, we report the computational results of our
metaheuristics to enable further competition between solvers.
