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The Generalised Travelling Salesman Problem (GTSP) is a well-known problemthat, among other applications, arises in warehouse order picking, where eachstock is distributed between several locations -- a typical approach in largemodern warehouses. However, the instances commonly used in the literature havea completely different structure, and the methods are designed with thoseinstances in mind. In this paper, we give a new pseudo-random instancegenerator that reflects the warehouse order picking and publish new benchmarktestbeds. We also use the Conditional Markov Chain Search framework toautomatically generate new GTSP metaheuristics trained specifically forwarehouse order picking. Finally, we report the computational results of ourmetaheuristics to enable further competition between solvers.

Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking