Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking
This addresses warehouse order picking efficiency for logistics and operations, but is incremental as it adapts existing methods to a specific domain.
The authors tackled the Generalised Travelling Salesman Problem (GTSP) in warehouse order picking by developing a new pseudo-random instance generator and benchmark testbeds, and used Conditional Markov Chain Search to generate metaheuristics, reporting computational results for solver competition.
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.