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

## TL;DR

This paper introduces a new benchmark generator and metaheuristics based on Conditional Markov Chain Search for the Generalised Travelling Salesman Problem in warehouse order picking, tailored to real-world warehouse structures.

## Contribution

It presents a novel pseudo-random instance generator reflecting warehouse order picking and develops specialized metaheuristics using the Conditional Markov Chain Search framework.

## Key findings

- New benchmark testbeds for warehouse GTSP instances
- Metaheuristics trained specifically for warehouse order picking
- Computational results demonstrating effectiveness of the approach

## Abstract

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.