Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking The Generalised Travelling Salesman Problem (GTSP) is a wellknown problem that, among other applications, arises in warehouse order picking, where each stock is distributed between several locations xe2x80x93 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 pseudorandom 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.

An UtilityBased Storage Assignment Strategy for eCommerce Warehouse Management Order picking is one of the most labor intensive and time consuming parts of ecommerce warehouse order management. An effective solution to the item storage assignment problem can significantly improve the efficiency of warehouse operations and improve customer service levels. The problem, however, is quite challenging due to distinct item characteristics (volume, velocity, variety), multiple item relationships (cooccurrence patterns, complementarity, compatibility and substitution effects), layout configurations (single vs multiblock, pure vs mixedshelves, dedicated vs scattered storage), orderpicking strategies (standalone, batched, zoned) and routing strategies (traversal, return, midpoint) and is NP hard. Several heuristic approaches have been proposed in the literature to address the storage assignment problem. More recent and stateoftheart approaches to the problem have considered item relationships to improve the efficiency of order picking. We argue that such methods are largely myopic in nature and consider just pairwise item correlations and also ignore quantities of items in an order. Moreover, current methods make several simplifying assumptions about layout configurations, orderpicking and routing strategies. In this paper, we propose a novel utilitybased storage assignment strategy for the item storage assignment problem. It uses a topk high utility itemset mining method and a heuristic algorithm for effective storage assignment. We conduct rigorous experimental evaluation of the proposed idea and compare it against other related methods. The experimental results on wide variety of layout configurations, routing strategies and order distributions show very promising results for the proposed storage assignment strategy. The findings of this study are likely to be useful for warehouse managers in improving operational efficiencies and delivering better customer service levels.