Meta-heuristic improvement of order picking efficiency
Exploiting cross-order similarity in a goods-to-person warehouse
T.H.R. Biemans (TU Delft - Mechanical Engineering)
B De Schutter – Mentor (TU Delft - Delft Center for Systems and Control)
Frank Gorte – Mentor
Ton Van Den Boom – Graduation committee member (TU Delft - Team Bart De Schutter)
J. Alonso-Mora – Graduation committee member (TU Delft - Learning & Autonomous Control)
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Abstract
This research aims at determining a suitable order release strategy for a warehouse featuring a shuttle-based storage and retrieval system with multiple goods-to-person picking workstations. These picking workstations facilitate efficient picking; however, current release strategies do not take similarity between orders into account, while this could potentially lead to better capacity utilisation. A meta-heuristic approach has been developed in literature to exploit this similarity, thereby using less stock retrievals to fulfill customer orders. However, its capabilities are limited. In this thesis, the meta-heuristic is extended to deal with multiple pick stations and a stock-multiplicity constraint. The algorithm consists of multiple steps. First, a random-start greedy algorithm generates an initial solution for a large set of orders. However, this initial solution will be of poor quality in general. As the computation cost scales exponentially with the order set size, heuristically optimising the entire order set is infeasible in practice. Secondly, the initial order sequence is split into subsets of equal length, each of which will be picked by a separate picking workstation. The picking workstations are then sequentially optimised using the meta-heuristic approach assuming an infinite stock. Afterwards, a check on the stock-multiplicity constraint is conducted, i.e. it is checked if the warehouse is capable of supplying all picking stations simultaneously. If an order line violates this constraint, the order is selected and put in a random position of the order completion sequence of that picking station. It was found that this method is capable of avoiding violations of the stock-multiplicity constraints, although it was also found that this constraint will hardly be violated in a realistic goods-to-person warehouse setting, i.e. with a large number of orders, picking stations, and order lines per order. Furthermore, our simulations show that the algorithm is capable of significantly reducing the number of stock retrievals per picking workstation.