Overview

Today’s warehouses are faced with new challenges that require strategies that can offer more flexibility than conventional strategies are able to do.  In this context, Wenrong’s PhD project aims to address the flexibility challenge by investigating how order-picking, which is a key factor affecting warehouse performance, can be dynamically managed.  The order-picking operation is typically constrained by inventory management in the warehouse and transportation management of placed orders.  More specifically, the order-picking operation is managed based on three key decisions:

a) When should the orders be picked from the warehouse?

b) Which storage location should the order-picker visit?

c) How should the orders be batched together to form a pick-list?

Having proposed an interventionist routing algorithm to enable the dynamic re-routing of an order-picker during the picking operation, Wenrong’s project now investigates the dynamism from making the three decisions in different sequences.  By formulating the problem as a Markov Decision Process (MDP), Wenrong aims to develop a method of making the decisions in appropriate sequence based on the status of the operation so as to improve the flexibility as well as the efficiency of the order-picking operation.

Objective

To improve the flexibility challenge in today’s warehouse by investigating how order-picking, the key factor affecting warehouse performance, can be dynamically managed.

Approaches

• Interventionist Order-picking strategy: to update a picker during picking-tour with the relevant order status and a new optimal picking-route.
• Coordination for outbound processes: centred by the intervention style for order-picking, the scheduling of two outbound processes,  transportation and order-picking, is modelled using a Markov decision process (MDP) based approach in order to determine a solution of fulfilling the orders with the lowest cost.

Outputs

• Interventionist Routing Algorithm (IRA).
• Methods of adopting IRA (interventionist order-picking strategies).
• A MDP model for scheduling the transportation and order-picking operations.
• A solution for determining solutions of the MDP model using reinforcement learning based approach (Q-learning method).

Supervisor

Duncan McFarlane

Sponsor

YH Global China