Recent changes in the retailing landscape require companies to optimise their operations in order to stay competitive. In a business-to-consumer context, the widespread adoption of e-commerce is posing challenges for retailers. Due to fierce competition between companies, responding to customer orders as fast as possible has become essential to attract customers. In a business-to-business context, a reduction in lead times can be observed, requiring fast customer response times. In order to reduce the delivery delays, new optimisation strategies are required. In this dissertation, new strategies are proposed to optimise the order handling process, with a focus on the order picking and vehicle routing subprocesses. A first strategy is the use of online optimisation algorithms, allowing to quickly consider dynamically arriving orders during the operations. A second strategy is the use of integrated decision-making. Instead of solving interrelated planning problems separately, a solution for the combined planning problem is obtained, leading to large efficiency improvements. In this dissertation, multiple metaheuristic algorithms are developed for the considered optimisation problems. All algorithms are able to handle dynamic order arrivals and solve the problem in an integrated manner. First, we study how order picking operations can be scheduled efficiently in a dynamic, business-to-business setting. For this, a heuristic algorithm is developed, which considers the integration of order batching, picker routing and batch scheduling decisions and is able to account for dynamic order arrivals. A comparison to the current state-of-the-art highlights the strong performance of the proposed algorithm in a static context. Next, experiments in the online context show the benefit of anticipating on future order arrivals, in order to keep the customer service level high. Additionally, the benefits of receiving customer orders a bit more in advance are shown, indicating the complexity of handling very urgent orders as fast as possible. Second, in order to show the applicability of the proposed algorithm in practice, a real-life study is performed on real-life order data. The real-life study shows the benefits of using our metaheuristic algorithm compared to simpler heuristics as used in the current operational practice. Large efficiency gains are possible, leading to an improved service level and reduced costs. Moreover, a waiting strategy is proposed, allowing to postpone an order picker’s departure at the depot if his batch is not filled to its capacity. The use of this waiting strategy is shown to lead to improved operational efficiency. Finally, the combined optimisation problem of order picking and vehicle routing operations in a business-to-consumer setting is studied. By considering the order picking and vehicle routing decisions in an integrated manner, improved solutions can be obtained. Four metaheuristic algorithms are developed to solve the combined order picking and vehicle routing problem, considering the integration of order picking and vehicle routing decisions to a different extent. The results show the benefits of using more sophisticated algorithms that consider the interaction between picking and routing. By performing experiments considering different operating conditions, the difficulty of accommodating to stringent customer requests is highlighted. Combined, in all settings considered, for both order picking individually and order picking and vehicle routing combined, the results indicate large benefits of adopting algorithms that use integrated decision-making strategies, in theoretical as well as practical settings. The use of order anticipation and a waiting strategy are studied and shown to lead to improved results in the order picking problem. In the reallife case, the proposed metaheuristic algorithm leads to strong performance improvements compared to the current operational practice, allowing for large cost reductions without harming service levels. Finally, in the study on the integrated order picking and vehicle routing problem, insights are obtained into the challenges of same-day delivery operations in an e-commerce setting. In a next step, these findings can be used to develop more effective pricing strategies for the delivery fees, as delivery fees tailored to current operational situation may contribute to the efficiency of the order handling process.