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.

As the share of solar and wind in the energy mix increases, the inherent intermittency of these energy sources pose a great challenge for the Dutch power grid. The occurrence of problems with congestion and balancing power supply and demand are becoming more common. Companies in the Netherlands show potential to implement means for smart use of energy to alleviate these kind of problems. However, the gap between academic solutions and the industry is often too large. This research presents a method to find the optimal design of an integrated energy system in the Netherlands. A use case at Picnic is used to apply the proposed method. A flexible load scheduling optimisation algorithm is presented to explore the financial benefit of an integrated energy system that combines a photovoltaic system, an energy storage system, a cold storage system and a fleet of electric vehicles. The financial performance of different system designs are compared. Results show that the optimised load schedules for the EV fleet and CS system achieve a 2.6% decrease of energy costs with respect to the benchmark of Picnic. A PV system turns out to be beneficial for every size that the grid connection allows. This research finds 600 square meters to be optimal in the case of Picnic. An energy storage system would make optimised load schedules obsolete due to its flexibility. An energy storage system with a capacity of 250 kWh is found to be optimal according to the performance analysis. However, significant limitations in the assumptions of the storage system advise against installing it. Further research is required to elaborate the different elements in the proposed model. Different markets are suggested to use as a basis for load scheduling and a broader set of system designs is suggested to analyse their performance.