Synchromodale Samenwerkingsmodellen ter ontwikkeling van het ‘Physical Internet’

Decades of advancements have resulted in deep knowledge about inventory control. Yet, despite our on-going quest to unravel hard problems, many of them continue to puzzle us. At the same time we have entered an era of analytics in which data, computational power and (smart) algorithms are easily accessible, allowing researchers to push the performance of algorithms to unseen levels. A milestone was achieved in 2016 when AlphaGo---a machine learning algorithm---beat the best human player at the ancient board game Go, by relying on a technique called deep reinforcement learning. In the wake of this breakthrough we provide evidence in Chapter 2 that deep reinforcement learning can also solve hard inventory problems, which is especially promising when problem-dependent heuristics are lacking. Nonetheless, the resulting replenishment policies generally remain hard to interpret, hampering adoption in practice. In parallel, robust optimization has been gaining momentum within inventory control. By minimizing the worst-case cost rather than the expected cost, mathematical insights may emerge while the robustness is desirable in practical settings. In Chapter 3 we apply robust optimization in a transportation setting with limited supply but with the option to order more at a premium. Our model saves up to 5% compared to the policy currently in use by a collaborating manufacturer.  The above methods fare well in today's data-driven era but from a scientific perspective mathematical proofs remain indispensable. In Chapter 4 we extend our knowledge about dual-source supply chains by proving the optimal policy structure of a system in which the local supplier charges a premium per unit sourced beyond a threshold. Such a cost structure relates to contemporary challenges such as a renewed interest in on-shoring due to the Covid-19 pandemic. Chapter 5 closes with the main conclusions and a reflection on future research avenues that emerged during this fantastic journey.