The energy transition from fossil fuels to sustainable alternatives makes the design of future-proof ships even more important. In the design phase of a ship, it is uncertain how many and which fuels it will use in the future due to many external factors. In fact, a ship typically sails for decades, increasing the likelihood that it will use different fuels during its lifetime. Pipe route design is expensive and time-consuming, mainly done by hand. Motivated by this, in previous research, we have proposed a mathematical optimization framework for automatic pipe routing under uncertainty of the energy transition. In this paper, we build on the state-of-the-art by implementing design constraints in mathematical models based on discussions with maritime design experts. Additionally, we apply these models to realistic, complex situations of a commercial ship design company. Our experiments show that location-dependent installation costs, which reflect reality, increase the usefulness of stochastic optimization compared to deterministic and robust optimization. Additionally, to prepare for a possible transition to more sustainable fuels, we recommend installing suitable pipes near the engine room upfront to prevent expensive retrofits in the future.@en

Providers of Emergency Medical Services (EMS) face the online ambulance dispatch problem, in which they decide which ambulance to send to an incoming incident. Their objective is to minimize the fraction of arrivals later than a target time. Today, the gap between existing solutions and the optimum is unknown, and we provide a bound for this gap. Motivated by this, we propose a benchmark model (referred to as the offline model) to calculate the optimal dispatch decisions assuming that all incidents are known in advance. For this model, we introduce and implement three different methods to compute the optimal offline dispatch policy for problems with a finite number of incidents. The performance of the offline optimal solution serves as a bound for the performance of an – unknown – optimal online dispatching policy. We show that the competitive ratio (i.e., the worst case performance ratio between the optimal online and the optimal offline solution) of the dispatch problem is infinitely large; that is, even an optimal online dispatch algorithm can perform arbitrarily bad compared to the offline solution. Then, we performed benchmark experiments for a large ambulance provider in the Netherlands. The results show that for this realistic EMS system, when dispatching the closest idle vehicle to every incident, one obtains a fraction of late arrivals that is approximately 2.7 times that of the optimal offline policy. We also analyze another online dispatch heuristic, that manages to reduce this gap to approximately 1.9. This constitutes the first quantification of the gap between online and offline dispatch policies.

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