The development of autonomous ships is at an advanced stage. It seems to be just a question of time before such ships become available at an industrial scale. In this paper, we investigate the economic advantages of utilizing autonomous over conventional ships. We consider a shipping network composed of conventional mother ships and autonomous daughter ships that perform cargo transshipment at specific ports. The mother ships sail between Europe and the Norwegian coastline and are conventional due to international regulations. The daughter ships serve small ports located along the Norwegian coastline and are autonomous. We apply this concept of mother and daughter routes to a Norwegian shipping company that transports containers between a continental European port and 21 ports scattered along the Norwegian coastline. Furthermore, we extend this case to a shipping network of 42 ports to study a possible increase in the activity of the company. For solving the corresponding optimization problem, we present a path‐flow‐based model formulation and a heuristic route generation method. We carry out extensive computational experiments that consider differently sized problems, various demand scenarios, advanced routes structures, and sensitivity analyses for cost rates and the degree of automation of the maritime transport system. The results provide evidence that autonomous ships might contribute to considerable cost savings.

This paper studies a prediction problem using time series data and machine learning algorithms. The case study is related to the quality control of bumper beams in the automotive industry. These parts are milled during the production process, and the locations of the milled holes are subject to strict tolerance limits. Machine learning models are used to predict the location of milled holes in the next beam. By doing so, tolerance violations are detected at an early stage, and the production flow can be improved. A standard neural network, a long short term memory network (LSTM), and random forest algorithms are implemented and trained with historical data, including a time series of previous product measurements. Experiments indicate that all models have similar predictive capabilities with a slight dominance for the LSTM and random forest. The results show that some holes can be predicted with good quality, and the predictions can be used to improve the quality control process. However, other holes show poor results and support the claim that real data problems are challenged by inappropriate information or a lack of relevant information.