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.

The LNG-ADP problem is a tactical planning problem for creating an annual delivery program (ADP) for a liquefied natural gas (LNG) producer. An ADP specifies the departure dates of the LNG carriers as well as the delivery dates at the different customers for a period of 12–18 months. The problem can be formulated as maritime inventory routing problem, as it is an important requirement to plan the deliveries such that inventory levels are kept within minimum and maximum limits at the customers as well as the LNG production facility. Inspired by the case of Yamal LNG, we propose a novel discrete-time formulation for the LNG-ADP problem with transshipment and intermediate storage. Our formulation also allows for waiting at the unloading ports. The problem is solved using a rolling horizon heuristic (RHH) for a case based on the Yamal LNG project. We study the impact of different RHH configurations on run time and solution quality. The results show that using a central period that is shorter than the forecast period provides the best objective function, whereas a central period that is longer than the forecast period improves run time. We also explore the effect of allowing waiting at the unloading ports. Waiting does not necessarily improve the objective function value, despite increasing the solution space. However, we observe a reduction in run time for instances where waiting is allowed.

The introduction of transshipment ports in the liquefied natural gas (LNG) supply chain in recent years offers additional flexibility, but also challenges to the planning of the annual delivery program. We present a new variant of the LNG-annual delivery program (ADP) planning problem by considering transshipment as well as time-dependent sailing times. We present a continuous time formulation for the LNG-ADP problem and propose a rolling horizon heuristic to solve the problem. Both the model and heuristic were used to solve a case inspired by the Yamal LNG project. The computational results show that the heuristic provides good solutions within a relatively short amount of time, especially compared to the exact solution methods. However, there is a trade-off between computational time and solution quality when designing the rolling horizon heuristic. The results also show the impact storage capacity at the transshipment port has on the total cost.

Here we study a short-sea feeder network design problem based on mother and daughter vessels. The main feature of the studied system is performing transshipment of cargo between mother and daughter vessels at appropriate locations at sea. This operation requires synchronization between both types of vessels as they have to meet at the same location at the same time. This paper studies the problem of designing a synchronized feeder network, explicitly accounting for the effect of uncertain travel times caused by harsh weather conditions. We propose an optimization-simulation framework to find robust solutions for the transportation system. The optimization model finds optimal routes that are then evaluated by a discrete-even simulation model to measure their robustness under uncertain weather conditions. This process of optimization simulation is repeated until a satisfactory condition is reached. To find even better solutions, we include different performance-improving strategies by adding robustness during route generation or exploiting flexibility in sailing speed to recover from delays. We apply the solution method to a case based on realistic data from a Norwegian shipping company. The results show that the method finds near-optimal solutions that offer robustness against schedule perturbations due to harsh weather. They also highlight the importance of considering uncertainty when designing a short-sea feeder network with transshipment at sea.