Lochbrunner, Markus

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Markus Lochbrunner

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2021 - 20222

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Gerade angezeigt 1 - 2 von 2
  • Vorschaubild
    Publikation
    Combining machine learning with human knowledge for delivery time estimations
    (American Association for Artificial Intelligence (AAAI) Press, 2022) Lochbrunner, Markus; Witschel, Hans Friedrich; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank
    Although machine learning algorithms outperform humans in many predictive tasks, their quality depends much on the availability of sufficient and representative training data. On the other hand, humans are capable of making predictions based on “spontaneous” transfers of knowledge from other domains or situations in cases where no directly relevant experiences exist. This can be seen very well in the task of predicting lead times in goods transport, where sudden disruptions or shortages may occur that are not reflected in historical data, but known to a well-informed human. If the variation can be anticipated and more accurate lead times estimated, proactive measures can be taken to decrease the impact. Therefore, we describe three novel approaches for delivery time predictions, combining a machine learning model with human input. The proposed logic covers two phases, learning based on actual delivery data and capturing human knowledge to cover exceptional situations not reflected in historical data. The proposed models and the resulting estimates were evaluated using deliveries from a retail company. It was found that the pure machine learning model delivers better results than a combination of humans and machines. On the one hand, this is caused by the complexity of incorporating human knowledge into the algorithm in a suitable way. On the other hand, it is also due to the tendency of humans to over-generalise the impact of certain events. Thus, although the pure machine learning model delivers superior estimation accuracy than the human-machine combination, our systematic qualitative analysis of the results presents insights for future development in this area.
    04B - Beitrag Konferenzschrift
  • Kein Vorschaubild vorhanden
    Publikation
    Benchmarking tabu search and simulated annealing for the capacitated vehicle routing problem
    (2021) Arockia, Amala; Lochbrunner, Markus; Hanne, Thomas; Dornberger, Rolf
    This paper addresses the Capacitated Vehicle Routing Problem (CVRP) consisting of a single depot and several customers that are supplied with goods by capacitated vehicles from a depot. The main objective of the vehicle routing problem is to minimize the traveled distance of all vehicles. We compare the Tabu Search (TS) and Simulated Annealing (SA) algorithm with different initial solution strategies to solve the CVRP. We run the publicly available solver on a set of benchmark problems comparing above mentioned methods and initial solutions. The results show that TS appears superior for small-sized problems, while SA has an advantage for mid-sized problems. For larger problems the preferability of a methods depends on the available run time with SA appear promising for shorter runtime and TS for longer.
    04B - Beitrag Konferenzschrift