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When a truck or bus suffers from a breakdown it is important that the vehicle comes back on the road as soon as possible. In this paper we present a prototype diagnostic decision support system capable of automatically identifying possible causes of a failure and propose recommended actions on how to get the vehicle back on the road as cost efficiently as possible. This troubleshooting system is novel in the way it integrates the remote diagnosis with the workshop diagnosis when providing recommendations. To achieve this integration, a novel planning algorithm has been developed that enables the troubleshooting system to guide the different users (driver, help-desk operator, and mechanic) through the entire troubleshooting process. In this paper we formulate the problem of integrated remote and workshop troubleshooting and present a working prototype that has been implemented to demonstrate all parts of the troubleshooting system.

Maintenance planning of trucks at Scania have previously been done using static cyclic plans with fixed sets of maintenance tasks, determined by mileage, calendar time, and some data driven physical models. Flexible maintenance have improved the maintenance program with the addition of general data driven expert rules and the ability to move sub-sets of maintenance tasks between maintenance occasions. Meanwhile, successful modelling with machine learning on big data, automatic planning using constraint programming, and route optimization are hinting on the ability to achieve even higher fleet utilization by further improvements of the flexible maintenance. The maintenance program have therefore been partitioned into its smallest parts and formulated as individual constraint rules. The overall goal is to maximize the utilization of a fleet, i.e. maximize the ability to perform transport assignments, with respect to maintenance.

The European Union’s 2020 target aims to be producing 20 % of its energy from renewable sources by 2020, to achieve a 20 % reduction in greenhouse gas emissions and a 20 % improvement in energy efficiency compared to 1990 levels. To reach these goals, the energy consumption has to decrease which results in reduction of the emissions. The transport sector is the second largest energy consumer in the EU, responsible for 25 % of the emissions of greenhouse gases caused by the low efficiency (<40 %) of combustion engines. Much work has been done to improve that efficiency but there is still a large amount of fuel energy that converts to heat and escapes to the ambient atmosphere through the exhaust system. Taking advantage of thermoelectricity, the heat can be recovered, improving the fuel economy.