Determination of Weights for Multiobjective Combinatorial Optimization in Incident Management With an Evolutionary Algorithm

Incident management in railway operations includes dealing with complex and multiobjective planning problems with numerous constraints, usually with incomplete information and under time pressure. An incident should be resolved quickly with minor deviations from the original plans and at acceptable costs. The problem formulation usually includes multiple objectives relevant to a railway company and the employees involved in controlling operations. Still, there is little established knowledge and agreement regarding the relative importance of objectives such as expressed by weights. Due to the difficulties in assessing weights in a multiobjective context directly involving decision makers, we elaborate on the autoconfiguration of weighted fitness functions based on nine objectives used in a related Integer Linear Programming (ILP) problem. Our approach proposes an evolutionary algorithm and tests it on real-world railway incident management data. The proposed method outperforms the baseline, where weights are equally distributed. Thus, the algorithm shows the capability to learn weights in future applications based on the priorities of employees solving railway incidents which is not yet possible due to an insufficient availability of real-life data from incident management.