This paper deals with large-scale crew scheduling problems arising at the main Dutch railway operator, Netherlands Railways (NS). NS operates about 30000 trains a week. All these trains need a driver and a certain number of guards. Some labor rules restrict the duties of a certain crew base over the complete week. Therefore, splitting the problem in several subproblems per day leads to suboptimal solutions.In this paper, we present an algorithm, called LUCIA, which can solve such huge instances without splitting. This algorithm combines Lagrangian heuristics, column generation and fixing techniques. We compare the results with existing practice. The results show that the new method significantly improves the solution.
One of the key issues for transportation companies is to produce an optimal plan for the work of crew members. Crew planning consists of a sequence of phases, the first two corresponding to planning duties (sequences of trips to be done by crew members from their home base to their home base) and planning rosters (sequences of duties and rest days to be followed by crew members during a certain number of weeks). Both duty and roster planning are subject to a large number of constraints. Duty planning is constrained by intra-duty constraints and roster planning by inter-duty constraints. Since inter-duty constraints relate how duties can be combined into a roster, it is desirable that some of these constraints be transposed into the duty planning phase, as additional constraints, to guarantee that the duties produced in the first phase are “rosterable” in the second phase. Both Artificial Intelligence (AI) and Operations Research (OR) have addressed duty planning, but for very large scale problems, OR has been far more successful due to its global vision of the problem. This paper discusses the use of AI local search to improve an OR-based duty planning optimizer that uses additional constraints.
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