We propose a new FPTAS for the multi-objective shortest path problem. The algorithm uses elements from both an exact labeling algorithm and an FPTAS proposed by Tsaggouris and Zaroliagis (2009). We analyze the running times of these three algorithms both from a theoretical and a computational point of view. Theoretically, we show that there are instances for which the new FPTAS runs an arbitrary times faster than the other two algorithms. Furthermore, for the bi-objective case, the number of approximate solutions generated by the proposed FPTAS is at most the number of Pareto-optimal solutions multiplied by the number of nodes. By performing a set of computational tests, we show that the new FPTAS performs best in terms of running time in case there are many dominated paths and the number of Pareto-optimal solutions is not too small.
In this paper, we consider the Robust Periodic Timetabling Problem (RPTP), the problem of designing an adjustable robust periodic timetable. We develop a solution method for a parametrized class of uncertainty regions. This class relates closely to uncertainty regions known in the robust optimization literature, and naturally defines a metric for the robustness of the timetable. The proposed solution method combines a linear decision rule with well-known reformulation techniques and cutting-plane methods. We show that the RPTP can be solved for practical-sized instances by applying the solution method to practical cases of Netherlands Railways (NS). In particular, we show that the trade-off between the efficiency and robustness of a timetable can be analyzed using our solution method.
Millions of employees around the world work in irregular rosters. The quality of these rosters is of utmost importance. High-quality rosters should be attractive on an individual level, but also divide the work fairly over the employees. We develop novel methodology to compute the trade-off between fairness and attractiveness in crew rostering. First, we propose an intuitive fairness scheme for crew rostering and analyze its theoretical performance. To do so, we introduce the approximate resource-allocation problem. This extension of the resource-allocation problem provides a framework for analyzing decision making in contexts where one relies on approximations of the utility functions. Fairness is a typical example of such a context due to its inherently subjective nature. We show that the scheme has “optimal” properties for a large class of approximate utility functions. Furthermore, we provide a tight bound on the utility loss for this scheme. We then present a unified approach to crew rostering. This approach integrates our proposed fairness scheme with a novel mathematical formulation for crew rostering. We call the resulting problem the Fairness-Oriented Crew Rostering Problem and develop a dedicated exact Branch-Price-and-Cut solution method. We conclude by applying our solution approach to practical instances from Netherlands Railways, the largest passenger railway operator in the Netherlands. Our computational results confirm the importance of taking the fairness–attractiveness trade-off into account. This paper was accepted by Yinyu Ye, optimization.
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