No abstract

In this contribution, a generic two-phase stochastic variable neighborhood approach is applied to nurse rostering problems. The proposed algorithm is used for creating feasible and efficient nurse rosters for many different nurse rostering cases. In order to demonstrate the efficiency and generic applicability of the proposed approach, experiments with real-world input data coming from many different nurse rostering cases have been conducted. The nurse rostering instances used have significant differences in nature, structure, philosophy and the type of hard and soft constraints. Computational results show that the proposed algorithm performs better than six different existing approaches applied to the same nurse rostering input instances using the same evaluation criteria. In addition, in all cases, it manages to reach the best-known fitness achieved in the literature, and in one case, it manages to beat the best-known fitness achieved till now

No abstract

Electric buses have long been recognized as a promising direction for offering sustainable public transportation services. While range and battery performance constraints have hindered the widespread adoption of electric buses in the past, technological advances make them a prominent and attractive option for public transportation in the future. Still, operational constraints and the need for additional (charging) infrastructure highlight the need for introducing appropriate decision-making tools, tailor-made for supporting the design of transit networks operated by electric buses. This paper focuses on developing and testing a comprehensive route design model for the case of a transit network, operated exclusively by an electric bus fleet (Electric Transit Route Network Design Problem—E-TRNDP). The model is formulated as a bi-level optimization problem, which attempts to jointly design efficient transit routes and locate required charging infrastructure. A multi-objective, particle swarm optimization algorithm, coupled with a mixed linear—integer programming model is used to solve the model. An existing benchmark network is used as a test-bed for the proposed model and solution process; results illustrate that the proposed model and solution method yield realistic design outcomes in an acceptable time frame.

No abstract