River dolphins are strongly affected by the construction of hydroelectric dams. Potential isolation in subpopulations above and below such dams and the resulting low genetic variability of these subpopulations can cause extinction at a local level. Here we aimed to estimate density and population size of South American river dolphins (boto Inia geoffrensis and tucuxi Sotalia fluviatilis), map their distribution, and estimate potential biological removal (PBR) limits in order to evaluate the effects of population fragmentation between planned dams in the Tapajós River, Amazonian basin, Brazil. Boat-based surveys were conducted following a line transect sampling protocol covering different dolphin habitats in 2 stretches of the river divided by rapids. The mark−recapture distance sampling method was applied to account for animals missed on the trackline. After the estimation of population sizes by habitat, PBR was calculated. The farthest upriver sighting of tucuxis was close to the São Luiz do Tapajós rapids, whereas the farthest upriver sighting of botos was upstream of the rapids, suggesting that botos move upstream through the rapids. Estimated abundance of tucuxis (3372 ind., CV = 0.38) was twice as high as that estimated for botos (1815 ind., CV = 0.4). The PBR ranged from 11 to 18 ind. for boto and 21 to 34 for tucuxi. Throughout this study, we identified low abundances of river dolphins compared to other Amazon rivers. Boto may not be sustainable at a population level, due primarily to population fragmentation which would result from the construction of the proposed dams. Precautionary measures are urgently needed before construction of dams begins in the Tapajós River.

Home care services are in high demand given how they are steadily becoming the primary source of care for the elderly. Powerful decision support tools are indispensable for effectively managing available staff in the context of ever-increasing demand for care and limited caregiver availability. This paper advances home care literature by introducing flexible task durations, thereby enabling tasks to be completed faster and ultimately more care to be scheduled. This new concept, which originates from practice, introduces an additional decision to be made when creating a schedule, thereby greatly increasing the scheduling complexity. Consequently, this paper introduces a new optimization-based decision support model which allows for scheduling with flexible task duration, as well as other types of flexibility. A computational study quantifies the impact of: (i) scheduling with a finer task granularity thereby enabling accurate prioritization of high and low priority care, (ii) flexibility in task duration enabling tasks to be completed faster and more care to be scheduled, and (iii) increasing the number of different locations visited by a caregiver thereby enabling a trade-off between the number of serviced clients and caregiver workload. A new publicly available real-world data set is used, obtained directly from home care organizations operating in Flanders. Analysis of the computational results demonstrates that significant improvements in operational efficiency may be realized with minimal effort required by organizations. Furthermore, the proposed algorithm's performance is confirmed by comparison against the bounds obtained by solving an integer programming formulation of the problem. Finally, a management policy scheme is proposed which, when gradually implemented in a home care organization, results in a more efficient and therefore cost-effective deployment of its workforce.

A direct way of reducing the number of cars on the road is to dissuade individuals from exclusively using their car and instead integrate public transport into their daily routine. Planning multi-modal journeys is a complex task for which individuals often rely on decision support tools. However, offering individuals different journey options represents a significant algorithmic challenge. The failure to provide users with a set of journey options that differ considerably from one another in terms of the modes of transport employed is currently preventing the widespread uptake of multi-modal journey planning among the general public. In this paper, we introduce a dynamic programming algorithm that remedies this situation by modeling different transport networks as a graph that is then pruned by various graph-reduction pre-processing techniques. This approach enables us to offer a diverse set of efficient multi-modal solutions to users almost instantaneously. A computational study on three datasets corresponding to various real-world mobility networks with up to 30,000 vertices and 596,000 arcs demonstrates the effectiveness of the proposed algorithm.

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