Sentence examples for travel optimization from inspiring English sources

To test whether bees rely on nearest-neighbour movements, we investigated the travel optimization performance of traplining bumble-bees faced with a multi-location routing problem in a flight cage.

We analysed the travel optimization performance of bees (flight distances, flight durations and number of flower visits) using complete flower visitation sequences, including all revisits to the same flower.

Here, we report the travel optimization performance of bumble-bees (Bombus terrestris) foraging in a flight cage containing six artificial flowers arranged such that movements between nearest-neighbour locations would lead to a long suboptimal route.

Figure 2b shows the corresponding R2 values for the travel time optimization of p in the [1 < p < 2] interval.

Furthermore, the number of necessary iterations depends strictly on the distance which the nodes need to travel during the optimization step.

The results demonstrate feasible and effective of the proposed model and solution methodology, and show that rail ridership increases and total passenger travel time declines after optimization.

The upper level focuses on choosing the potential links in the pre-specified candidate set, and the lower level assigns all the flows to the super network with principles of user equilibrium or system optimization with travel time budgets.

Main streams methods for distributed routing seek to avoid congestion by global travel time reduction based on optimization methods [7, 9].

By incorporating travel behavior models within the optimization process, the model accounts for the potential network-level effects of highway improvement schemes.

By compromising in the passenger attraction, concurrent optimization reduces the travel time from 11.8 to 10.8 min, shortens the track length from 6.7 to 5.8 miles, decreases the initial cost from 74.3 to 54.1 M, and also decreases the operation and user cost for about 4.0 M.

The proposed methodology is based on combined elitist ant colony optimization and multiple travelling salesman problem.