2 resultados para Difficult times

em Digital Commons at Florida International University


Relevância:

60.00% 60.00%

Publicador:

Resumo:

After a decade of over-expansion, the hotel industry began the '90s with excess capacity and decreased demand. Since 1993, the U.S. hotel industry has experienced a turnaround which continued into 1994- 1995 with good performance by most firms. However; competition will continue to be fierce and many challenges are awaiting hotel companies in a more global environment. This article examines the key elements for achieving success in a challenging hospitality industry environment while focusing on the strategies and techniques employed by some successful hotel companies during difficult times.

Relevância:

30.00% 30.00%

Publicador:

Resumo:

This dissertation presents a system-wide approach, based on genetic algorithms, for the optimization of transfer times for an entire bus transit system. Optimization of transfer times in a transit system is a complicated problem because of the large set of binary and discrete values involved. The combinatorial nature of the problem imposes a computational burden and makes it difficult to solve by classical mathematical programming methods. ^ The genetic algorithm proposed in this research attempts to find an optimal solution for the transfer time optimization problem by searching for a combination of adjustments to the timetable for all the routes in the system. It makes use of existing scheduled timetables, ridership demand at all transfer locations, and takes into consideration the randomness of bus arrivals. ^ Data from Broward County Transit are used to compute total transfer times. The proposed genetic algorithm-based approach proves to be capable of producing substantial time savings compared to the existing transfer times in a reasonable amount of time. ^ The dissertation also addresses the issues related to spatial and temporal modeling, variability in bus arrival and departure times, walking time, as well as the integration of scheduling and ridership data. ^