A simulation-based heuristic for fleet assignment
Integer programming, simulation, and rules of thumb have been integrated to develop a simulation-based heuristic for short-term assignment of fleet in the car rental industry. It generates a plan for car movements, and a set of booking limits to produce high revenue for a given planning horizon. Thr...
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ndltd-fiu.edu-oai-digitalcommons.fiu.edu-etd-24312018-07-19T03:34:13Z A simulation-based heuristic for fleet assignment Anorga, Sonia Rosario Integer programming, simulation, and rules of thumb have been integrated to develop a simulation-based heuristic for short-term assignment of fleet in the car rental industry. It generates a plan for car movements, and a set of booking limits to produce high revenue for a given planning horizon. Three different scenarios were used to validate the heuristic. The heuristic's mean revenue was significant higher than the historical ones, in all three scenarios. Time to run the heuristic for each experiment was within the time limits of three hours set for the decision making process even though it is not fully automated. These findings demonstrated that the heuristic provides better plans (plans that yield higher profit) for the dynamic allocation of fleet than the historical decision processes. Another contribution of this effort is the integration of IP and rules of thumb to search for better performance under stochastic conditions. 2000-12-22T08:00:00Z text application/pdf http://digitalcommons.fiu.edu/etd/1296 http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=2431&context=etd FIU Electronic Theses and Dissertations FIU Digital Commons Industrial Engineering |
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Industrial Engineering Anorga, Sonia Rosario A simulation-based heuristic for fleet assignment |
description |
Integer programming, simulation, and rules of thumb have been integrated to develop a simulation-based heuristic for short-term assignment of fleet in the car rental industry. It generates a plan for car movements, and a set of booking limits to produce high revenue for a given planning horizon.
Three different scenarios were used to validate the heuristic. The heuristic's mean revenue was significant higher than the historical ones, in all three scenarios. Time to run the heuristic for each experiment was within the time limits of three hours set for the decision making process even though it is not fully automated. These findings demonstrated that the heuristic provides better plans (plans that yield higher profit) for the dynamic allocation of fleet than the historical decision processes.
Another contribution of this effort is the integration of IP and rules of thumb to search for better performance under stochastic conditions. |
author |
Anorga, Sonia Rosario |
author_facet |
Anorga, Sonia Rosario |
author_sort |
Anorga, Sonia Rosario |
title |
A simulation-based heuristic for fleet assignment |
title_short |
A simulation-based heuristic for fleet assignment |
title_full |
A simulation-based heuristic for fleet assignment |
title_fullStr |
A simulation-based heuristic for fleet assignment |
title_full_unstemmed |
A simulation-based heuristic for fleet assignment |
title_sort |
simulation-based heuristic for fleet assignment |
publisher |
FIU Digital Commons |
publishDate |
2000 |
url |
http://digitalcommons.fiu.edu/etd/1296 http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=2431&context=etd |
work_keys_str_mv |
AT anorgasoniarosario asimulationbasedheuristicforfleetassignment AT anorgasoniarosario simulationbasedheuristicforfleetassignment |
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1718712963814129664 |