A dynamic vehicle-scheduling problem

• Main Author: Szeto, Caroline
• Language: English
• Published: 2010
• Online Access: http://hdl.handle.net/2429/18911

This study applies Doll's formal decision rules to solve a dynamic vehicle-scheduling problem provided by ALLTRANS EXPRESS LTD. ( Vancouver ). Computer simulation is used as the research tool. The computer simulated results are compared with ALLTRANS solutions based on the performance measures of mean travel time per customer, mean and standard deviation of time to serve a customer, and mean and standard deviation of delivery time per customer. Doll's decision rules contain two scheduling heuristics, i e , closest customer heuristic and time saved heuristic, and a set of three dispatching decision rules associated with parameters ME, MB and S. It is found that Doll's decision rule methods do not improve the solutions in terms of reducing travel time per customer but can produce higher service quality in terms of reducing the time to satisfy a customer requirement after its occurrence. The general performance of Doll's decision rules on this specific situation indicates that: (1) The time saved heuristic is more preferable in solving this problem. (2) Both ME and MB can affect the performance measures described above, and combinations of these two parameters can control the trade-off between the mean travel time per customer and mean time to satisfy a customer request after its occurrence. (3) Geographical restriction which depends basically on the design of sectoring mechanism ( S ) can affect all five performance measures. Further research should be done on testing the effects of the within sector condition ( S ) of the dispatching decision rules, with emphasis on the design of a specific sectoring mechanism. Also, with a larger size problem, further studies should be performed on the use of combinations of the dispatching decision rules to control the trade-off between mean travel time per customer and mean times to satisfy a customer request after its occurrence. === Business, Sauder School of === Graduate