It is sometimes challenging to plan winter maintenance operations in advance because snow storms are stochastic with respect to, e.g., start time, duration, impact area, and severity. In addition, maintenance trucks may not be readily available at all times due to stochastic service disruptions. A stochastic dynamic fleet management model is developed to assign available trucks to cover uncertain snow plowing demand. The objective is to simultaneously minimize the cost for truck deadheading and repositioning, as well as to maximize the benefits (i.e., level of service) of plowing. The problem is formulated into a dynamic programming model and solved using an approximate dynamic programming algorithm. Piecewise linear functional approximations are used to estimate the value function of system states (i.e., snow plow trucks location over time). We apply our model and solution approach to a snow plow operation scenario for Lake County, Illinois. Numerical results show that the proposed algorithm can solve the problem effectively and outperforms a rolling-horizon heuristic solution.
Dynamic Snow Plow Fleet Management Under Uncertain Demand and Service Disruption
IEEE Transactions on Intelligent Transportation Systems ; 17 , 9 ; 2574-2582
01.09.2016
1061671 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Dynamic Snow Plow Fleet Management Under Uncertain Demand and Service Disruption
Online Contents | 2016
|Engineering Index Backfile | 1895
Engineering Index Backfile | 1895
|