Prediction of the demand of the railway sleepers: A simulation model for replacement strategies

This paper describes the development of a simulation model to assess the inventory requirements of alternative rail sleeper replacement strategies. The main aim of the model is to determine the optimal replacement strategy, given replacement costs and resultant train operating cost benefits. We consider the replacement problem under the following assumptions: The time to failure under constant stress follows a Weibull distribution and the scale parameter is a function of stress level and the three stress levels under normal (all adjacent units are good), medium-stress (one adjacent unit has failed) and high-stress conditions (two adjacent units are failed) are considered. The cumulative exposure model is used to model the failure distributions. The operational cost per unit time depends on the maximum number of consecutive failed units. The replacement cost consists of the fixed cost and variable cost proportional to the number of units replaced. A finite horizon is considered and total expected cost is a criterion for comparing the proposed policies. The model has been tested using rail system data and the results are presented in this paper.