Sizing industrial rail car fleets using discrete-event simulation

DuPont has many products that use rail cars in various portions of their supply chains. Often these cars are used to deliver final products to a variety of customers at different geographical locations. In many cases it is difficult to optimally size these fleets, since the underlying system is complex, dynamic, and involves random variables. This paper describes how DuPont has used discrete-event simulation (“DES”) to optimally size an industrial rail car fleet used to deliver final products to customers. It explains why it is important to DuPont to optimize the size of our rail car fleets; how such fleets are sized without DES; the value of DES in modeling one particular rail car system; and some of the lessons from building such DES models