A stochastic multia??period investment selection model to optimize strategic railway capacity planning

摘要

North American Freight Railroads are approaching the limits of practical capacity because of substantial future demand. In this research, we develop a Stochastic Multia??period Investment Selection Model (Sa??MISM) to assist railroads best allocate their capital investments in the longa??term strategic capacity planning process. The novel optimization framework uses stochastic programming and Benders decomposition and provides a means to cope with unfulfilled demand and demand uncertainty in a longa??term multia??period investment selection problem. Sa??MISM can determine which portions of a rail network need to be upgraded with what kind of expansion options at each defined period in the planning horizon. Experimental results show that the inclusion of demand uncertainty results in a better and more robust capacity plan. Using this decision support tool will help railroads maximize their return from capacity expansion projects and minimize the risk in strategic capacity planning subject to demand uncertainty. Copyright ?? 2012 John Wiley & Sons, Ltd.