Evelien van der Hurk
3 December 2014
Passenger Guidance and Rolling Stock Rescheduling under Uncertainty for Disruption Management
In this paper we investigate the potential benefits of providing personalized travel advice to the passengers in case of major disruptions with an uncertain duration in public transport. The advice is intended to help passengers plan their journey given the uncertain duration. Moreover, knowing the capacity bottlenecks, the operator can help the passengers to avoid the overcrowded part of the network. By combining the travel advice with appropriate decisions on the rolling stock circulation, one expects to decrease the overall passenger delays.
We propose a new optimization based approach to simultaneously provide personalized travel advice and an updated rolling stock schedule. Our computational tests on realistic instances of Netherlands Railways (NS) indicate that the addition of the travel advice effectively improves the service quality to the passengers.
4 December 2014
Spare Parts Inventory Control Under Markov Modulated Supply Risk
Spare parts supply chains are highly dependent on the dynamics of their installed bases. A varying number of capital products in use strongly affects the nonstationary supply-side risk especially towards the end-of-life of capital products. This supply-side risk presents itself through stochastic lead times coupled with the risk of losing the supplier permanently, which we call supply failure. We consider an exogenous Markov chain modulating random lead time and supply failure probabilities to capture the nonstationarity in supply-side risk. Assuming that no order crossovers occur, we prove that base stock policy is optimal and under certain conditions optimal base stock levels present monotonicity properties. We conduct an impact study to understand the value of considering stochastic lead times and supply failure risk in inventory control. Our results indicate that considering supply-side risk can reduce total discounted costs by up to 51% and increase service levels by up to 21%.
Marjan van den Akker from (Department of Information and Computing Sciences, Utrecht University)
12 December 2014
Using column generation to solve parallel machine scheduling problems with minmax objective functions.
We present a solution framework for a number of scheduling problems in which the goal is to find a feasible schedule that minimizes some objective function of the minimax type on a set of parallel, identical machines, subject to release dates, deadlines, and/or generalized precedence constraints. After having derived the lower bound on the objective function by column generation, we try to find a matching upper bound by identifying a feasible schedule with objective function value equal to this lower bound. Our computational results show that our lower bound is so strong that this is almost always possible. We present applications to resource constrained project scheduling and to crew assignment within vehicle routing.