Scheduling policies for the stochastic resource leveling problem

When uncertainties come into play, the leveled baseline schedule obtained by solving the deterministic resource leveling problem can hardly be executed as planned and this schedule may even become infeasible. In addition, traditional stochastic methods may also suffer from not being able to produce satisfactorily leveled schedules. Therefore, there is a pressing need for new procedures that are capable of dealing with resource leveling subject to uncertainties. We study the resource leveling problem subject to activity durations uncertainty where the usage of renewable resources needs to be leveled over time. We present two heuristics for producing scheduling policies with the objective of minimizing the expected sum of the weighted coefficient of variation of the resource usage. Our two heuristics represent two different ways of tackling the stochastic resource leveling problem. The first heuristic, a modified version of the Burgess and Killebrew leveling procedure, obtains a scheduling policy by solving the deterministic equivalent of the stochastic resource leveling problem. The second one, a simulation-based tabu search procedure, directly works with the stochastic resource leveling problem. Computational experiments are conducted on the well-known PSPLIB J90 instances.

Publication year:2014

BOF-keylabel:yes

IOF-keylabel:yes

BOF-publication weight:1

CSS-citation score:1

Authors:International

Authors from:Higher Education

Accessibility:Closed