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Scheduling problems in manufacturing, logistics and project management havefrequently been modeled using the framework of Resource Constrained ProjectScheduling Problems with minimum and maximum time lags (RCPSP/max). Due to theimportance of these problems, providing scalable solution schedules forRCPSP/max problems is a topic of extensive research. However, all existingmethods for solving RCPSP/max assume that durations of activities are knownwith certainty, an assumption that does not hold in real world schedulingproblems where unexpected external events such as manpower availability,weather changes, etc. lead to delays or advances in completion of activities.Thus, in this paper, our focus is on providing a scalable method for solvingRCPSP/max problems with durational uncertainty. To that end, we introduce therobust local search method consisting of three key ideas: (a) Introducing andstudying the properties of two decision rule approximations used to computestart times of activities with respect to dynamic realizations of thedurational uncertainty; (b) Deriving the expression for robust makespan of anexecution strategy based on decision rule approximations; and (c) A robustlocal search mechanism to efficiently compute activity execution strategiesthat are robust against durational uncertainty. Furthermore, we also provideenhancements to local search that exploit temporal dependencies betweenactivities. Our experimental results illustrate that robust local search isable to provide robust execution strategies efficiently.

Robust Local Search for Solving RCPSP/max with Durational Uncertainty