Robust Local Search for Solving RCPSP/max with Durational Uncertainty

Abstract

Scheduling problems in manufacturing, logistics and project management have frequently been modeled using the framework of Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max). Due to the importance of these problems, providing scalable solution schedules for RCPSP/max problems is a topic of extensive research. However, all existing methods for solving RCPSP/max assume that durations of activities are known with certainty, an assumption that does not hold in real world scheduling problems 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 solving RCPSP/max problems with durational uncertainty. To that end, we introduce the robust local search method consisting of three key ideas: (a) Introducing and studying the properties of two decision rule approximations used to compute start times of activities with respect to dynamic realizations of the durational uncertainty; (b) Deriving the expression for robust makespan of an execution strategy based on decision rule approximations; and (c) A robust local search mechanism to efficiently compute activity execution strategies that are robust against durational uncertainty. Furthermore, we also provide enhancements to local search that exploit temporal dependencies between activities. Our experimental results illustrate that robust local search is able to provide robust execution strategies efficiently

Mechanism Design for Strategic Project Scheduling

“…Overall, the proposed solution framework for solving strategic RCPSP contains three processes: task allocating, task executing and payment making. We build on the idea of generating POSs for RCPSP/max using a flow-based continuous time linear model in [Fu et al, 2016; and provide an MILP formulation in Table 1 to determine the optimal allocation and POS. Given the declarations from agents, temporal and resource constraints among tasks, the objective of the MILP is to generate the best task allocation and execution policy POS, where 'best' here is characterized with respect to maximizing welfare for all agents based on the revealed types.…”

“…If V is linear or quadratic, the model can be solved using solvers like CPLEX. In fact, as indicated in [Fu et al, 2016], problems with up to 30 activities can be executed efficiently when minimizing makespan. To summarize, the overall flow of the strategic RCPSP works as follows: (i) Agents bid for tasks of interest by reporting certain types.…”

“…The focus is on minimizing the time taken to complete a set of tasks pertaining to a common project within the context of resource constraints. Given the NP-Hard complexity of solving RCPSPs, existing research has focussed on methods for generating high quality solutions efficiently [Vanhoucke and Coelho, 2016;Schutt et al, 2013;Fu et al, 2012].…”

Robustness and stability in dynamic constraint satisfaction problems

“…One of the most common form of changes in this type of problems is time delays. In (Fu et al, 2012), the authors stated that unexpected external events such as manpower availability, weather changes, etc. lead to delays or advances in completion of activities in scheduling problems.…”

Solving the Resource Constrained Project Scheduling Problem using the parallel Tabu Search designed for the CUDA platform

“…The Resource Constrained Project Scheduling Problem (RCPSP), which has a wide range of applications in logistics, manufacturing and project management [1], is a universal and well-known problem in the operations research domain. The problem can be briefly described using a set of activities and a set of precedence constraints describing the relationships among activities.…”