Planning for conjunctive goals
Artificial Intelligence
Scheduling project networks with resource constraints and time windows
Annals of Operations Research
O-Plan: the open planning architecture
Artificial Intelligence
Generating feasible schedules under complex metric constraints
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Artificial Intelligence - Special volume on planning and scheduling
A Constraint-Based Method for Project Scheduling with Time Windows
Journal of Heuristics
Automated Planning and Scheduling for Goal-Based Autonomous Spacecraft
IEEE Intelligent Systems
Computing the Envelope for Stepwise-Constant Resource Allocations
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Greedy Algorithms for the Multi-capacitated Metric Scheduling Problem
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
From precedence constraint posting to partial order schedules: A CSP approach to Robust Scheduling
AI Communications - Constraint Programming for Planning and Scheduling
Exploration of the robustness of plans
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Exploiting temporal flexibility to obtain high quality schedules
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Planning with sharable resource constraints
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Constraint-based methods for scheduling discretionary services
AI Communications
Robustness for a single railway line: Analytical and simulation methods
Expert Systems with Applications: An International Journal
Robust local search for solving RCPSP/max with durational uncertainty
Journal of Artificial Intelligence Research
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Goal separation is often a fruitful approach when solving complex problems. It provides a way to focus on relevant aspects in a stepwise fashion and hence bound the problem solving scope along a specific direction at any point. This work applies goal separation to the problem of synthesizing robust schedules. The problem is addressed by separating the phase of problem solution, which may pursue a standard optimization criterion (e.g., minimal makespan), from a subsequent phase of solution robustification in which a more flexible set of solutions is obtained and compactly represented through a temporal graph, called a Partial Order Schedule ( $\mathcal{POS}$ ). The key advantage of a $\mathcal{POS}$ is that it provides the capability to promptly respond to temporal changes (e.g., activity duration changes or activity start-time delays) and to hedge against further changes (e.g., new activities to perform or unexpected variations in resource capacity). On the one hand, the paper focuses on specific heuristic algorithms for synthesis of $\mathcal{POS}$ s, starting from a pre-existing schedule (hence the name Solve-and-Robustify). Different extensions of a technique called chaining, which progressively introduces temporal flexibility into the representation of the solution, are introduced and evaluated. These extensions follow from the fact that in multi-capacitated resource settings more than one $\mathcal{POS}$ can be derived from a specific fixed-times solution via chaining, and carry out a search for the most robust alternative. On the other hand, an additional analysis is performed to investigate the performance gain possible by further broadening the search process to consider multiple initial seed solutions. A detailed experimental analysis using state-of-the-art rcpsp/max benchmarks is carried out to demonstrate the performance advantage of these more sophisticated solve and robustify procedures, corroborating prior results obtained on smaller problems and also indicating how this leverage increases as problem size is increased.