Partial-Order Support-Link Scheduling

Partial-order schedules are valued because they are flexible, and therefore more robust to unexpected delays. Previous work has indicated that constructing partial-order schedules by a two-stage method, in which a fixed-time schedule is first found and

Integrating Planning and Scheduling : A Constraint-based Approach

Automated decision making is one of the important problems of Artificial Intelligence (AI). Planning and scheduling are two sub-fields of AI that research automated decision making. The main focus of planning is on general representations of actions, causal reasoning among actions and domain-independent solving strategies. Scheduling generally optimizes problems with complex temporal and resource constraints that have simpler causal relations between actions. However, there are problems that have both planning characteristics (causal constraints) and scheduling characteristics (temporal and resource constraints), and have strong interactions between these constraints. An integrated approach is needed to solve this class of problems efficiently. The main contribution of this thesis is an integrated constraint-based planning and scheduling approach that can model and solve problems that have both planning and scheduling characteristics. In our representation problems are described using a multi-valued state variable planning language with explicit representation of different types of resources, and a new action model where each action is represented by a set of transitions. This action-transition model makes the representation of actions with delayed effects, effects with different durations, and the representation of complex temporal and resource constraints like time-windows, deadline goals, sequence-dependent setup times, etc simpler. Constraint-based techniques have been successfully applied to solve scheduling problems. Therefore, to solve a combined planning/scheduling problem we compile it into a CSP. This compilation is bounded by the number of action occurrences. The constraint model is based on the notion of “support” for each type of transition. The constraint model can be viewed as a system of CSPs, one for each state variable and resource, that are synchronized by a simple temporal network for action start times. Central to our constraint model is the explicit representation and maintenance of the precedence constraints between transitions on the same state variable or resource. We propose a branching scheme for solving the CSP based on establishing supports for transitions, which imply precedence constraints. Furthermore, we propose new propagation and inference techniques that infer precedence relations from temporal and mutex constraints, and infer tighter temporal bounds from the precedence constraints. The distinguishing feature of these inference and propagation techniques is that they not only consider the transitions and actions that are included in the plan but can also consider actions and transitions that are not yet included in or excluded from the plan. We conclude the thesis with a modeling case study of a complex satellite problem domain to demonstrate the effectiveness of our representation. This problem domain has action choices that are tightly coupled with temporal and resource constraints. We show that most of the complexities of this problem can be expressed in our representation in a simple and intuitive way

Resource-based Planning with Timelines

Real world planning applications typically involve making decisions that consumes limited resources, which requires both planning and scheduling. In this paper we propose a new approach that bridges the gap between planning and scheduling by explicitly modeling the problem in terms of resources, state variables and actions. We show that it is an intuitive way to formulate real world problems with complex constraints, and that solutions can be found by compiling the problem into a constraint satisfaction problem

Integrating Planning and Scheduling in a CP Framework: A Transition-based Approach

Many potential real-world planning applications are on the border of planning and scheduling. To handle the complex choices of actions and temporal and resource constraints of these problems we need to integrate planning and scheduling techniques. Here we propose a transition-based formulation of temporal planning problems, that enables us to represent features like deadlines, time windows, release times etc. in a simple way. We describe a CSP encoding of the transition-based formulation and its potential advantages in integrating planning and scheduling techniques

Integrating Planning and Scheduling in a CP Framework: A Transition-Based Approach

Many potential real-world planning applications are on the border of planning and scheduling. To handle the complex choices of actions and temporal and resource constraints of these problems we need to integrate planning and scheduling techniques. Here we propose a transition-based formulation of temporal planning problems, that enables us to represent features like deadlines, time windows, release times etc. in a simple way. We describe a CSP encoding of the transition-based formulation and its potential advantages in integrating planning and scheduling techniques

Partial-Order Support-Link Scheduling

Partial-order schedules are valued because they are flexible, and therefore more robust to unexpected delays. Previous work has indicated that constructing partial-order schedules by a two-stage method, in which a fixed-time schedule is first found and a partial order then lifted from it, is far more efficient than constructing them directly by a least-commitment partial-order scheduling algorithm. However, the two-stage method is limited to exploring only a fraction of the space of partial-order schedules, namely those that can be obtained from the given fixed-time schedule. We introduce a novel constraint formulation of partial-order scheduling, which establishes explicit resource-providing “links ” between activities instead of detecting and eliminating potential resource conflicts. We show that this yields an algorithm that is much faster than previous (precedence constraint posting) partialorder scheduling methods, and comparable to the two-stage method in terms of the quality and robustness of the schedules it finds. This algorithm is also complete, and because it searches the entire space of partial-order schedules, can be adapted to optimising different robustness criteria