Integrating planning and scheduling in workflow domains

One of the main obstacles in applying AI planning techniques to real problems is the difficulty to model the domains. Usually, this requires that people that have developed the planning system carry out the modeling phase since the representation depends very much on a deep knowledge of the internal working of the planning tools. On some domains such as business process reengineering (BPR), there has already been work on the definition of languages that allow non-experts entering knowledge on processes into the tools. We propose here the use of one of such BPR languages to enter knowledge on the organisation processes to be used by planning tools. Then, planning tools can be used to semi-automatically generate business process models. As instances of this domain, we will use the workflow modeling tool SHAMASH, where we have exploded its object oriented structure to introduce the knowledge through its user-friendly interface and, using a translator transform it into predicate logic terms. After this conversion, real models can be automatically generated using a planner that integrates planning and scheduling, IPSS. We present results in a real workflow domain, the telephone installation (TI) domain.The SHAMASH project has being carried out in the course of the R&D project funded by the Esprit Program of the Commission of the European Communities as project number 25491. A complementary grant was given by the Spanish research commission, CICYT, under project number TIC98-1847-CE. We thank the partners of this project, who have originated and contributed to the ideas reported: UF (Unio´n Fenosa), SAGE (Software AG Espan˜ a), SEMA GROUP sae, UC3M (Universidad Carlos III de Madrid), WIP (Wirstchaft und infrastruktur & Co Planungs KG), and EDP (Electricidade de Portugal). We would specially like to thank all the UC3M team, the PLANET people and Paul Kearney (BT). Through talks with him we have outlined many ideas. This work has also been partially funded by grant MCyT TIC2002-04146-C05-05 and the UAH project PI2005/084.Publicad

The APT/ERE planning and scheduling manifesto

The Entropy Reduction Engine, ERE project, is focusing on the construction of integrated planning and scheduling systems. Specifically, the project is studying the problem of integrating planning and scheduling in the context of the closed loop plan use. The results of this research are particularly relevant when there is some element of dynamism in the environment, and thus some chance that a previously formed plan will fail. After a preliminary study of the APT management and control problem, it was felt that it presents an excellent opportunity to show some of the ERE Project's technical results. Of course, the alignment between technology and problem is not perfect, so planning and scheduling for APTs presents some new and difficult challenges as well

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

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

Planning and Scheduling of Business Processes in Run-Time: A Repair Planning Example

Over the last decade, the efficient and flexible management of business processes has become one of the most critical success aspects. Furthermore, there exists a growing interest in the application of Artificial Intelligence Planning and Scheduling techniques to automate the production and execution of models of organization. However, from our point of view, several connections between both disciplines remains to be exploited. The current work presents a proposal for modelling and enacting business processes that involve the selection and order of the activities to be executed (planning), besides the resource allocation (scheduling), considering the optimization of several functions and the reach of some objectives. The main novelty is that all decisions (even the activities selection) are taken in run-time considering the actual parameters of the execution, so the business process is managed in an efficient and flexible way. As an example, a complex and representative problem, the repair planning problem, is managed through the proposed approach.Ministerio de Ciencia e Innovación TIN2009-13714Junta de Andalucía P08-TIC-0409

OCL Plus:Processes and Events in Object-Centred Planning

An important area in AI Planning is the expressiveness of planning domain specification languages such as PDDL, and their aptitude for modelling real applications. This paper presents OCLplus, an extension of a hierarchical object centred planning domain definition language, intended to support the representation of domains with continuous change. The main extension in OCLplus provides the capability of interconnection between the planners and the changes that are caused by other objects of the world. To this extent, the concept of event and process are introduced in the Hierarchical Task Network (HTN), object centred planning framework in which a process is responsible for either continuous or discrete changes, and an event is triggered if its precondition is met. We evaluate the use of OCLplus and compare it with a similar language, PDDL+

Decomposability and scalability in space-based observatory scheduling

In this paper, we discuss issues of problem and model decomposition within the HSTS scheduling framework. HSTS was developed and originally applied in the context of the Hubble Space Telescope (HST) scheduling problem, motivated by the limitations of the current solution and, more generally, the insufficiency of classical planning and scheduling approaches in this problem context. We first summarize the salient architectural characteristics of HSTS and their relationship to previous scheduling and AI planning research. Then, we describe some key problem decomposition techniques supported by HSTS and underlying our integrated planning and scheduling approach, and we discuss the leverage they provide in solving space-based observatory scheduling problems

OptBPPlanner: Automatic Generation of Optimized Business Process Enactment Plans

Unlike imperative models, the specifi cation of business process (BP) properties in a declarative way allows the user to specify what has to be done instead of having to specify how it has to be done, thereby facilitating the human work involved, avoiding failures, and obtaining a better optimization. Frequently, there are several enactment plans related to a specifi c declarative model, each one presenting specifi c values for different objective functions, e.g., overall completion time. As a major contribution of this work, we propose a method for the automatic generation of optimized BP enactment plans from declarative specifi cations. The proposed method is based on a constraint-based approach for planning and scheduling the BP activities. These optimized plans can then be used for different purposes like simulation, time prediction, recommendations, and generation of optimized BP models. Moreover, a tool-supported method, called OptBPPlanner, has been implemented to demonstrate the feasibility of our approach. Furthermore, the proposed method is validated through a range of test models of varying complexity.Ministerio de Ciencia e Innovación TIN2009-1371

What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle