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

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

A planning approach to the automated synthesis of template-based process models

The design-time specification of flexible processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To tackle these issues, we propose an approach for the automated synthesis of a library of template-based process models that achieve goals in dynamic and partially specified environments. The approach is based on a declarative problem definition and partial-order planning algorithms for template generation. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially specified contextual environments. As running example, a disaster response scenario is given. The approach is backed by a formal model and has been tested in experiment

Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

Allocation of Land at the Rural-Urban Fringe Using a Spatially-Realistic Ecosystem Constraint

Development in rural-urban fringe communities is increasing with the potential to damage healthy ecosystems and endanger the long-term persistence of resident flora and fauna. The environmental impacts of development include loss, degradation, and fragmentation of wildlife habitat, increased air and water pollution, increased soil erosion, and decreased aesthetic appeal of the landscape. Current land use policies rarely incorporate features of landscape-scale ecosystem health. This paper develops a model that combines ecological and economic constructs to determine the optimal allocation of development across a spatially-realistic landscape. The land allocation model establishes links between long-term metapopulation persistence and development through an ecosystem constraint. A social planner seeks to maximize the benefits of development while guaranteeing a certain likelihood of long-term metapopulation persistence across the landscape that accounts for the changes to habitat patches and species dispersal success brought about by development. It is shown that in an economically homogeneous environment, the allocation of land to developed uses is determined solely by ecological elements (landscape structure and species parameters). The amount of land remaining in each habitat patch is the same regardless of their initial sizes or initial levels of development. The cost to society of meeting the ecological objective for metapopulation persistence depends on the land rent, the level of the safe-minimum-standard, the area of the landscape management unit, the distance between habitat patches, the dispersal ability of the focal species, and the species-specific area scaling parameter. Cost is not affected by the initial conditions of the habitat patches or the amount of development that has already taken place in the landscape management unit. When heterogeneity is introduced, the allocation of land is also determined by the differential land rents. More development occurs in habitat patches and landscape management units with higher land rents compared with the homogeneous case. In the heterogeneous land use case, where different land uses have different intensities of damages, the development intensity parameters are factors in the solution with more development occurring in areas zoned for less intensive land uses and the cost to society of achieving the ecological objective is a function of initial habitat patch sizes.Land Economics/Use,

XSRL: An XML web-services request language

One of the most serious challenges that web-service enabled e-marketplaces face is the lack of formal support for expressing service requests against UDDI-resident web-services in order to solve a complex business problem. In this paper we present a web-service request language (XSRL) developed on the basis of AI planning and the XML database query language XQuery. This framework is designed to handle and execute XSRL requests and is capable of performing planning actions under uncertainty on the basis of refinement and revision as new service-related information is accumulated (via interaction with the user or UDDI) and as execution circumstances necessitate change

UCTx: a multi-agent system to assist a transplant coordination unit

We present a system called UCTx, designed to model and automate some of the tasks performed by a Transplant Coordination Unit (UCTx) inside a Hospital. The aim of this work is to show how a multi-agent approach allows us to describe and implement the model, and how UCTx is capable of dealing with another multi-agent (Carrel, an Agent Mediated Institution for the exchange of Human Tissues among Hospitals for Transplantation) in order to meet its own goals, acting as the representative of the hospital in the negotiation. As an example we introduce the use of this Agency in the case of Cornea Transplantation.Postprint (published version

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