A combination of different resource management policies in a multi-project environment

Multi-project problem environments are defined according to the way resources are managed in the problem environment, which is called the resource management policy (RMP) in this study. Different resource management policies can be defined according to the characteristics of the projects and/or resources in the problem environment. The most common RMP encountered in the multi-project scheduling literature is the resource sharing policy (RSP), where resources can be shared among projects without any costs or limitations. This policy can be seen as an extreme case since there is a strong assumption of unconstrained resource sharing. Another RMP can be defined as the other extreme such that resources cannot be shared among projects, which is called the resource dedication policy (RDP). The last RMP considered in this study is between these two policies where resources are dedicated but can be transferred among projects when a project finishes, the dedicated resources to this project can be transferred to another one starting after the finish of the corresponding project. This RPM is called the resource transfer policy (RTP). In this study we investigate a problem environment where all these three types of RPM are present. Additionally, the general resource capacities are taken as decision variables that are constrained by a given general budget. We call this multi-project environment as the Generalized Resource Portfolio Problem (GRPP). We have investigated this problem and proposed an iterative solution approach based on exact solution methods which determines the general resource capacities from the budget, resource dedications, resource sharing and resource transfer decisions and schedules the individual projects. Computational results for over forty test problems are reported

Crisis action planning and replanning using SIPE-2

Rome Laboratory and DARPA are jointly sponsoring an initiative to develop the next generation of AI planning and scheduling technology focused on military operations planning, especially for crisis situations. SRI International has demonstrated their knowledge-based planning technology in this domain with a system called SOCAP, System for Operations Crisis Action Planning. SOCAP's underlying power comes from SIPE-2, a hierarchical, domain-independent, nonlinear AI planner also developed at SRI. This paper discusses the features of SIPE-2 that made it an ideal choice for military operations planning and which contributed greatly to SOCAP's success

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

Bargaining Between Goals

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-70-A-0362-0003.Bargaining is a process used to modify conflicting demands on an expendable resource so that a satisfactory allocation can be made. In this paper, I consider the design of a bargaining system to handle the problem of scheduling an individual's weekly activities and appointments. The bargaining system is based on the powerful reasoning strategy of producing a simplified linear plan by considering the various constraints independently and then debugging the resulting conflicts.MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agenc

A platform for enterprise-wide healthcare knowledge management

The importance of effective information and knowledge management in enterprises has spurred the development of numerous information and knowledge management software.Whilst emphasis is placed on effective document management, the essence of knowledge management is diluted as the focus is presently on managing uninterpreted data and information in document-type formats.To address this issue of the lack of true knowledge management in enterprises, especially in healthcare enterprises, we propose a Platform for Enterprise-Wide Healthcare Knowledge Management (KM-Platform).This platform is made up of two suites of applications and services, i.e. the Intelligent Agent-Based Knowledge Management Application Suite and the Strategic Visualisation, Planning and Coalition Formation Service Suite

An Ontological formalization of the planning task

In this paper we propose a generic task ontology, which formalizes the space of planning problems. Although planning is one of the oldest researched areas in Artificial Intelligence and attempts have been made in the past at developing task ontologies for planning, these formalizations suffer from serious limitations: they do not exhibit the required level of formalization and precision and they usually fail to include some of the key concepts required for specifying planning problems. In con-trast with earlier proposals, our task ontology formalizes the nature of the planning task independently of any planning paradigm, specific domains, or applications and provides a fine-grained, precise and comprehensive characterization of the space of planning problems. Finally, in addition to producing a formal specification we have also operationalized the ontology into a set of executable definitions, which provide a concrete reusable resource for knowledge acquisition and system development in planning applications

Reformulation in planning

Reformulation of a problem is intended to make the problem more amenable to efficient solution. This is equally true in the special case of reformulating a planning problem. This paper considers various ways in which reformulation can be exploited in planning