In addition to the traditional consideration of technical and economical issues, staffing aspects are gaining in importance in the planning of production systems. The usual approach is to determine the manufacturing equipment according to the applied technology and then to define the necessary staff. However, this approach may lead to problems after the technical issues have been determined. On the other hand, a reversed approach, namely planning the technical resources based on the personnel to be implemented, is hardly practical.The following article deals with the topic of an integrated, simulation-supported procedure for the planning of personnel and technical resources based on a pre-determined order programme and the corresponding production logistical conditions. (C) 2009 Elsevier B.V. All rights reserved.

We address the problem of coordinating the activities of a team of agents in a dynamic, uncertain, nonlinear environment. Bounded rationality, bounded communication, subjectivity and distribution make it extremely challenging to find effective strategies. In these domains it is difficult to accurately predict whether potential policy modifications will lead to an increase in the value of the team reward. Our Predictability and Criticality Metrics (PCM) approach errs on the side of safety, and advocates considering policy modifications that are guaranteed to not harm the current policy, and uses simple metrics to choose from within that set a modification that increases the team reward. In the context of the DARPA Coordinators program, we show how the PCM approach yielded a system that significantly outperformed several competing approaches in an extensive independent evaluation.

The examination of human performance within industrial processes increasingly extends beyond the matter of resource implementation. In addition to traditional approaches to personnel assignment planning, new management methods for the preservation and further development of personnel are becoming established. The following paper discusses the effects and challenges related to computed-aided competence management. For this purpose it is indispensable to examine the notion of competence more in depth as well as the resulting competence management. Since as of yet no universally valid definition of competence exists, the following article discusses various facets of competence management and illustrates these by considering individual simulation-aided decision and planning tools for industrial process optimization.

Our Criticality-Sensitive Coordination (CSC) agents are designed to enhance the performance of a human-team working together in uncertain and dynamic settings by monitoring and adapting their plans as dictated by the evolution of the environment. Such situations model military scenarios such as a coordinated joint operations or enterprise settings such as multiple-project management. Among the many challenges in these situations are the large space of possible states due to uncertainty, the distributed / partial knowledge of current state and plan among the agents and the need to react in a timely manner to events that may not be in the original model. In fact, reaction alone is often insufficient as in environments where success depends on completing sequences of coupled actions, one needs to anticipate future difficulties and enable contingencies to alleviate potential hazards.

The examination of humans and their competence within manufacturing systems increasingly extends beyond the matter of resource employment. Even within highly automated systems, the mere consideration of the technical resource machinery is insufficient (Maline 1994; Zülch, Heitz and Schindele 1995). The following paper describes an approach to the consideration of manufacturing systems as well as processes based on competences related to worker qualifications. The already existing personnel structure and its qualification profile is thereby taken into account. The prospective comparison of equipment- and personnel-oriented solutions using multi-criterion prioritisation is thus used to effect a solution acceptable for both sides. The simulation procedure ESPE-IP was created with these research aspects in mind.

In this work, we address the problem of coordinating the distributed execution of plans and schedules by multiple agents subject to a number of different execution uncertainties. The coordination of multi-agent teams in uncertain, dynamic domains is a challenging problem requiring the fusion of techniques from many disciplines. We describe an approach based on the dynamic and selective use of a family of different problem-solving strategies that combine stochastic state estimation with repair-based and heuristic-guided planning and scheduling techniques. This approach is implemented as a cognitive problem-solving architecture that combines (i) a deliberative scheduler, which performs partially-centralized solution repair, (ii) an opportunistic scheduler, which locally optimizes resource utilization for plan enhancement, and (iii) a downgrader, which proactively guides the execution into regions of higher likelihood of success. This paper characterizes the complexity of the problem through examples and experiments, and discusses the advantages and effectiveness of the implemented solution.

Abstract : Respect is an effort to allow a problem solver to accept advice and exhibit learning. It is based on a high-level description of a problem solver that can be examined and manipulated by the problem-solver itself. Experiments are being conducted in such problem-solving arenas as meeting scheduling, type checking, and logistics planning. By specializing a general-purpose problem solver to a declarative problem specification, we obtain a procedure for that particular problem domain, which is not necessarily efficient. Because the initial problem solver is high-level, the information is explicitly available as to which operations can be reordered; reordering can lead to radical changes in the search space. In the meeting scheduling domain, this reordering can ensure that we schedule the scarce resources first (busy people, popular facilities). We have seen examples in which failed solutions can be examined automatically to suggest relaxation of constraints---a kind of problem reformulation. For example, if a scheduling problem is unsolvable, an examination of the search space can suggest that a trip be extended an additional day, or that some participants work an extra hour. The envisioned system accepts advice as to how to examine the search space.

We investigate the problem of keeping the plans of mul- tiple agents synchronized during execution. We assume that agents only have a partial view of the overall plan. They know the tasks they must perform, and know the tasks of other agents with whom they have direct depen- dencies. Initially, agents are given a schedule of tasks to perform together with a collection of contingency plans that they can engage during execution in case execu- tion deviates from the plan. During execution, agents monitor the status of their tasks, adjusting their local schedules as necessary and informing dependent agents about the changes. When agents determine that their schedule is broken or that a contingency schedule may be better, they engage in coordinating plan changes with other agents. We present a "dynamic partial central- ization" approach to coordination. When a unit detects a problem (task delay, inability to perform a task), it will dynamically form a cluster of the critically affected agents (a subset of all potentially affected agents). The cluster will elect a leader, who will retrieve all task and contingency plan information from the cluster members and compute a solution depending on the situation.

Abstract : Resource management is the key component of any real-time application. Traditional approaches to the development of embedded applications usually restrict their resource management capabilities to the level of the real-time scheduler implemented as an admissibility policy associated with a myopic dispatcher mechanism. The main problem with this approach is that they have very limited visibility of the actual levels of resource availability in the system. The non-functional aspect aspects of the architecture are completely ignored by these approaches. To overcome these limitations, system developers manually optimize the architectures for the particular application under consideration. These optimizations are not reusable and, once a new application is needed, a new architecture needs to be created.

: This report summarizes research performed towards the development of architectures and tools for mixed-initiative scheduling. CMU's approach is rooted in incremental constraint-based search procedures and draws on interactive visual interfaces to integrate user and system decision-making. The report first describes a successful application of CMU's approach to the problem of allocating aircraft and aircrews to airlift and tanker missions at the Air Mobility Command (AMC). The developed system, called the AMC Barrel Allocator, has been taken over by AMC and is now part of the operational air mobility planning system in AMC's Tanker/Airlift Control Center. CMU next considered progress made in the area of configurable scheduling systems. Specifically provided is an overview of the OZONE scheduling ontology, which was designed to provide a conceptual mapping from high-level domain analysis to construction of an executable scheduling model and support rapid application construction. The report also described CMU's work in 2D and 3D visualization of resource capacity constraints, aimed at early identification of mismatches between resource demand and supply. CMU illustrates its use in analyzing port throughput capacity in the context of strategic deployment planning. A technology integration experiment involving a second air campaign scheduling application of the incremental scheduling approach in a visionary, effects-based planning demonstration is then described. The report summarizes the results obtained in the development of core procedures for generating temporally flexible schedules which provide some measure of robustness in a dynamic execution environment.

Planware is an integrated development environment for the domain of complex planning and scheduling systems. Its design and implementation aim at supporting the entire planning and scheduling process including domain analysis and knowledge acquisition; application development and testing; and mixed-initiative, human-in-the-loop, plan and schedule computation. Based on principles of automatic software synthesis, Planware addresses the problem of maintaining the synchronization between evolving specifications, and the corresponding system implementation. Planware automatically generates optimized and specialized planning and scheduling code from high-level models of complex problems. Resources and tasks are uniformly modeled using a hierarchical state machine formalism that represents activities as states, and includes constructs for expressing constraints on states and transitions. The generator analyzes the state machine models to instantiate program schemas generating concrete implementations of backtrack search and constraint propagation algorithms. Coordination between resources and tasks is achieved through the use of services: tasks require services, and resources provide services. Planware’s scheduler generator component matches providers with requesters, and automatically generates the code necessary to verify and enforce, at schedule computation time, the service constraints imposed in the model. Planware’s user interface is based on Sun’s NetBeans platform and provides integrated graphic and text editors for modeling complex resource systems, automatically generating batch schedulers, and executing the generated schedulers on test data sets.

In this paper, we describe the Barrel Allocator, a scheduling tool developed for day-to-day allocation and management of airlift and tanker resources at the USAF Air Mobility Command (AMC). The system utilizes an incremental and configurable constraint-based search framework to provide a range of automated and semi-automated scheduling capabilities, including generating an initial solution to the fleet assignment problem, selective re-optimization of resource allocations to incorporate new higher priority missions while minimizing solution change, merging of previously planned missions to reduce non-productive flying time, and generation and synchronization of tanker missions to satisfy air refueling requirements. In situations where all mission requirements cannot be met, the system can generate and compare alternative constraint relaxation options. The current, version of Barrel Allocator will go into operational use at AMC as a module of Release 2.0 of AMC's Consolidated Air Mobility Planning System (CAMPS) in early 2000.

In this report we present an ontology for Multi-Modal Transportation Planning and Scheduling. We take, as our starting point, the previously developed OZONE scheduling ontology, which provides a general basis for formulating scheduling domain models. We extend this core framework as necessary to capture the essential characteristics and constraints of multi-modal transportation planning and scheduling, and then use this extended framework as a basis for elaborating concepts of particular relevance to transportation planning and scheduling. Though we define a fairly large base of transportation planning and scheduling terms, our intension has not been to produce an exhaustive domain ontology. Rather our primary goal has been to define the representational framework and ontological basis for comprehensive modeling and solution of multimodal transportation planning and scheduling problems.

In this paper, we examine the workflow management process from a scheduling perspective. Recognizing that effective workflow management requires an ability to efficiently allocate limited resources to tasks over time, we concentrate on characterizing this domain as a continuous distributed scheduling problem and on understanding the requirements and opportunities for providing workflow scheduling support within multi-agent environments. Our goals are twofold: (1) to relate the characteristics of the workflow management problem to scheduling models previously developed for other domains, and (2) to identify the issues and challenges surrounding application of previously developed scheduling technology to this problem.

In this paper, we consider the use of ontologies as a basis for structuring and simplifying the process of constructing domain-speciic problem-solving tools. We focus speciically on the task of scheduling. Though there is commonality in scheduling system requirements and design at several levels across application domains, diier-ent scheduling environments invariably present diierent challenges (e.g., diierent dominating constraints, diierent objectives, diierent domain structure, diierent sources of uncertainty, etc.), and hence we can expect high-performance application systems to require customized solutions. Unfortunately, the time and cost associated with such domain-speciic system development at present is typically quite large. Our work toward overcoming this application construction bottleneck has led to the development of OZONE, a toolkit for connguring constraint-based scheduling systems. A central component of OZONE is its scheduling on-tology, which deenes a reusable and extensi-ble base of concepts for describing and representing scheduling problems, domains and constraints. The OZONE ontology provides a framework for analyzing the information requirements of a given target domain, and a structural foundation for constructing an appropriate domain model. Through direct association of software component capabilities with concepts in the on-tology, the ontology promotes rapid connguration of executable systems and allows concentration of modeling eeort on those idiosyncratic aspects of the target domain. The OZONE ontology and toolkit represent a synthesis of extensive prior work in developing constraint-based scheduling models for a range of applications in manufacturing , space and transportation logistics. We rst motivate the use of ontologies as model building tools, establishing linkages to recent concepts in software engineering and proposing an extended view of ontologies that includes capability descriptions. We then describe our perspective on the structure of planning and scheduling domain models and summarize major components of current OZONE scheduling ontology. In recent years, the eld of software engineering has placed increasing emphasis on software reusability as a key to reducing the time and cost of application system construction and maintenance. Techniques for development and (re)use of software components have received wide attention and use (Biggerstaa & Perlis 1989; Krueger 1992), and tools that support system development from reusable building blocks are maturing Despite this activity, however, the systematic development of applications from components remains an open issue. One obstacle stems from the lack of communication and coordination between component developers (who must design for reuse) and component users (who design with reuse) (Becker & D az-Herrera 1994); overly complex components are diicult …