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In this paper, we describe ASPEN (Automated Planning/Scheduling Environment), a modular, reconfigurable application framework which is capable of supporting a wide variety of planning and scheduling applications.
New and innovative software technology is presented that provides a cost effective bridge for smoothly transitioning prototype software, in the field of planning and scheduling, into an operational environment. Specifically, this technology mixes the flexibility and human design efficiency of dynamic data typing with the rigor and run-time efficiencies of static data typing. This new technology provides a very valuable tool for conducting the extensive, up-front system prototyping that leads to specifying the correct system and producing a reliable, efficient version that will be operationally effective and will be accepted by the intended users.
The challenging timeline for DARPA's Orbital Express mission demanded a flexible, responsive, and (above all) safe approach to mission planning. Because the mission was a technology demonstration, pertinent planning information was learned during actual mission execution. This information led to amendments to procedures, which led to changes in the mission plan. In general, we used the ASPEN planner scheduler to generate and validate the mission plans. We enhanced ASPEN to enable it to reason about uncertainty. We also developed a model generator that would read the text of a procedure and translate it into an ASPEN model. These technologies had a significant impact on the success of the Orbital Express mission.
Scheduling considerations in the deployment of transportable laser ranging systems (TLRS) in 1985 are discussed. The new TLRS units will be field tested in North America and Europe first before being sent to remote Mediterranean and South American sites. Six weeks' residence will be required for 600 hr of measurements at each site, although a second crew could bring the necessary time down to four weeks. The surveys are set for periods of the year with the greatest incidence of clear skies. Twelve sites have been selected in the Mediterranean area, although no weather data have been acquired. Site selections are constrained by the need for accessible roads, telephones, and the stability of the terrain. Details of the marker monument layouts are provided.
In 1992, NASA made the decision to evolve a Consolidated Planning System (CPS) by adding the Space Transportation System (STS) requirements to the Space Station Freedom (SSF) planning software. This paper describes this evolutionary process, which began with a series of six-month design-build-test cycles, using a domain-independent architecture and a set of developmental tools known as the Advanced Scheduling Environment. It is shown that, during these tests, the CPS could be used at multiple organizational levels of planning and for integrating schedules from geographically distributed (including international) planning environments. The potential for using the CPS for other planning and scheduling tasks in the SSF program is being currently examined.
The coordination of interconnected elements across the different layers of the supply chain is essential for all industrial processes and the key to optimal decision-making. Yet, the modeling and optimization of such interdependent systems are still burdensome. Here we address the simultaneous modeling and optimization of medium-term planning and short-term scheduling problems under demand uncertainty using mixed-integer bi-level multi-follower programming and data-driven optimization. Bi-level multi-follower programs model the natural hierarchy between different layers of supply chain management holistically, while scenario analysis and data-driven optimization allow us to retrieve the guaranteed feasible solutions of the integrated formulation under various demand considerations. We address the data-driven optimization of this challenging class of problems using the DOMINO framework, which was initially developed to solve single-leader single-follower bi-level optimization problems to guaranteed feasibility. This framework is extended to solve single-leader multi-follower stochastic formulations and its performance is characterized by well-known single and multi-product process scheduling case studies. Through our data-driven algorithmic approach, we present guaranteed feasible solutions to linear and nonlinear mixed-integer bi-level formulations of simultaneous planning and scheduling problems and further characterize the effects of the scheduling level complexity on the solution performance, which spans over several hundred continuous and binary variables, and thousands of constraints.
The Y-12 National Complex (Y-12) site has numerous aging facilities that are crucial to the Department of Energy and the national security strategy for the nation. Y-12’s commitment to safety and regulatory compliance is of the highest importance. The commitment to meet the national security mission also creates additional rigor and complexity to the everyday maintenance and planning process. Y-12 is a collection of many facilities, both old and new, nestled between two ridges in Oak Ridge, TN. Y-12 was made with the short-term focus of ending “The Great War” through the creation of the worlds’ first atomic bomb. Almost eighty (80) years have passed since the groundbreaking, with the mission of the site changing from decade to decade. While the mission has changed, the way Y-12 employees continuously meet the challenge has not. The site was created to react and overcome; Y-12 still takes pride in the ability to react and overcome. The difference is the site is no longer ignorant to the need for a better way to manage the aging facilities and infrastructure. Shear willpower and determination was once the way to reach the objectives, but as a wise man once said, “Work smarter, not harder.” The business case for change started within the senior leadership at Y-12. A team of managers sat down and dictated objectives to provide a clear scope for the maintenance planning and scheduling optimization team, to include our Eruditio integrated blended learning coaches. In addition to providing the direction, they also made themselves available for escalation of issues in the event the team ran in to road blocks.
This article describes a planning method applicable to agents with great perception and decision-making capabilities and the ability to communicate with other agents. Each agent has a task to fulfill allowing for the actions of other agents in its vicinity. Certain simultaneous actions may cause conflicts because they require the same resource. The agent plans each of its actions and simultaneously transmits these to its neighbors. In a similar way, it receives plans from the other agents and must take account of these plans. The planning method allows us to build a distributed scheduling system. Here, these agents are robot vehicles on a highway communicating by radio. In this environment, conflicts between agents concern the allocation of space in time and are connected with the inertia of the vehicles. Each vehicle made a temporal, spatial, and situated reasoning in order to drive without collision. The flexibility and reactivity of the method presented here allows the agent to generate its plan based on assumptions concerning the other agents and then check these assumptions progressively as plans are received from the other agents. A multi-agent execution monitoring of these plans can be done, using data generated during planning and the multi-agent decision-making algorithm described here. A selective backtrack allows us to perform incremental rescheduling.
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The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting.
NASA's Hubble Space Telescope (HST) is a major astronomical facility that was launched in April, 1990. In late 1993, the first of several planned servicing missions refurbished the telescope, including corrections for a manufacturing flaw in the primary mirror. Orbiting above the distorting effects of the Earth's atmosphere, the HST provides an unrivaled combination of sensitivity, spectral coverage and angular resolution. The HST is arguably the most complex scientific observatory ever constructed and effective use of this valuable resource required novel approaches to astronomical observation and the development of advanced software systems including techniques to represent scheduling preferences and constraints, a constraint satisfaction problem (CSP) based scheduler and a rule based planning system. This paper presents a discussion of these systems and the lessons learned from operational experience.
The Mars Exploration Rovers Mars '03 mission is one of NASA's most ambitious science missions to date. The rovers will be launched in the summer of 2003 with each rover carrying instruments to conduct remote and in-situ observation to elucidate the planet's past climate, water activity, and habitability. Science is the primary driver of MER and, as a consequence, making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this critically, the MER project has selected MAPGEN (Mixed-Initiative Activity Plan GENerator) as an activity planning tool. MAPGEN combines two exiting systems, each with a strong heritage: APGEN the Activity Planning tool from the Jet Propulsion Laboratory and the Europs Planning/Scheduling system from NASA Ames Research Center. This paper discusses the issues arising from combining these tools in the context of this mission.
Programing Language for Allocation and Network Scheduling, Plans, handles many scheduling requirements of Space Shuttle program. Also generalized, high-level tree manipulation language.
An autonomous spacecraft must balance long-term and short-term considerations. It must perform purposeful activities that ensure long-term science and engineering goals are achieved and ensure that it maintains resource margins.
This paper discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences from high-level goals using AI-based planning software.