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Minimizing Conflicts: A Heuristic Repair Method for ConstraintSatisfaction and Scheduling Problems
 J. ARTIFICIAL INTELLIGENCE RESEARCH
, 1993
"... This paper describes a simple heuristic approach to solving largescale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a valueorder ..."
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Cited by 404 (6 self)
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This paper describes a simple heuristic approach to solving largescale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a valueordering heuristic, the minconflicts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of different search strategies. We demonstrate empirically that on the nqueens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. We also describe a scheduling application where the approach has been used successfully. A theoretical analysis is presented both to explain why this method works well on certain types of problems and to predict when it is likely to be most effective.
Spike: Intelligent scheduling of hubble space telescope observations
 Intelligent Scheduling
, 1994
"... ..."
Minimizing con icts: a heuristic repair methodfor constraint satisfaction andscheduling problems
 Artif. Intell
, 1992
"... Abbreviated Title: \Minimizing Con icts: A Heuristic Repair Method" This paper describes a simple heuristic approach to solving largescale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through th ..."
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Cited by 33 (1 self)
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Abbreviated Title: \Minimizing Con icts: A Heuristic Repair Method" This paper describes a simple heuristic approach to solving largescale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by avalueordering heuristic, the mincon icts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of di erent search strategies. We demonstrate empirically that on the nqueens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. We also describe a scheduling application where the approach has been used successfully. A theoretical analysis is presented both to explain why this method works well on certain types of problems and to predict when it is likely to be One of the most promising general approaches for solving combinatorial search problems is to generate an
Analyzing a Heuristic Strategy for ConstraintSatisfaction and Scheduling
 in Intelligent Scheduling
, 1994
"... This paper describes a simple heuristic approach to solving largescale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a valueordering ..."
Abstract

Cited by 27 (3 self)
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This paper describes a simple heuristic approach to solving largescale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a valueordering heuristic, the minconflicts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of different search strategies. On the nqueens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. The technique has also been used for scheduling the Hubble Space telescope. A theoretical analysis is presented both to explain why this method works well on certain types of problems and to predict when it is likely to be most effective. 1 Introduction One of the most promising general approaches for solving combinatorial search problems is to generate an initial...
Artificial Intelligence Approaches to Spacecraft Scheduling
 Proc. ESA Workshop on Artificial Intelligence Applications for Space Projects, ESTEC
, 1988
"... Summary: The problem of optimal spacecraft scheduling is both important and difficult. Efficient utilization of spacecraft resources is essential, but the accompanying scheduling problems are often computationally intractable and are difficult to approximate because of the presence of numerous inter ..."
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Cited by 1 (1 self)
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Summary: The problem of optimal spacecraft scheduling is both important and difficult. Efficient utilization of spacecraft resources is essential, but the accompanying scheduling problems are often computationally intractable and are difficult to approximate because of the presence of numerous interacting constraints. We have applied artificial intelligence techniques to the problem of scheduling astronomical observations and other spacecraft activities for the NASA/ESA Hubble Space Telescope (HST). This presents a particularly challenging problem since a yearlong observing program can contain some tens of thousands of exposures which are subject to a large number of scientific, operational, spacecraft, and environmental constraints. We have developed new techniques for machine reasoning about scheduling constraints and goals, especially in cases where uncertainty is an important scheduling consideration and where resolving conflicts among conflicting preferences is essential. These techniques have been utilized in a set of workstationbased scheduling tools for HST. Graphical displays of activities, constraints, and schedules are an important feature of the system. Highlevel scheduling strategies using both rulebased and neural network approaches have been developed. While the specific constraints we have implemented are those most relevant to HST, the framework we have developed is far more general and could easily handle other kinds of scheduling problems. This paper describes the concept and implementation of the HST scheduling tools and how they could be adapted to other domains. 1.
Dissertation Abstract A Predictive Model for Satisfying Conflicting Objectives in Scheduling Problems
"... The economic viability of a manufacturing organization depends on its ability to maximize customer services; maintain efficient, lowcost operations; and minimize total investment. These objectives conflict with one another and, thus, are difficult to achieve on an operational basis. Much of the wor ..."
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The economic viability of a manufacturing organization depends on its ability to maximize customer services; maintain efficient, lowcost operations; and minimize total investment. These objectives conflict with one another and, thus, are difficult to achieve on an operational basis. Much of the work in the area of automated scheduling systems recognizes this problem but does not address it effectively. The work presented by this Ph.D. dissertation was motivated by the desire to generate good, costeffective schedules in dynamic and stochastic manufacturing environments (Berry 1991). 1 Experimental analysis is used to illustrate…the PCP approach within an advanced scheduling architecture. It is argued that to achieve the required balance between objectives, a scheduling system must have the ability to relate the consequence of a decision to the satisfaction of overall objectives. The dissertation introduces the concept of a preference capacity plan (PCP) in an attempt to give automated schedulers this ability. PCP takes cognizance of both predicted demand for capacity and the interactions that exist between scheduling objectives. Experimental analysis is used to illustrate the power, versatility, and practicality of the PCP approach