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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 ..."
Abstract

Cited by 399 (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.
Algorithms for Constraint Satisfaction Problems: A Survey
 AI MAGAZINE
, 1992
"... A large variety of problems in Artificial Intelligence and other areas of computer science can be viewed as a special case of the constraint satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, planning genetic ..."
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Cited by 371 (0 self)
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A large variety of problems in Artificial Intelligence and other areas of computer science can be viewed as a special case of the constraint satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, planning genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem. Others use backtracking to directly search for possible solutions. Some are a combination of these two techniques. This paper presents a brief overview of many of these approaches in a tutorial fashion.
Distributed constrained heuristic search
 IEEE Transactions on Systems, Man, and Cybernetics
, 1991
"... In this paper we present a model of decentralized problem solving, called Distributed Constrained Heuristic Search (DCHS) that provides both structure and focus in individual agent search spaces so as to optimize decisions in the global space. The model achieves this by integrating decentralized con ..."
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Cited by 90 (11 self)
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In this paper we present a model of decentralized problem solving, called Distributed Constrained Heuristic Search (DCHS) that provides both structure and focus in individual agent search spaces so as to optimize decisions in the global space. The model achieves this by integrating decentralized constraint satisfaction and heuristic search. It is a formalism suitable for describing a large set of DAI problems. We introduce the notion of textures that allow agents to operate in an asynchronous concurrent manner. The employment of textures coupled with distributed asynchronous backjumping (DAB), a type of distributed dependencydirected backtracking that we have developed, enables agents to instantiate variables in such a way as to substantially reduce backtracking. We have experimentally tested our approach in the domain of decentralized jobshop scheduling. A formulation of distributed jobshop scheduling as a DCHS is presented as well as experimental results.
Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem
 Artificial Intelligence
, 1996
"... Practical Constraint Satisfaction Problems (CSPs) such as design of integrated circuits or scheduling generally entail large search spaces with hundreds or even thousands of variables, each with hundreds or thousands of possible values. Often, only a very tiny fraction of all these possible assignme ..."
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Cited by 51 (2 self)
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Practical Constraint Satisfaction Problems (CSPs) such as design of integrated circuits or scheduling generally entail large search spaces with hundreds or even thousands of variables, each with hundreds or thousands of possible values. Often, only a very tiny fraction of all these possible assignments participates in a satisfactory solution. This article discusses techniques that aim at reducing the effective size of the search space to be explored in order to find a satisfactory solution by judiciously selecting the order in which variables are instantiated and the sequence in which possible values are tried for each variable. In the CSP literature, these techniques are commonly referred to as variable and value ordering heuristics. Our investigation is conducted in the job shop scheduling domain. We show that, in contrast with problems studied earlier in the CSP literature, generic variable and value heuristics do not perform well in this domain. This is attributed to the difficulty of these heuristics to properly account for the tightness of constraints and/or the connectivity of the constraint graphs induced by job shop scheduling CSPs. A new probabilistic framework is introduced that better captures these key aspects of the job shop scheduling search space. Empirical results show that variable and value ordering heuristics
Spike: Intelligent scheduling of hubble space telescope observations
 Intelligent Scheduling
, 1994
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Reactive Scheduling Systems
 Expert Systems and Intelligent Manufacturing
, 1994
"... In most practical environments, scheduling is an ongoing reactive process where evolving and changing circumstances continually force reconsideration and revision of preestablished plans. Scheduling research has traditionally ignored this "process view" of the problem, focusing instead on optimizat ..."
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Cited by 34 (4 self)
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In most practical environments, scheduling is an ongoing reactive process where evolving and changing circumstances continually force reconsideration and revision of preestablished plans. Scheduling research has traditionally ignored this "process view" of the problem, focusing instead on optimization of performance under idealized assumptions of environmental stability and solution executability. In this paper, we present work aimed at the development of reactive scheduling systems, which approach scheduling as a problem of maintaining a prescriptive solution over time, and emphasize objectives (e.g., solution continuity, system responsiveness) which relate directly to effective development and use of schedules in dynamic environments. We describe OPIS, a scheduling system designed to incrementally revise schedules in response to changes to solution constraints. OPIS implements a constraintdirected approach to reactive scheduling. Constraint analysis is used to prioritize outstandin...
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 the spa ..."
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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
Intelligent Backtracking Techniques for Job Shop Scheduling
 In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning
, 1992
"... This paper studies a version of the job shop scheduling problem in which some operations have to be scheduled within nonrelaxable time windows (i.e. earliest/latest possible start time windows). This problem is a wellknown NPcomplete Constraint Satisfaction Problem (CSP). A popular method for solv ..."
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Cited by 33 (4 self)
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This paper studies a version of the job shop scheduling problem in which some operations have to be scheduled within nonrelaxable time windows (i.e. earliest/latest possible start time windows). This problem is a wellknown NPcomplete Constraint Satisfaction Problem (CSP). A popular method for solving these types of problems consists in using depthfirst backtrack search. Our earlier work focused on developing efficient consistency enforcing techniques and efficient variable /value ordering heuristics to improve the efficiency of this procedure. In this paper, we combine these techniques with new lookback schemes that help the search procedure recover from socalled deadend search states (i.e. partial solutions that cannot be completed without violating some constraints). More specifically, we successively describe three intelligent backtracking schemes: Dynamic Consistency Enforcement dynamically enforces higher levels of consistency in selected critical subproblems, Learning From Fa...
Transformational Approach to Transportation Scheduling
 In Proceedings of the 8th KnowledgeBased Software Engineering Conference
, 1993
"... We have used KIDS (Kestrel Interactive Development System) to derive extremely fast and accurate transportation schedulers from formal specifications. As test data we use strategic transportation plans which are generated by U.S. government planners. In one such problem, the derived scheduler was ab ..."
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Cited by 31 (2 self)
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We have used KIDS (Kestrel Interactive Development System) to derive extremely fast and accurate transportation schedulers from formal specifications. As test data we use strategic transportation plans which are generated by U.S. government planners. In one such problem, the derived scheduler was able to schedule 15,460 individual movement requirements in 71 cpu seconds. The computed schedules use relatively few resources and satisfy all specified constraints. The speed of this scheduler derives from the synthesis of strong problemspecific constraint checking and constraint propagation code. 1 Introduction This paper describes our exploration of the transformational development of transportation schedulers. Our approach involves several stages. The first step is to develop a formal model of the transportation scheduling domain, called a domain theory. Second, the constraints, objectives, and preferences of a particular scheduling problem are stated within a domain theory as a problem...
Knowledgebased production management: Approaches, results and prospects
 Production Planning & Control
, 1992
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