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Partial constraint satisfaction problems and guided local search
 Proc., Practical Application of Constraint Technology (PACT'96
, 1996
"... A largely unexplored aspect of Constraint Satisfaction Problem (CSP) is that of overconstrained instances for which no solution exists that satisfies all the constraints. In these problems, mentioned in the literature as Partial Constraint Satisfaction Problems (PCSPs), we are often looking for sol ..."
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Cited by 31 (12 self)
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A largely unexplored aspect of Constraint Satisfaction Problem (CSP) is that of overconstrained instances for which no solution exists that satisfies all the constraints. In these problems, mentioned in the literature as Partial Constraint Satisfaction Problems (PCSPs), we are often looking for solutions which violate the minimum number of constraints. In more realistic settings, constraints violations incur different costs and solutions are sought that minimize the total cost from constraint violations and possibly other criteria. Problems in this category present enormous difficulty to complete search algorithms. In practical terms, complete search has more or less to resemble the traditional Branch and Bound taking no advantage of the efficient pruning techniques recently developed for CSPs. In this report, we examine how the stochastic search method of Guided Local Search (GLS) can be applied to these problems. The effectiveness of the method is demonstrated on instances of the Radio Link Frequency Assignment Problem (RLFAP), which is a realworld Partial CSP.
Weighting for Godot: Learning Heuristics for GSAT
 In Proceedings of the AAAI
, 1996
"... We investigate an improvement to GSAT which associates a weight with each clause. GSAT moves to assignments maximizing the weight of satisfied clauses and this weight is incremented when GSAT moves to an assignment in which this clause is unsatisfied. We present results showing that this algorithm a ..."
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Cited by 30 (2 self)
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We investigate an improvement to GSAT which associates a weight with each clause. GSAT moves to assignments maximizing the weight of satisfied clauses and this weight is incremented when GSAT moves to an assignment in which this clause is unsatisfied. We present results showing that this algorithm and its variants outperform one of the best known modifications of GSAT to date using two metrics: number of solved problems on a single try, and minimum mean number of flips to solve a test suite of problems. Content Areas: Constraint Satisfaction, Search 1 Introduction Local search procedures are an alternative to complete search algorithms for solving combinatorially expensive search problems. GSAT is a local search algorithm which can often find solutions to satisfiable SAT problems in Conjunctive Normal Form (CNF) quickly [ SLM92 ] . GSAT operates by changing a complete assignment of variables into one in which the maximum possible number of clauses are satisfied by flipping a single ...
A General Approach for Constraint Solving by Local Search
 In CPAIOR’00
, 2000
"... In this paper, we present a general approach for solving constraint problems by local search. The proposed approach is based on a set of highlevel constraint primitives motivated by constraint programming systems. These constraints constitute the basic bricks to formulate a given combinatorial prob ..."
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Cited by 30 (2 self)
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In this paper, we present a general approach for solving constraint problems by local search. The proposed approach is based on a set of highlevel constraint primitives motivated by constraint programming systems. These constraints constitute the basic bricks to formulate a given combinatorial problem. A tabu search engine ensures the resolution of the problem such formulated. Experimental results are shown to validate the proposed approach.
Using Global Constraints for Local Search
 DIMACS SERIES IN DISCRETE MATHEMATICS AND THEORETICAL COMPUTER SCIENCE
, 2000
"... Conventional ways of using local search are difficult to generalize. Increased efficiency is the only goal, generality often being disregarded. This is manifested in the highly monolithic encodings of complex problems and the application of highly specific satisfaction methods. Other approaches tak ..."
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Cited by 28 (9 self)
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Conventional ways of using local search are difficult to generalize. Increased efficiency is the only goal, generality often being disregarded. This is manifested in the highly monolithic encodings of complex problems and the application of highly specific satisfaction methods. Other approaches take the general constraint programming framework as a starting point and try to introduce local search methods for constraint satisfaction. These methods frequently fail because they have only a very limited view of the unknown searchspace structure. The present paper attempts to overcome the drawbacks of these two approaches by using global constraints. The use of global constraints for local search allows us to revise a current state on a more global level with domainspecific knowledge, while preserving features like reusability and maintenance. The proposed strategy is demonstrated on a dynamic jobshop scheduling problem.
Ants can solve Constraint Satisfaction Problems
 IEEE Transactions on Evolutionary Computation
, 2001
"... In this paper we describe a new incomplete approach for solving constraint satisfaction problems (CSPs) based on the ant colony optimization (ACO) metaheuristic. The idea is to use artificial ants to keep track of promising areas of the search space by laying trails of pheromone. This pheromone info ..."
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Cited by 24 (9 self)
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In this paper we describe a new incomplete approach for solving constraint satisfaction problems (CSPs) based on the ant colony optimization (ACO) metaheuristic. The idea is to use artificial ants to keep track of promising areas of the search space by laying trails of pheromone. This pheromone information is used to guide the search, as a heuristic for choosing values to be assigned to variables.
Succeedfirst or Failfirst: A Case Study in Variable and Value Ordering
, 1996
"... It is well known that appropriate variable and value ordering heuristics are often crucial when solving constraint satisfaction problems. A variable ordering heuristic which is often recommended, and is often successful, is based on the `failfirst' principle: choose next the variable with the small ..."
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Cited by 22 (2 self)
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It is well known that appropriate variable and value ordering heuristics are often crucial when solving constraint satisfaction problems. A variable ordering heuristic which is often recommended, and is often successful, is based on the `failfirst' principle: choose next the variable with the smallest remaining domain. Generalpurpose value ordering heuristics are less common, but it has been argued that a value which has least effect on future choices should be chosen, a kind of `succeedfirst' strategy. This paper considers variable and value ordering heuristics for the car sequencing problem. A number of cars are to be made on a production line: each of them may require one or more options which are installed at different stations on the line. The option stations have lower capacity than the rest of the production line, e.g. a station may be able to cope with at most one car out of every two. The cars are to be arranged in sequence so that these capacities are not exceeded. The cho...
Extending GENET for NonBinary CSP’s
, 1995
"... GENET has been shown to be efficient and effective on certain hard or large constraint satasfaction problems. Although GENET has been enhanced to handle also the atmost and illegal constraints in addition to binary constraints, it is deficient in handling nonbinary constraints in general. In thi ..."
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Cited by 21 (3 self)
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GENET has been shown to be efficient and effective on certain hard or large constraint satasfaction problems. Although GENET has been enhanced to handle also the atmost and illegal constraints in addition to binary constraints, it is deficient in handling nonbinary constraints in general. In this paper, we present EGENET, an extended GENET. EGENET features a convergence and learning procedure similar to that of GENET and a generic representation scheme for general constraints, which range from disjunctive constraints to nonlinear constraints to symbolic constraants. We have implemented an eficient prototype of E GENET for singleprocessor machines. Benchmarking results confirms the eficiency and flexibility of EGENET. Our implementation also compares well against CHIP, PROCLANN, and GENET.
The car sequencing problem: overview of stateoftheart methods and industrial casestudy of the ROADEF’2005 challenge problem
 EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (EJOR)
, 2007
"... The ROADEF challenge is organized every two years by the French Society of Operations Research and DecisionMaking Aid. The goal is to allow industrial partners to witness recent developments in the field of Operations Research and Decision Analysis, and researchers to face up a decisional problem, ..."
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Cited by 21 (5 self)
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The ROADEF challenge is organized every two years by the French Society of Operations Research and DecisionMaking Aid. The goal is to allow industrial partners to witness recent developments in the field of Operations Research and Decision Analysis, and researchers to face up a decisional problem, often complex, occurred in industry. In 2005, the subject of this challenge has been proposed by the car manufacturer RENAULT and concerned a car sequencing problem. This problem involves scheduling cars along an assembly line with hard and soft capacity constraints. The industrial problem considered in the challenge differs from the standard problem since, besides capacity constraints imposed by the assembly shop, it also introduces paint batching constraints to minimize the consumption of solvents in the paint shop. We review the exact and heuristic methods of the literature proposed to solve the standard problem and we present the industrial context and the specificities of the challenge problem. We describe the process of the ROADEF’2005 challenge and the methods proposed by the competing teams. We also analyse the results of these methods on the car sequencing instances provided by RENAULT. The final ranking of the candidates is reported and directions for future research based on the results are drawn.
Systematic versus stochastic constraint satisfaction
 Proc., 14th International Joint Conference on AI
, 1995
"... This panel explores issues of systematic and stochastic control in the context of constraint satisfaction. 1 ..."
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Cited by 20 (2 self)
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This panel explores issues of systematic and stochastic control in the context of constraint satisfaction. 1
Solving constraint satisfaction sequencing problems by iterative repair
 IN 1ST INTERNATIONAL CONFERENCE ON THE PRACTICAL APPLICATION OF CONSTRAINT TECHNOLOGIES AND LOGIC PROGRAMMING (PACLP
, 1999
"... Many constraint satisfaction problems involve sequencing constraints, where the aim is to find a sequence for a domain of values such that all the constraints on the sequence are satisfied. Specialised techniques have been developed to tackle this problem within the constraint programming framework ..."
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Cited by 18 (1 self)
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Many constraint satisfaction problems involve sequencing constraints, where the aim is to find a sequence for a domain of values such that all the constraints on the sequence are satisfied. Specialised techniques have been developed to tackle this problem within the constraint programming framework using constructive, backtracking search. In this paper we investigate local search techniques to tackle this problem. By taking advantage of the structure of the sequencing problem we show that within a local search framework we can reduce the size of the search space and the number of constraints which are required to be satisfied. We present SwapGenet, an iterative repairbased algorithm designed specifically for solving the constraint satisfaction sequencing problem. We present results of an empirical evaluation demonstrating the superiority of SwapGenet over Genet on the car sequencing problem.