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Search procedures and parallelism in constraint programming
 In Proc. of CP99
, 1999
"... Abstract. In this paper, we present a major improvement in the search procedures in constraint programming. First, we integrate various search procedures from AI and OR. Second, we parallelize the search on sharedmemory computers. Third, we add an objectoriented extensible control language to impl ..."
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Cited by 36 (4 self)
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Abstract. In this paper, we present a major improvement in the search procedures in constraint programming. First, we integrate various search procedures from AI and OR. Second, we parallelize the search on sharedmemory computers. Third, we add an objectoriented extensible control language to implement complex complete and incomplete search procedures. The result is a powerful set of tools which offers both brute force search using simple search procedures and parallelism, and finely tuned search procedures using that expressive control language. With this, we were able both to solve difficult and open problems using complete search procedures, and to quickly produce good results using incomplete search procedures. 1
Propositional Satisfiability and Constraint Programming: a Comparative Survey
 ACM Computing Surveys
, 2006
"... Propositional Satisfiability (SAT) and Constraint Programming (CP) have developed as two relatively independent threads of research, crossfertilising occasionally. These two approaches to problem solving have a lot in common, as evidenced by similar ideas underlying the branch and prune algorithms ..."
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Cited by 34 (4 self)
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Propositional Satisfiability (SAT) and Constraint Programming (CP) have developed as two relatively independent threads of research, crossfertilising occasionally. These two approaches to problem solving have a lot in common, as evidenced by similar ideas underlying the branch and prune algorithms that are most successful at solving both kinds of problems. They also exhibit differences in the way they are used to state and solve problems, since SAT’s approach is in general a blackbox approach, while CP aims at being tunable and programmable. This survey overviews the two areas in a comparative way, emphasising the similarities and differences between the two and the points where we feel that one technology can benefit from ideas or experience acquired
Program does not equal program: Constraint programming and its relationship to mathematical programming
 Interfaces
"... Arising from research in the computer science community, constraint programming is a fairly new technique for solving optimization problems. For those familiar with mathematical programming, a number of language barriers make it difficult to understand the concepts of constraint programming. In this ..."
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Cited by 28 (1 self)
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Arising from research in the computer science community, constraint programming is a fairly new technique for solving optimization problems. For those familiar with mathematical programming, a number of language barriers make it difficult to understand the concepts of constraint programming. In this short tutorial on constraint programming, we explain how it relates to familiar mathematical programming concepts and how constraint programming and mathematical programming technologies are complementary. We assume a minimal background in linear and integer programming. G eorge Dantzig [1963] invented the simplex method for linear programming in 1947 and first described it in a paper entitled “Programming in a linear structure ” [Dantzig 1948, 1949]. Fifty years later, linear programming is now a strategictechnique used by thousands of businesses trying to optimize their global operations. In the mid1980s, researchers developed constraint programming as a computer science technique by combining developments in the artificial intelligence community with the development of new computer programming languages. Fifteen years later, constraint programming is now being seen as an important technique that complements traditional mathematical programming technologies as businesses continue to look for ways to optimize their business operations. Developed independently as a technique within the computer science literature, constraint programming is now getting attention from the operations research com
Amplification of Search Performance through Randomization of Heuristics
 the Eighth International Conference on Principles and Practice of Constraint Programming
, 2002
"... Randomization as a means for improving search performance in combinatorial domains has received increasing interest in recent years. ..."
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Cited by 28 (14 self)
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Randomization as a means for improving search performance in combinatorial domains has received increasing interest in recent years.
An Efficient Approximate Algorithm for Winner Determination in Combinatorial Auctions
 In Proceedings of the Second acm Conference on Electronic Commerce
, 2000
"... This paper presents an approximate algorithm for the winner determination problem in combinatorial auctions. This algorithm is based on limited discrepancy search (LDS). Internet auctions have become an integral part of Electronic Commerce and can incorporate largescale, complicated types of auctio ..."
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Cited by 25 (3 self)
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This paper presents an approximate algorithm for the winner determination problem in combinatorial auctions. This algorithm is based on limited discrepancy search (LDS). Internet auctions have become an integral part of Electronic Commerce and can incorporate largescale, complicated types of auctions including combinatorial auctions, where multiple items are sold simultaneously and bidders can express complementarity among these items. Although we can increase participants' utilities by using combinatorial auctions, determining the optimal winners is a complicated constraint optimization problem that is shown to be NPcomplete. We introduce the idea...
Algorithm Performance and Problem Structure for Flowshop Scheduling
 In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI99
, 1999
"... Test suites for many domains often fail to model features present in realworld problems. For the permutation flowshop sequencing problem (PFSP), the most popular test suite consists of problems whose features are generated from a single uniform random distribution. Synthetic generation of problems ..."
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Cited by 21 (7 self)
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Test suites for many domains often fail to model features present in realworld problems. For the permutation flowshop sequencing problem (PFSP), the most popular test suite consists of problems whose features are generated from a single uniform random distribution. Synthetic generation of problems with characteristics present in realworld problems is a viable alternative. We compare the performance of several competitive algorithms on problems produced with such a generator. We find that, as more...
Limited discrepancy beam search
 In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI
, 2005
"... Beam search reduces the memory consumption of bestfirst search at the cost of finding longer paths but its memory consumption can still exceed the given memory capacity quickly. We therefore develop BULB (Beam search Using Limited discrepancy Backtracking), a complete memorybounded search method th ..."
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Cited by 20 (2 self)
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Beam search reduces the memory consumption of bestfirst search at the cost of finding longer paths but its memory consumption can still exceed the given memory capacity quickly. We therefore develop BULB (Beam search Using Limited discrepancy Backtracking), a complete memorybounded search method that is able to solve more problem instances of large search problems than beam search and does so with a reasonable runtime. At the same time, BULB tends to find shorter paths than beam search because it is able to use larger beam widths without running out of memory. We demonstrate these properties of BULB experimentally for three standard benchmark domains. 1
A New Local Search Algorithm Providing High Quality Solutions to Vehicle Routing Problems
, 1997
"... This paper describes a new local search algorithm that provides very high quality solutions to vehicle routing problems. The method uses greedy local search, but avoids local minima by using a large neighbourhood based upon rescheduling selected customer visits using constraint programming technique ..."
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Cited by 18 (0 self)
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This paper describes a new local search algorithm that provides very high quality solutions to vehicle routing problems. The method uses greedy local search, but avoids local minima by using a large neighbourhood based upon rescheduling selected customer visits using constraint programming techniques. The move operator adopted is completely generic, in that virtually any side constraint can be efficiently incorporated into the search process. Computational results show that a naive implementation of the method produces results bettering the best produced by competing techniques using minimaescaping methods. 1 Introduction In recent years, the method of choice for solving vehicle routing problems has been to use a local search technique. These local search methods have been favoured since they quickly provide solutions to problems of practical size that have not been solved by exact methods. However, because local search techniques only make small changes to the solution, they can onl...
Incomplete Tree Search using Adaptive Probing
, 2001
"... When not enough time is available to fully explore a search tree, different algorithms will visit different leaves. Depthfirst search and depthbounded discrepancy search, for example, make opposite assumptions about the distribution of good leaves. Unfortunately, it is rarely clear a priori which ..."
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Cited by 16 (3 self)
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When not enough time is available to fully explore a search tree, different algorithms will visit different leaves. Depthfirst search and depthbounded discrepancy search, for example, make opposite assumptions about the distribution of good leaves. Unfortunately, it is rarely clear a priori which algorithm will be most appropriate for a particular problem. Rather than fixing strong assumptions in advance, we propose an approach in which an algorithm attempts to adjust to the distribution of leaf costs in the tree while exploring it. By sacrificing completeness, such flexible algorithms can exploit information gathered during the search using only weak assumptions. As an example, we show how a simple depthbased additive cost model of the tree can be learned online. Empirical analysis using a generic tree search problem shows that adaptive probing is competitive with systematic algorithms on a variety of hard trees and outperforms them when the nodeordering heuristic makes many mistakes. Results on boolean satisfiability and two different representations of number partitioning confirm these observations. Adaptive probing combines the flexibility and robustness of local search with the ability to take advantage of constructive heuristics.
KBFS: KBestFirst Search
 Annals of Mathematics and Artificial Intelligence
, 2003
"... We introduce a new algorithm, Kbestfirst search (KBFS), which is a generalization of the well known bestfirst search. In KBFS, each iteration simultaneously expands the K best nodes from the openlist (rather than just the best as in BFS). We claim that KBFS outperforms BFS in domains where t ..."
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Cited by 14 (1 self)
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We introduce a new algorithm, Kbestfirst search (KBFS), which is a generalization of the well known bestfirst search. In KBFS, each iteration simultaneously expands the K best nodes from the openlist (rather than just the best as in BFS). We claim that KBFS outperforms BFS in domains where the heuristic function has large errors in estimation of the real distance to the goal state or does not predict deadends in the search tree. We present empirical results that confirm this claim and show that KBFS outperforms BFS by a factor of 15 on random trees with deadends, and by a factor of 2 and 7 on the Fifteen and TwentyFour tile puzzles, respectively. KBFS also finds better solutions than BFS and hillclimbing for the number partitioning problem. KBFS is only appropriate for finding approximate solutions with inadmissible heuristic functions.