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22
The alldifferent Constraint: A Survey
, 2001
"... The constraint of difference is known to the constraint programming community since Lauriere introduced Alice in 1978. Since then, several strategies have been designed to solve the alldifferent constraint. This paper surveys the most important developments over the years regarding the alldifferent ..."
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Cited by 49 (1 self)
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The constraint of difference is known to the constraint programming community since Lauriere introduced Alice in 1978. Since then, several strategies have been designed to solve the alldifferent constraint. This paper surveys the most important developments over the years regarding the alldifferent constraint. First we summarize the underlying concepts and results from graph theory and integer programming. Then we give an overview and an abstract comparison of different solution strategies. In addition, the symmetric alldifferent constraint is treated. Finally, we show how to apply costbased filtering to the alldifferent constraint.
Robust solutions for combinatorial auctions
 In Proceedings of the 6th ACM Conference on Electronic Commerce
, 2005
"... Bids submitted in auctions are usually treated as enforceable commitments in most bidding and auction theory literature. In reality bidders often withdraw winning bids before the transaction when it is in their best interests to do so. Given a bidwithdrawal in a combinatorial auction, finding an al ..."
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Cited by 16 (2 self)
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Bids submitted in auctions are usually treated as enforceable commitments in most bidding and auction theory literature. In reality bidders often withdraw winning bids before the transaction when it is in their best interests to do so. Given a bidwithdrawal in a combinatorial auction, finding an alternative repair solution of adequate revenue without causing undue disturbance to the remaining winning bids in the original solution may be difficult or even impossible. We have called this the “Bidtaker’s Exposure Problem”. When faced with such unreliable bidders, it is preferable for the bidtaker to preempt such uncertainty by having a solution that is robust to bidwithdrawal and provides a guarantee that possible withdrawals may be repaired easily with a bounded loss in revenue. Firstly, we use the Weighted Super Solutions framework [13], from the field of Constraint Programming, to solve the problem of finding a robust solution of maximum revenue. A weighted super solution guarantees that any subset of bids likely to be withdrawn can be repaired to form a new solution of at least a given revenue by making a limited number of changes. Secondly, we introduce an auction model that uses a form of leveled commitment contract [27, 28], which we have called mutual bid bonds, to improve solution reparability by facilitating backtracking on winning bids by the bidtaker. We then examine the tradeoff between robustness and revenue in different economically motivated auction scenarios for different constraints on the revenue of repair solutions. We also demonstrate experimentally that fewer winning bids partake in robust solutions, thereby reducing any associated overhead in dealing with extra bidders.
Counting solutions of CSPs: A Structural Approach
 In IJCAI05
, 2005
"... Determining the number of solutions of a CSP has several applications in AI, in statistical physics, and in guiding backtrack search heuristics. It is a #Pcomplete problem for which some exact and approximate algorithms have been designed. Successful CSP models often use higharity, global constrain ..."
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Cited by 15 (3 self)
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Determining the number of solutions of a CSP has several applications in AI, in statistical physics, and in guiding backtrack search heuristics. It is a #Pcomplete problem for which some exact and approximate algorithms have been designed. Successful CSP models often use higharity, global constraints to capture the structure of a problem. This paper exploits such structure and derives polytime evaluations of the number of solutions of individual constraints. These may be combined to approximate the total number of solutions or used to guide search heuristics. We give algorithms for several of the main families of constraints and discuss the possible uses of such solution counts. 1
Combination of among and cardinality constraints
 Proceedings of the Second International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR 2005), volume 3524 of Lecture Notes in Computer Science
, 2005
"... Abstract. A cardinality constraint imposes that each value of a set V must be taken a certain number of times by a set of variables X, whereas an among constraint imposes that a certain number of variables of a set X must take a value in the set V. This paper studies several combinations of among co ..."
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Cited by 11 (1 self)
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Abstract. A cardinality constraint imposes that each value of a set V must be taken a certain number of times by a set of variables X, whereas an among constraint imposes that a certain number of variables of a set X must take a value in the set V. This paper studies several combinations of among constraints and several conjunctions of among constraints and cardinality constraints. Some filtering algorithms are proposed and they are characterized when it is possible. Moreover, a weak form of Singleton arc consistency is considered. At last, it is shown how the global sequencing constraint and the global minimum distance constraint can be easily modeled by some conjunctions of cardinality and among constraints. Some results are also given for the global minimum distance constraint. They show that our study outperforms the existing constraints in ILOG Solver. 1
A Global Constraint for Total Weighted Completion Time for Cumulative Resources
, 2008
"... The criterion of total weighted completion time occurs as a subproblem of combinatorial optimization problems in such diverse areas as scheduling, container loading and storage assignment in warehouses. These applications often necessitate considering a rich set of requirements and preferences, whi ..."
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Cited by 10 (2 self)
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The criterion of total weighted completion time occurs as a subproblem of combinatorial optimization problems in such diverse areas as scheduling, container loading and storage assignment in warehouses. These applications often necessitate considering a rich set of requirements and preferences, which makes constraint programming (CP) an effective modeling and solving approach. On the other hand, basic CP techniques can be inefficient in solving models that require inference over sum type expressions. In this paper, we address increasing the solution efficiency of constraintbased approaches to cumulative resource scheduling with the above criterion. Extending previous results for unary capacity resources, we define the COMPLETIONm global constraint for propagating the total weighted completion time of activities that require the same cumulative resource. We present empirical results in two different problem domains: scheduling a single cumulative resource, and container loading with constraints on the location of the center of gravity. In both domains, the proposed constraint propagation algorithm outperforms existing propagation techniques.
RiskManaged Combinatorial Auctions
"... Auction theory has traditionally regarded bids as enforceable commitments. We study the setting where we relax this standard, yet often incorrect, assumption that is common to almost all prior literature on the subject. Specifically, we consider the setting in which winning bids may be withdrawn, or ..."
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Cited by 8 (0 self)
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Auction theory has traditionally regarded bids as enforceable commitments. We study the setting where we relax this standard, yet often incorrect, assumption that is common to almost all prior literature on the subject. Specifically, we consider the setting in which winning bids may be withdrawn, or reneged upon, before the auction transaction is completed successfully. We examine the potential impact of winningbid withdrawal in combinatorial auctions. We call this the bidtaker’s exposure problem. We argue that it is possible for the bidtaker to preempt such uncertainty by choosing an allocation of items to bidders that is robust to bidwithdrawal. Such a robust allocation comes with a guarantee that if it is invalidated by winning bid withdrawals it can be repaired easily with a bounded loss in revenue. We discuss the computational challenges posed by risk management and propose a constraint programming approach to tackling it. We empirically study the tradeoff between robustness and revenue. We also introduce a new auction model that improves robustness by facilitating backtracking on winning bids by the bidtaker. Finally, we examine the issue of incentivizing truthful bidding whilst seeking to manage risk in the allocation.
Integrating operations research in constraint programming
, 2010
"... This paper presents Constraint Programming as a natural formalism for modelling problems, and as a flexible platform for solving them. CP has a range of techniques for handling constraints including several forms of propagation and tailored algorithms for global constraints. It also allows linear p ..."
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Cited by 7 (0 self)
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This paper presents Constraint Programming as a natural formalism for modelling problems, and as a flexible platform for solving them. CP has a range of techniques for handling constraints including several forms of propagation and tailored algorithms for global constraints. It also allows linear programming to be combined with propagation and novel and varied search techniques which can be easily expressed in CP. The paper describes how CP can be used to exploit linear programming within different kinds of hybrid algorithm. In particular it can enhance techniques such as Lagrangian relaxation, Benders decomposition and column generation.
ConstraintBased local search for inventory control under stochastic demand and lead time
 INFORMS Journal on Computing
, 2011
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 3 (0 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Applying interchangeability to complex local problems in distributed constraint reasoning
 in Proc. Workshop on Distributed Constraint Reasoning, AAMAS
, 2006
"... Abstract. Many algorithms for distributed constraint problems assume each agent has a single variable. For problems with multiple variables per agent, one standard approach is to transform each agent’s local problem by defining a single new variable whose domain is the set of all local solutions, an ..."
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Cited by 3 (1 self)
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Abstract. Many algorithms for distributed constraint problems assume each agent has a single variable. For problems with multiple variables per agent, one standard approach is to transform each agent’s local problem by defining a single new variable whose domain is the set of all local solutions, and reformulating the interagent constraints accordingly. We propose two general improvements to this method intended to (i) reduce problem size by removing interchangeable and dominated values from the new domains, and (ii) speed up search by identifying values that are interchangeable with respect to interagent constraints. 1