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71
A Scheme for Unifying Optimization and Constraint Satisfaction Methods
, 2000
"... Optimization and constraint satisfaction methods are complementary to a large extent, and there has been much recent interest in combining them. Yet no generally accepted principle or scheme for their merger has evolved. We propose a scheme based on two fundamental dualities, the duality of search a ..."
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Cited by 33 (6 self)
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Optimization and constraint satisfaction methods are complementary to a large extent, and there has been much recent interest in combining them. Yet no generally accepted principle or scheme for their merger has evolved. We propose a scheme based on two fundamental dualities, the duality of search and inference and the duality of strengthening and relaxation. Optimization as well as constraint satisfaction methods can be seen as exploiting these dualities in their respective ways. Our proposal is that rather than employ either type of method exclusively, one can focus on how these dualities can be exploited in a given problem class. The resulting algorithm is likely to contain elements from both optimization and constraint satisfaction, and perhaps new methods that belong to neither.
Allocation and scheduling for mpsocs via decomposition and nogood generation
 In Procs. of the 11th Intern. Conference on Principles and Practice of Constraint Programming  CP 2005
, 2005
"... This paper proposes a decomposition approach to the allocation and scheduling of a multitask application on a multiprocessor systemonchip (MPSoCs) [Wolf, 2004]. This is currently one of the most critical problems in electronic design automation for VeryLarge Scale Integrated (VLSI) circuits. Wit ..."
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Cited by 27 (13 self)
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This paper proposes a decomposition approach to the allocation and scheduling of a multitask application on a multiprocessor systemonchip (MPSoCs) [Wolf, 2004]. This is currently one of the most critical problems in electronic design automation for VeryLarge Scale Integrated (VLSI) circuits. With the limits of chip integration reaching beyond one billion of elementary devices, current advanced integrated hardware platforms for highend consumer application (e.g. multimediaenabled phones) contain multiple processors and memories, as well as complex onchip interconnects. The hardware resources in these MPSoCs need to be optimally allocated and scheduled under tight throughput constraints when executing a target software workload (e.g. a video decoder). The multiprocessor system
Constraint programming contribution to benders decomposition: a case study
 In CP02
, 2002
"... Abstract. The aim of this paper is to demonstrate that CP could be a better candidate than MIP for solving the master problem within a Benders decomposition approach. Our demonstration is based on a case study of a workforce scheduling problem encountered in a large call center of Bouygues Telecom, ..."
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Cited by 20 (3 self)
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Abstract. The aim of this paper is to demonstrate that CP could be a better candidate than MIP for solving the master problem within a Benders decomposition approach. Our demonstration is based on a case study of a workforce scheduling problem encountered in a large call center of Bouygues Telecom, a French mobile phone operator. Our experiments show that CP can advantageously replace MIP for the implementation of the master problem due to its greater ability to efficiently manage a wide variety of constraints such as the ones occurring in time tabling applications. 1.
On the Global Solution of Linear Programs with Linear Complementarity Constraints
, 2007
"... This paper presents a parameterfree integerprogramming based algorithm for the global resolution of a linear program with linear complementarity constraints (LPEC). The cornerstone of the algorithm is a minimax integer program formulation that characterizes and provides certificates for the three ..."
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Cited by 20 (3 self)
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This paper presents a parameterfree integerprogramming based algorithm for the global resolution of a linear program with linear complementarity constraints (LPEC). The cornerstone of the algorithm is a minimax integer program formulation that characterizes and provides certificates for the three outcomes—infeasibility, unboundedness, or solvability—of an LPEC. An extreme point/ray generation scheme in the spirit of Benders decomposition is developed, from which valid inequalities in the form of satisfiability constraints are obtained. The feasibility problem of these inequalities and the carefully guided linear programming relaxations of the LPEC are the workhorse of the algorithm, which also employs a specialized procedure for the sparsification of the satifiability cuts. We establish the finite termination of the algorithm and report computational results using the algorithm for solving randomly generated LPECs of reasonable sizes. The results establish that the algorithm can handle infeasible, unbounded, and solvable LPECs effectively.
Identifying and Exploiting Problem Structures Using Explanationbased Constraint Programming
 Constraints
"... Abstract. Identifying structures in a given combinatorial problem is often a key step for designing efficient search heuristics or for understanding the inherent complexity of the problem. Several Operations Research approaches apply decomposition or relaxation strategies upon such a structure ident ..."
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Cited by 18 (2 self)
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Abstract. Identifying structures in a given combinatorial problem is often a key step for designing efficient search heuristics or for understanding the inherent complexity of the problem. Several Operations Research approaches apply decomposition or relaxation strategies upon such a structure identified within a given problem. The next step is to design algorithms that adaptively integrate that kind of information during search. We claim in this paper, inspired by previous work on impactbased search strategies for constraint programming, that using an explanationbased constraint solver may lead to collect invaluable information on the intimate dynamically revealed and static structures of a problem instance. Moreover, we discuss how dedicated OR solving strategies (such as Benders decomposition) could be adapted to constraint programming when specific relationships between variables are exhibited. 1.
Logic, Optimization, and Constraint Programming
 INFORMS Journal on Computing
, 2000
"... Because of their complementary strengths, optimization and constraint programming can be profitably merged. Their integration has been the subject of increasing commercial and research activity. This paper summarizes and contrasts the characteristics of the two fields; in particular, how they use ..."
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Cited by 18 (2 self)
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Because of their complementary strengths, optimization and constraint programming can be profitably merged. Their integration has been the subject of increasing commercial and research activity. This paper summarizes and contrasts the characteristics of the two fields; in particular, how they use logical inference in di#erent ways, and how these ways can be combined. It sketches the intellectual background for recent e#orts at integration. In particular, it traces the history of logicbased methods in optimization and the development of constraint programming in artificial intelligence. It concludes with a review of recent research, with emphasis on schemes for integration, relaxation methods, and practical applications. Optimization and constraint programming are beginning to converge, despite their very di#erent origins. Optimization is primarily associated with mathematics and engineering, while constraint programming developed much more recently in the computer science an...
Inferencebased sensitivity analysis for mixed integer/linear programming
 Operations Research
, 2000
"... A new method of sensitivity analysis for mixed integerlinear programming MILP is derived from the idea of inference duality The inference dual of an optimization problem asks how the optimal value can be deduced from the constraints In MILP a deduction based on the resolution method of theorem pro ..."
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Cited by 14 (3 self)
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A new method of sensitivity analysis for mixed integerlinear programming MILP is derived from the idea of inference duality The inference dual of an optimization problem asks how the optimal value can be deduced from the constraints In MILP a deduction based on the resolution method of theorem proving can be obtained from the branchandcut tree that solves the primal problem One can then investigate which perturbations of the problem leave this proof intact On this basis it is shown that in a minimization problem any perturbation that satises a certain system of linear inequalities will reduce the optimal value no more than a prespecied amount One can also give an upper bound on the increase in the optimal value that results from a given perturbation The method is illustrated on two realistic problems
Decomposition and learning for a hard real time task allocation algorithm
 Principles and Practice of Constraint Programming (CP 2004), Lecture Notes in Computer Science 3258
, 2004
"... Abstract. We present a cooperation technique using an accurate management of nogoods to solve a hard realtime problem which consists in assigning periodic tasks to processors in the context of fixed priorities preemptive scheduling. The problem is to be solved offline and our solving strategy is r ..."
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Cited by 14 (0 self)
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Abstract. We present a cooperation technique using an accurate management of nogoods to solve a hard realtime problem which consists in assigning periodic tasks to processors in the context of fixed priorities preemptive scheduling. The problem is to be solved offline and our solving strategy is related to the logic based Benders decomposition. A master problem is solved using constraint programming whereas subproblems are solved with schedulability analysis techniques coupled with an ad hoc nogood computation algorithm. Constraints and nogoods are learnt during the process and play a role close to Benders cuts. 1
SIMPL: A system for integrating optimization techniques
 Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR 2004), Lecture Notes in Computer Science 3011
, 2004
"... Abstract. In recent years, the Constraint Programming (CP) and Operations Research (OR) communities have explored the advantages of combining CP and OR techniques to formulate and solve combinatorial optimization problems. These advantages include a more versatile modeling framework and the ability ..."
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Cited by 11 (4 self)
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Abstract. In recent years, the Constraint Programming (CP) and Operations Research (OR) communities have explored the advantages of combining CP and OR techniques to formulate and solve combinatorial optimization problems. These advantages include a more versatile modeling framework and the ability to combine complementary strengths of the two solution technologies. This research has reached a stage at which further development would benefit from a generalpurpose modeling and solution system. We introduce here a system for integrated modeling and solution called SIMPL. Our approach is to view CP and OR techniques as special cases of a single method rather than as separate methods to be combined. This overarching method consists of an inferrelaxrestrict cycle in which CP and OR techniques may interact at any stage. We describe the main features of SIMPL and illustrate its usage with examples.