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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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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
Constraint programming based column generation for employee timetabling
 IN: 7TH INTERNATIONAL CONFERENCE ON INTEGRATION OF AI AND OR TECHNIQUES IN CONSTRAINT PROGRAMMING FOR COMBINATORIAL OPTIMIZATION PROBLEMS (CPAIOR’05), PRAGUE
, 2005
"... The Employee Timetabling Problem (ETP) is a general class of problems widely encountered in service organizations (such as call centers for instance). Given a set of activities, a set of demand curves (specifying the demand in terms of employees for each activity for each time period) the problem ..."
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Cited by 11 (4 self)
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The Employee Timetabling Problem (ETP) is a general class of problems widely encountered in service organizations (such as call centers for instance). Given a set of activities, a set of demand curves (specifying the demand in terms of employees for each activity for each time period) the problem consists of constructing a set of work shifts such that each activity is at all time covered by a sufficient number of employees. Work shifts can cover many activities and must meet work regulations such as breaks, meals and maximum working time constraints. Furthermore, it is often desired to optimize a global objective function such as minimizing labor costs or maximizing a quality of service measure. This paper presents variants of this problem which are modeled with the Dantzig formulation. This approach consists of first generating all feasible work shifts and then selecting the optimal set. We propose to address the shift generation problem with constraint satisfaction techniques based on expressive and efficient global constraints such as gcc and regular. The selection problem, which is modeled with an integer linear program, is solved by a standard MIP solver for smaller instances and addressed by column generation for larger ones. Since a column generation procedure needs to generate only shifts of negative reduced cost, the optimization constraint costregular is introduced and described. Preliminary experimental results are given on a typical ETP.
A hybrid Benders’ decomposition method for solving stochastic constraint programs with linear recourse
 In Joint ERCIM/CoLogNET International Workshop on Constraint Solving and Constraint Logic Programming
, 2005
"... Abstract. We adopt Benders ’ decomposition algorithm to solve scenariobased Stochastic Constraint Programs (SCPs) with linear recourse. Rather than attempting to solve SCPs via a monolithic model, we show that one can iteratively solve a collection of smaller subproblems and arrive at a solution to ..."
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Abstract. We adopt Benders ’ decomposition algorithm to solve scenariobased Stochastic Constraint Programs (SCPs) with linear recourse. Rather than attempting to solve SCPs via a monolithic model, we show that one can iteratively solve a collection of smaller subproblems and arrive at a solution to the entire problem. In this approach, decision variables corresponding to the initial stage and linear recourse actions are grouped into two subproblems. The subproblem corresponding to the recourse action further decomposes into independent problems, each of which is a representation of a single scenario. Our computational experience on stochastic versions of the wellknown template design and warehouse location problems shows that, for linear recourse SCPs, Benders ’ decomposition algorithm provides a very efficient solution method. 1
Integrating Benders decomposition within Constraint Programming
 In Proceedings CP 2005, short paper
, 2005
"... Benders decomposition [1] is a solving strategy based on the separation of the variables of the problem. It is often introduced as a basis for models and techniques using the complementary strengths of constraint programming and optimization techniques. Hybridization schemes have appeared recently a ..."
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Benders decomposition [1] is a solving strategy based on the separation of the variables of the problem. It is often introduced as a basis for models and techniques using the complementary strengths of constraint programming and optimization techniques. Hybridization schemes have appeared recently and provided
Improving Global Constraints Support by Local Search
 In COSOLV’03
, 2003
"... Abstract. Most global constraints maintain a support for their filtering algorithm, namely a tuple consistent with both the constraint and current domains. However, this highly valuable information is rarely used outside of the constraint. In this paper we define the TVBreak Packing problem (TVBP) ..."
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Abstract. Most global constraints maintain a support for their filtering algorithm, namely a tuple consistent with both the constraint and current domains. However, this highly valuable information is rarely used outside of the constraint. In this paper we define the TVBreak Packing problem (TVBP) and we propose a generic hybridization scheme that we test on this realworld application. The principle of this Branch and Move approach is to use the support of the main global constraint of the problem as a guide for the branching strategy. The accuracy of this oracle is enhanced by local search improvements of this support tuple at each node of the search tree. 1.
The TV break packing problem
 In European Journal of Operational Research
, 2005
"... Abstract. Instead of selling advertisement spots one by one, some French satellite channels decided in 2002 to modify their commercial offer in order to sell packages of spots. These new General Conditions of Sale lead to an interesting optimization problem that we named the TVBREAK PACKING PROBLEM ..."
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Abstract. Instead of selling advertisement spots one by one, some French satellite channels decided in 2002 to modify their commercial offer in order to sell packages of spots. These new General Conditions of Sale lead to an interesting optimization problem that we named the TVBREAK PACKING PROBLEM (TVBP). We establish its NPhardness and study various resolutions approaches including linear programming (LP), lagrangian relaxation (LR), constraint programming (CP) and local search (LS). Finally we propose a generic CP/LS hybridization scheme (Branch & Move) whose application to the TVBP obtained the best results in our experiments. Dual upper bounds of the maximal revenue are also computed.
Solving Allocation Problems of Hard RealTime Systems with Dynamic Constraint Programming. Proceeding of RealTime and Network Systems (RTNS’06
, 2006
"... In this paper, we present an original approach (CPRTA for ”Constraint Programming for solving RealTime Allocation”) based on constraint programming to solve an allocation problem of hard realtime tasks in the context of fixed priority preemptive scheduling. CPRTA is built on dynamic constraint pr ..."
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In this paper, we present an original approach (CPRTA for ”Constraint Programming for solving RealTime Allocation”) based on constraint programming to solve an allocation problem of hard realtime tasks in the context of fixed priority preemptive scheduling. CPRTA is built on dynamic constraint programming together with a learning method to find a feasible processor allocation under constraints. It is a new approach which produce in its current version as acceptable performances as classical algorithms do. Some experimental results are given to show it. Moreover, CPRTA shows very interesting properties. It is complete —i.e., if a problem has no solution, the algorithm is able to prove it—, and it is nonparametric —i.e., it does not require specific initializations—. Thanks to its capacity to explain failures, it offers attractive perspectives for guiding the architectural design process. 1.
Breaking variable symmetry in almost injective problems
"... Abstract. Lexicographic constraints are commonly used to break variable symmetries. In the general case, the number of constraint to be posted is potentially exponential in the number of variables. For injective problems (AllDiff), Puget's method ..."
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Abstract. Lexicographic constraints are commonly used to break variable symmetries. In the general case, the number of constraint to be posted is potentially exponential in the number of variables. For injective problems (AllDiff), Puget's method