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GLOBAL CONSTRAINTS AND FILTERING ALGORITHMS
"... Constraint programming (CP) is mainly based on filtering algorithms; their association with global constraints is one of the main strengths of CP. This chapter is an overview of these two techniques. Some of the most frequently used global constraints are presented. In addition, the filtering algor ..."
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Cited by 22 (1 self)
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Constraint programming (CP) is mainly based on filtering algorithms; their association with global constraints is one of the main strengths of CP. This chapter is an overview of these two techniques. Some of the most frequently used global constraints are presented. In addition, the filtering algorithms establishing arc consistency for two useful constraints, the alldiff and the global cardinality constraints, are fully detailed. Filtering algorithms are also considered from a theoretical point of view: three different ways to design filtering algorithms are described and the quality of the filtering algorithms studied so far is discussed. A categorization is then proposed. Over-constrained problems are also mentioned and global soft constraints are introduced.
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.
Better propagation for non-preemptive single-resource con-straint problems
- In Proceedings of the ERCIM/CoLogNet workshop
, 2004
"... Abstract. Overload checking, forbidden regions, edge finding, and notfirst/not-last detection are well-known propagation rules to prune the start times of activities which have to be processed without any interruption and overlapping on an exclusively available resource, i.e. machine. These rules ar ..."
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Abstract. Overload checking, forbidden regions, edge finding, and notfirst/not-last detection are well-known propagation rules to prune the start times of activities which have to be processed without any interruption and overlapping on an exclusively available resource, i.e. machine. These rules are extendable by two other rules which take the number of activities into account which are at most processable after or before another activity. To our knowledge, these rules are based on approximations of the (minimal) earliest completion times and the (maximal) latest start times of sets of activities. In this paper, the precise definitions of these time values as well as an efficient procedure for their calculations are given. Based on the precise time values the rules are re-formulated and applied to a well-known job shop scheduling benchmark. 1
A Resource Cost Aware Cumulative
"... Abstract. We motivate and introduce an extension of the well-known cumulative constraint which deals with time and volume dependent cost of resources. Our research is primarily interested in scheduling problems under time and volume variable electricity costs, but the constraint equally applies to m ..."
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Abstract. We motivate and introduce an extension of the well-known cumulative constraint which deals with time and volume dependent cost of resources. Our research is primarily interested in scheduling problems under time and volume variable electricity costs, but the constraint equally applies to manpower scheduling when hourly rates differ over time and/or extra personnel incur higher hourly rates. We present a number of possible lower bounds on the cost, including a min-cost flow, different LP and MIP models, as well as greedy algorithms, and provide a theoretical and experimental comparison of the different methods. 1
Dynamic Consolidation of Highly Available Web Applications
, 2011
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A Constraint Programming Agent for Automated Trading
"... The Trading Agent Competition (TAC) combines a fairly realistic model of the Internet commerce of the future, including shopbots and pricebots, with a challenging problem in automated reasoning and decision making. ..."
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Cited by 2 (1 self)
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The Trading Agent Competition (TAC) combines a fairly realistic model of the Internet commerce of the future, including shopbots and pricebots, with a challenging problem in automated reasoning and decision making.
Time-table disjunctive reasoning for the cumulative constraint
- In International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR15
, 2015
"... Abstract. Scheduling has been a successful domain of application for constraint programming since its beginnings. The cumulative constraint – which enforces the usage of a limited resource by several tasks – is one of the core components that are surely responsible of this success. Un-fortunately, e ..."
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Abstract. Scheduling has been a successful domain of application for constraint programming since its beginnings. The cumulative constraint – which enforces the usage of a limited resource by several tasks – is one of the core components that are surely responsible of this success. Un-fortunately, ensuring bound-consistency for the cumulative constraint is already NP-Hard. Therefore, several relaxations were proposed to reduce domains in polynomial time such as Time-Tabling, Edge-Finding, Ener-getic Reasoning, and Not-First-Not-Last. Recently, Vilim introduced the Time-Table Edge-Finding reasoning which strengthens Edge-Finding by considering the time-table of the resource. We pursue the idea of exploit-ing the time-table to detect disjunctive pairs of tasks dynamically during the search. This new type of filtering – which we call time-table disjunc-tive reasoning – is not dominated by existing filtering rules. We propose a simple algorithm that implements this filtering rule with a O(n2) time complexity (where n is the number of tasks) without relying on complex data structures. Our results on well known benchmarks highlight that using this new algorithm can substantially improve the solving process for some instances and only adds a marginally low computation overhead for the other ones.
Abstract SUBCONTRACTORS SCHEDULING ON RESIDENTIAL BUILDINGS CONSTRUCTION SITES
"... Erecting a residential construction is a complex project which implies many actors and strong deadline requirements. The aim of this paper is to present a case study about schedules of subcontractor’s tasks on residential buildings. Directly after the end of structural works, start not only tasks li ..."
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Erecting a residential construction is a complex project which implies many actors and strong deadline requirements. The aim of this paper is to present a case study about schedules of subcontractor’s tasks on residential buildings. Directly after the end of structural works, start not only tasks like electricity, plumbing, cover, water circuit installation, but also paintings, furniture, wallpapers, etc. which are often executed by subcontractors. Solving this problem consists in finding for each elementary task its starting date, its ending date and its volume executed each day between these two dates. A constraint programming based solution is presented in this paper. 1.
Global Constraints
"... For use only by the students of the CP 2005 Summer School. Abstract. Constraint programming (CP) is mainly based on filtering algorithms; their association with global constraints is one of the main strengths of CP. This chapter is an overview of these two techniques. Some of the most frequently use ..."
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Cited by 1 (0 self)
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For use only by the students of the CP 2005 Summer School. Abstract. Constraint programming (CP) is mainly based on filtering algorithms; their association with global constraints is one of the main strengths of CP. This chapter is an overview of these two techniques. Some of the most frequently used global constraints are presented. In addition, the filtering algorithms establishing arc consistency for two useful constraints, the alldiff and the global cardinality constraints, are fully detailed. Filtering algorithms are also considered from a theoretical point of view: three different ways to design filtering algorithms are described and the quality of the filtering algorithms studied so far is discussed. A categorization is then proposed. Over-constrained problems are also mentioned and global soft constraints are introduced. 1
Simple and Scalable Time-Table Filtering for the Cumulative Constraint
"... Abstract. Cumulative is an essential constraint in the CP framework, and is present in scheduling and packing applications. The lightest filter-ing for the cumulative constraint is time-tabling. It has been improved several times over the last decade. The best known theoretical time com-plexity for ..."
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Abstract. Cumulative is an essential constraint in the CP framework, and is present in scheduling and packing applications. The lightest filter-ing for the cumulative constraint is time-tabling. It has been improved several times over the last decade. The best known theoretical time com-plexity for time-table is O(n logn) introduced by Ouellet and Quimper. We show a new algorithm able to run in O(n), by relying on range min query algorithms. This approach is more of theoretical rather than prac-tical interest, because of the generally larger number of iterations needed to reach the fixed point. On the practical side, the recent synchronized sweep algorithm of Letort et al, with a time-complexity of O(n2), requires fewer iterations to reach the fix-point and is considered as the most scal-able approach. Unfortunately this algorithm is not trivial to implement. In this work we present a O(n2) simple two step alternative approach: first building the mandatory profile, then updating all the bounds of the activities. Our experimental results show that our algorithm out-performs synchronized sweep and the time-tabling implementations of other open-source solvers on large scale scheduling instances, sometimes significantly.
