Solving a Network Design Problem

Chabrier, Alain; Danna, Emilie; Pape, Claude Le; Perron, Laurent

doi:10.1023/b:anor.0000032577.81139.84

Cited by 29 publications

(13 citation statements)

References 6 publications

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“…In [31], the authors solved Vehicle Routing Problems by selecting the set of customer visits to remove and re-insert. On the Network Design Problem, the structure of the problem is exploited to define accurate neighborhoods [3]. On the Job Shop Scheduling Problem, a neighborhood that deals with the objective function has been studied [5]; the sub-problems were solved with MIP.…”

Section: Algorithm 1 Large Neighborhood Searchmentioning

confidence: 99%

Explanation-based large neighborhood search

2014

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One of the most well-known and widely used local search techniques for solving optimization problems in Constraint Programming is the Large Neighborhood Search (LNS) algorithm. Such a technique is, by nature, very flexible and can be easily integrated within standard backtracking procedures. One of its drawbacks is that the relaxation process is quite often problem dependent. Several works have been dedicated to overcome this issue through problem independent parameters. Nevertheless, such generic approaches need to be carefully parameterized at the instance level. In this paper, we demonstrate that the issue of finding a problem independent neighborhood generation technique for LNS can be addressed using explanation-based neighborhoods. An explanation is a subset of constraints and decisions which justifies a solver event such as a domain modification or a conflict. We evaluate our proposal for a set of optimization problems. We show that our approach is at least competitive with or even better than state-of-the-art algorithms and can be easily combined with state-of-the-art neighborhoods. Such results pave the way to a new use of explanation-based approaches for improving search.

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Section: Algorithm 1 Large Neighborhood Searchmentioning

confidence: 99%

Explanation-based large neighborhood search

2014

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“…Some problems involving undetermined graphs have been formulated using either binary variables, sets ( [14,15]) or integers (successor variables e.g. in [18,19]).…”

Section: Cp(graph)mentioning

confidence: 99%

“…In CP(Graph) [13], graph variables, and constraints on these variables are described (see also [14,15] for similar ideas). CP(Graph) can be used to express and solve combinatorial graph problems modeled as constrained subgraph extraction problems.…”

Section: Introductionmentioning

confidence: 99%

Combining Two Structured Domains for Modeling Various Graph Matching Problems

Deville

Dooms

Zampelli

2008

Lecture Notes in Computer Science

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Abstract. Graph pattern matching is a central application in many fields. In various areas, the structure of the pattern can only be approximated and exact matching is then too accurate. We focus here on approximations declared by the user within the pattern (optional nodes and forbidden arcs), covering graph/subgraph mono/isomorphism problems. In this paper, we show how the integration of two domains of computation over countable structures, graphs and maps, can be used for modeling and solving various graph matching problems from the simple graph isomorphism to approximate graph matching. To achieve this, we extend map variables allowing the domain and range to be non-fixed and constrained. We describe how such extended maps are designed then realized on top of finite domain and finite set variables with specific propagators. We show how a single monomorphism constraint is sufficient to model and solve those multiples graph matching problems. Furthermore, our experimental results show that our CP approach is competitive with a state of the art algorithm for subgraph isomorphism.

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“…RINS is generic: it can be applied to any MIP model, with no other input than the model itself. We have experimented RINS on a variety of MIP models, including network design (Chabrier et al 2004), scheduling, lot-sizing and crew scheduling models. RINS finds good integer solutions on models that previously were very difficult to solve, significantly outperforming the default CPLEX strategy and most often also outperforming local branching (Fischetti and Lodi 2003).…”

Section: A New Generic Heuristic For Branch-and-cutmentioning

confidence: 99%

Integrating local search techniques into mixed integer programming

Danna

2004

4OR

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We survey the main results of the author's PhD thesis that was supervised by Claude Le Pape (ILOG, France) and Philippe Michelon (Université d'Avignon, France) and has been defended in June 2004. The dissertation is written in French and is available from the author. It introduces several strategies for integrating local search techniques into mixed integer programming, with an emphasis on generic algorithms.

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Solving a Network Design Problem

Cited by 29 publications

References 6 publications

Explanation-based large neighborhood search

Explanation-based large neighborhood search

Combining Two Structured Domains for Modeling Various Graph Matching Problems

Integrating local search techniques into mixed integer programming

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