Constraint-based Very Large-Scale Neighborhood search

Mouthuy, Sébastien; Hentenryck, Pascal Van; Deville, Yves

doi:10.1007/s10601-011-9114-7

Cited by 12 publications

(6 citation statements)

References 20 publications

(46 reference statements)

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“…The multi-stage VLSN search was implemented in Comet (Van Hentenryck and Michel, 2005), based on the Constraint-Based Very Large-Scale Neighborhood Search framework presented in (Mouthuy et al, 2012). All experiments were run on an Intel Q6600 Quadcore 2,4GHz CPU.…”

Section: Methodsmentioning

confidence: 99%

“…Note that Large Neighborhood Search, such as used in (Shaw, 1998) can also be considered a VLNS search. A framework for constraint-based very large neighborhood search has been presented in (Mouthuy et al, 2012) and .…”

Section: Related Workmentioning

confidence: 99%

“…Detailed explanations on this requirement and on how it can be enforced can be found in (Mouthuy et al, 2012).…”

Section: Very Large Scale Neighborhoodsmentioning

confidence: 99%

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A Multistage Very Large-Scale Neighborhood Search for the Vehicle Routing Problem with Soft Time Windows

Mouthuy

Massen

Deville

et al. 2015

Transportation Science

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This paper considers the Vehicle Routing Problem with Soft Time Windows, a challenging routing problem, where customer's time windows may be violated at a certain cost. The Vehicle Routing Problem with Soft Time Windows has a lexicographic objective function, aiming at minimizing first the number of routes, then the number of violated time windows and finally the total routing distance. We present a multi-stage Very Large-Scale Neighborhood search for this problem. Each stage corresponds to a Variable Neighborhood Descent over a parametrizable Very Large-Scale Neighborhood. These neighborhoods contain an exponential number of neighbors, as opposed to classical local search neighborhoods. Often, searching Very Large-Scale Neighborhoods can produce local optima of a higher quality than polynomial-sized neighborhoods. Furthermore we use a sophisticated heuristic to determine service start times allowing to minimize the number of violated time windows. We test our approach on number of different problem types, and compare the results to the relevant state-of-the-art. The experimental results show that our algorithm improves best-known solutions on 53% of the most studied instances. Many of these improvements stem from a reduction of the number of vehicles, a critical objective in Vehicle Routing Problems.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Multistage Very Large-Scale Neighborhood Search for the Vehicle Routing Problem with Soft Time Windows

Mouthuy

Massen

Deville

et al. 2015

Transportation Science

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“…Subsequently, Mouthuy et al. () integrated these concepts within a very large scale neighborhood search framework. More recently, Ghiani et al.…”

Section: Related Workmentioning

confidence: 99%

“…In the context of constraint programming (CP), Michel (2007, 2009) showed how effective local search procedures can be automatically synthesized from models encoded in a rich constraint language for at least some application classes. Subsequently, Mouthuy et al (2012) integrated these concepts within a very large scale neighborhood search framework. More recently, Ghiani et al (2015) and Adamo et al (2017a) moved on from an MIP compact formulation to automatically design efficient neighborhood structures, without any human analysis.…”

Section: Related Workmentioning

confidence: 99%

Ejection chain moves for automatic neighborhood synthesis in constrained cardinality‐minimization problems

Adamo

Ghiani

Guerriero

et al. 2019

Int Trans Operational Res

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In this paper, we deal with the problem of automatically synthesizing “good” neighborhoods for a specific class of problems, namely constrained cardinality‐minimization problems. Exploiting the peculiarity of the objective function of such problems, we develop automatic ejection chain moves that define neighborhood structures to be explored with a black‐box solver. In particular, starting from a formulation of a cardinality‐minimization problem and a feasible solution, our procedure automatically detects the “entities” involved in the problem and learns the strength of the relationships among them. This information is then used to define the characteristics of our moves that consist in ejecting one entity at a time from the solution. If one of such moves results in an infeasible solution, then feasibility is recovered by performing an additional step based on the solution of an auxiliary problem. The computational results show that, when assessed on four well‐known constrained cardinality‐minimization problems, our approach outperforms both a black‐box mixed integer programming solver and a state‐of‐the‐art model‐based neighborhood search procedure with respect to both solution quality and computing times.

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