Neighborhood Synthesis from an Ensemble of MIP and CP Models

Adamo, Tommaso; Calogiuri, Tobia; Ghiani, Gianpaolo; Grieco, A; Guerriero, Emanuela; Manni, Emanuele

doi:10.1007/978-3-319-50349-3_15

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…In particular, Adamo et al. () fed the Automatic Neighborhood Design algorithm with an ensemble of CP and MIP models, whereas Adamo et al. () used the above concepts to automatically instantiate a variable neighborhood descent procedure.…”

Section: Related Workmentioning

confidence: 99%

“…Based on such features, some neighborhood design mechanisms were automatically derived and, finally, a proper setting of these mechanisms was determined by an automatic algorithm configuration phase. These approaches were recently extended by Adamo et al (2016Adamo et al ( , 2017bAdamo et al ( , 2020. In particular, Adamo et al (2016) fed the Automatic Neighborhood Design algorithm with an ensemble of CP and MIP models, whereas Adamo et al (2017b) used the above concepts to automatically instantiate a variable neighborhood descent procedure.…”

Section: Related Workmentioning

confidence: 99%

“…These approaches were recently extended by Adamo et al (2016Adamo et al ( , 2017bAdamo et al ( , 2020. In particular, Adamo et al (2016) fed the Automatic Neighborhood Design algorithm with an ensemble of CP and MIP models, whereas Adamo et al (2017b) used the above concepts to automatically instantiate a variable neighborhood descent procedure. In addition, Adamo et al (2020) exploited some of the previous ideas to automatically construct a first feasible solution for a generic MIP compact formulation.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Adamo

Ghiani

Guerriero

et al. 2019

Int Trans Operational Res

Self Cite

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show abstract

“…The authors discuss some limitations of supervised learning, such as the need to determine optimal TSP solutions (the targets), that in turn, may be ill-defined when those solutions are not optimal, or when there are multiple solutions. The reference [23] devised a three-step procedure, starting with a semantic feature extraction from the MIP model of the TSP. The extracted features are then exploited to derive a neighborhood design mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Learned Upper Bounds for the Time-Dependent Travelling Salesman Problem

et al. 2023

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Fleet management plays a central role in several application contexts such as distribution planning, mail delivery, garbage collection, salt gritting, field service routing. Since road congestion has a big impact on driving times, fleet management can be enhanced by taking into account data on current traffic conditions. Today, most carriers gather high-quality historical traffic data by using global position system information. These data serve as an input for defining time-dependent travel times, i.e. travel times changing according to traffic conditions throughout the day. Given a fixed-size fleet of vehicles and a graph with arc traversal times varying over time, Time-Dependent Vehicle Routing Problems aim to select the best routes while minimizing the travelling costs. The basic version with only one route is usually referred to as the Time-Dependent Travelling Salesman Problem. The main goal of this work is to define tight upper bounds for this problem by reusing the information gained when solving instances with similar features. This is customary in distribution management, where vehicle routes have to be generated over and over again with similar input data. To this aim, the authors devise an upper bounding technique based on the solution of a classical (and simpler) time-independent Asymmetric Travelling Salesman Problem, where the constant arc costs are suitably defined by the combined use of a Linear Program and a mix of unsupervised and supervised Machine Learning techniques. The effectiveness of this approach has been assessed through a computational campaign on the real travel time functions of two European cities: Paris and London. The overall average gap between the proposed heuristic and the best-known solutions is about 0.001%. For 31 instances, new best solutions have been obtained. INDEX TERMS machine learning, path ranking invariance, time-dependent routing, travelling salesman problemThis article has been accepted for publication in IEEE Access.

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“…In addition, to extract from both the model and the solution the information needed to obtain the clusters, we also take advantage of some concepts presented in Adamo et al. (, , ). Another difference of our approach lies in the clustering technique.…”

Section: Introductionmentioning

confidence: 99%

A learn‐and‐construct framework for general mixed‐integer programming problems

Adamo

Ghiani

Guerriero

et al. 2018

Int Trans Operational Res

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In this paper, we propose a new framework for finding an initial feasible solution from a mixed‐integer programming (MIP) model. We call it learn‐and‐construct since it first exploits the structure of the model and its linear relaxation solution and then uses this knowledge to try to produce a feasible solution. In the learning phase, we use an unsupervised learning algorithm to cluster entities originating the MIP model. Such clusters are then used to decompose the original MIP in a number of easier sub‐MIPs that are solved by using a black box solver. Computational results on three well‐known problems show that our procedure is characterized by a success rate larger than both the feasibility pump heuristic and a state‐of‐the‐art MIP solver. Furthermore, our approach is more scalable and uses less computing time on average.

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Neighborhood Synthesis from an Ensemble of MIP and CP Models

Cited by 5 publications

References 9 publications

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

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

Learned Upper Bounds for the Time-Dependent Travelling Salesman Problem

A learn‐and‐construct framework for general mixed‐integer programming problems

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