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

Adamo, Tommaso; Ghiani, Gianpaolo; Guerriero, Emanuela; Manni, Emanuele

doi:10.1111/itor.12578

Cited by 5 publications

(6 citation statements)

References 22 publications

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“…ML techniques can be incorporated in exact solution methods to lower computational complexity and efficiently provide the optimal solution. For example, Adamo et al (2020) implemented ML techniques to develop scalable decomposition-based solution algorithms for mixed-integer programming models. Likewise, incorporating such knowledge can guide heuristic and evolutionary algorithms toward making well-informed decisions and consequently shaping more intelligent tools.…”

Section: Solution Methodsmentioning

confidence: 99%

Learn to decompose multiobjective optimization models for large‐scale networks

Aslani

Mohebbi

2022

Int Trans Operational Res

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Infrastructures can be modeled as large‐scale networks consisting of nodes and arcs, making network optimization a popular modeling option for arising problems. In specific, providing timely restoration plans for interdependent infrastructures facing disruptions has been a challenge for decision makers. In this study, we focus on geospatial (co‐location) and functional interdependencies to capture the impact of cascading failures on infrastructure systems. The dynamics of real networks are more complicated to be captured by one objective function. Therefore, we define three objective functions in three pillars of sustainability: (a) economic, (b) social, and (c) environmental. To solve the multiobjective optimization model, we develop a learn‐to‐decompose framework, consisting of a multiobjective evolutionary algorithm based on decomposition module and a Gaussian process regression (GPR) module to periodically learn from the obtained Pareto front and guide the search direction. We also included a heuristic module to address two significant challenges in restoring interdependent infrastructures: the island scenario and co‐location interdependencies. We applied the proposed framework to benchmark problems and interdependent water and transportation networks in the City of Tampa, FL. We carried out sensitivity analyses to monitor the performance of the GPR by different kernel functions. We also provided insights for decision makers by finding the trade‐off between fortification (proactive) and restoration (reactive) costs. The result demonstrates the proposed framework is feasible and applicable for large‐scale networks.

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Section: Solution Methodsmentioning

confidence: 99%

Learn to decompose multiobjective optimization models for large‐scale networks

Aslani

Mohebbi

2022

Int Trans Operational Res

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“…In addition, Adamo et al. () exploited some of the previous ideas to automatically construct a first feasible solution for a generic MIP compact formulation.…”

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%

“…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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“…Mais recentemente, Adamo et al (2020) desenvolveram um método, a partir de uma solução da relaxação linear de um problema, tem o objetivo de encontrar uma solução factível. Adamo et al (2020) também utilizam informações da solução do problema relaxado e a estrutura do problema para determinar as partições usando k-medoids balanceado.…”

Section: Combinação De Técnicas De Otimização E Aprendizado De Máquin...unclassified

Aprendizado de máquina em heurísticas de decomposição para problemas de dimensionamento de lotes

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Neste trabalho, são propostas heurísticas baseadas na partição do conjunto de variáveis dos modelos matemáticos, as quais são utilizadas para a resolução de dois problemas de dimensionamento de lotes. As heurísticas desenvolvidas são compostas de duas fases: construção de uma solução inicial e melhoria. As soluções iniciais são obtidas por meio da heurística relax-and-fix ou por um resolvedor comercial de otimização (primeira solução factível obtida). As heurísticas de melhoria são do tipo fix-and-optimize com partições clássicas e ADN (Automatically designed neighborhoods), que constrói uma vizinhança de forma automática, utilizando aprendizado de máquina não supervisionado, ou seja, usamos dois algoritmos de agrupamento: o k-means e o k-medoids. Nos experimentos computacionais, abordamos o problema de dimensionamento de lotes com múltiplas plantas distintas e o problema de dimensionamento de lotes multiestágio. Para realizar a comparação entre as heurísticas, foram utilizadas instâncias da literatura e as soluções são comparadas com as soluções obtidas por um otimizador comercial.

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

Cited by 5 publications

References 22 publications

Learn to decompose multiobjective optimization models for large‐scale networks

Learn to decompose multiobjective optimization models for large‐scale networks

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

Aprendizado de máquina em heurísticas de decomposição para problemas de dimensionamento de lotes

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