An Evolutionary Algorithm for Column Generation in Integer Programming: An Effective Approach for 2D Bin Packing

Puchinger, Jakob; Raidl, Günther R.

doi:10.1007/978-3-540-30217-9_65

Cited by 26 publications

(11 citation statements)

References 8 publications

(16 reference statements)

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“…Puchinger and Raidl [32,33] propose new integer linear programming formulations for the three-stage two-dimensional bin packing problem. Based on these formulations, a branch-and-price algorithm was developed in which fast column generation is performed by applying a hierarchy of four methods: (a) a greedy heuristic, (b) an evolutionary algorithm, (c) solving a restricted form of the pricing problem using CPLEX, and finally (d) solving the complete pricing problem using CPLEX.…”

Section: Metaheuristics For Obtaining Incumbent Solutions and Boundsmentioning

confidence: 99%

Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification

Puchinger

Raidl

2005

Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach

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216

118

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Abstract. In this survey we discuss different state-of-the-art approaches of combining exact algorithms and metaheuristics to solve combinatorial optimization problems. Some of these hybrids mainly aim at providing optimal solutions in shorter time, while others primarily focus on getting better heuristic solutions. The two main categories in which we divide the approaches are collaborative versus integrative combinations. We further classify the different techniques in a hierarchical way. Altogether, the surveyed work on combinations of exact algorithms and metaheuristics documents the usefulness and strong potential of this research direction.

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Section: Metaheuristics For Obtaining Incumbent Solutions and Boundsmentioning

confidence: 99%

Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification

Puchinger

Raidl

2005

Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach

Self Cite

216

118

View full text Add to dashboard Cite

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“…Such methods typically require problem-specific tuning and can suffer from performance issues. More recently, hybrids involving meta-heuristics with constraint and integer programming have demonstrated the effectiveness of such integrations (Ernst 2010; Meyer and Ernst 2004;Puchinger and Raidl 2004;Thiruvady et al 2009Thiruvady et al , 2012. In this paper we demonstrate how an effective matheuristic can be created by combining Lagrangian relaxation and ant colony optimisation (ACO) and apply this hybrid method to the car sequencing problem.…”

Section: Introductionmentioning

confidence: 94%

A Lagrangian-ACO matheuristic for car sequencing

Thiruvady

Ernst

Wallace

2014

EURO Journal on Computational Optimization

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In this study, we investigate a hybrid Lagrangian relaxation ant colony optimisation for optimisation version of the car sequencing problem. Several cars are required to be scheduled on an assembly line and each car requires a number of options such as sunroof and/or air conditioning. These cars are required to be sequenced such that sub-sequences of specific sizes may only include a limited number of any option. While this is usually a hard constraint, in this study we treat it as a soft constraint and further require the utilisation of options be modulated across the sequence leading to the optimisation problem. We investigate various Lagrangian heuristics, ant colony optimisation (ACO) and hybrids of these methods. The results show that the Lagrangian-ACO hybrid is the best performing method for up to 300 cars.

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“…The way these approaches are combined may vary from problem to problem as can be seen in [14,9,7]. The way these approaches are combined may vary from problem to problem as can be seen in [14,9,7].…”

Section: Fig 2 the Ea Ages' Schemementioning

confidence: 99%

Hybrid Heuristics for Dynamic Resource-Constrained Project Scheduling Problem

Silva

Ochi

2010

Hybrid Metaheuristics

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Dynamic Resource-Constrained Project Scheduling Problem (DRCPSP) is a scheduling problem that works with an uncommon kind of resources: the Dynamic Resources. They increase and decrease in quantity according to the activated tasks and are not bounded like other project scheduling problems. This paper presents a new mathematical formulation for DRCPSP as well as two hybrid heuristics merging an evolutionary algorithm with an exact approach. Computational results show that both hybrid heuristics present better results than the stateof-the-art algorithm for DRCPSP does. The proposed formulation also provides better bounds.

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An Evolutionary Algorithm for Column Generation in Integer Programming: An Effective Approach for 2D Bin Packing

Cited by 26 publications

References 8 publications

Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification

Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification

A Lagrangian-ACO matheuristic for car sequencing

Hybrid Heuristics for Dynamic Resource-Constrained Project Scheduling Problem

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