Matching based very large-scale neighborhoods for parallel machine scheduling

Brueggemann, Tobias; Hurink, Johann L.

doi:10.1007/s10732-010-9149-8

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…A good example of the second type of VLSN approach is the ‘matching’ neighbourhood for the TSP (Sarvanov and Doroshko, 1981), which was later adapted to several other problems, such as scheduling problems (Brueggemann and Hurink, 2007, 2011) and the GAP (Mitrović‐Minić and Punnen, 2008, 2009).…”

Section: Some Other Matheuristicsmentioning

confidence: 99%

Matheuristics: survey and synthesis

Boschetti

Letchford

Maniezzo

2023

Int Trans Operational Res

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In integer programming and combinatorial optimisation, people use the term matheuristics to refer to methods that are heuristic in nature but draw on concepts from the literature on exact methods. We survey the literature on this topic, with a particular emphasis on matheuristics that yield both primal and dual bounds (i.e., upper and lower bounds in the case of a minimisation problem). We also make some comments about possible future developments.

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Section: Some Other Matheuristicsmentioning

confidence: 99%

Matheuristics: survey and synthesis

Boschetti

Letchford

Maniezzo

2023

Int Trans Operational Res

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“…Brueggemann and Hurink [7] presented a neighborhood of exponential size for the problem of scheduling independent jobs on parallel machines minimizing the weighted average completion time. The neighborhood can be searched through matchings in a certain improvement neighborhood.…”

Section: Neighborhoods Defined By Assignments and Matchingmentioning

confidence: 99%

Large Neighborhood Search

Pisinger

Røpke

2018

International Series in Operations Research &Amp; Management Science

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“…Majid et al [23] used this algorithm to solve supply chain network design problems. Brueggemann and Hurink [24] used this algorithm to solve a parallel machine scheduling problem. Guo et al [25] and Fanjul-Peyro and Ruiz [26] used this algorithm to solve parallel machines scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Very Large-Scale Neighborhood Search for Steel Hot Rolling Scheduling Problem With Slab Stack Shuffling Considerations

Shi

Liu

2021

IEEE Access

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We study a steel hot rolling scheduling (HRS) problem considering slab stack shuffling (SSS), which is a kind of key issues in the steel strip production. The HRS problem is to select suitable slabs for a predefined sequence of hot rolling slots, from their respective candidate slab sets so that the number of shuffling operations of slabs is minimized. Different from the most researches, we consider a situation where there are intersections among candidate slab sets and shuffled slabs will not return original stacks. Basing on a special index method of slabs, we present an integer linear programming (ILP) to compute approximation solutions of the problem. In order to improve the solutions obtained by the ILP model, we propose a very large-scale neighborhood (VLSN) search algorithm. In the VLSN search process, the solutions of HRS problems are improved by an iteration procedure that partial solutions are interactively destroyed and repaired. In each iteration, partitioned hot rolling slots correspond to a sub-problem. In the repair operations, we propose an efficient branch-and-bound algorithm for solving sub-problem. The computational results on a number of different scale simulated instances show that the VLSN search algorithm is efficient for solving this kind of HRS problems.INDEX TERMS Branch-and-bound algorithm, local search, steel hot rolling schedule, slab stack shuffling, very large-scale neighborhood search.

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Matching based very large-scale neighborhoods for parallel machine scheduling

Cited by 10 publications

References 22 publications

Matheuristics: survey and synthesis

Matheuristics: survey and synthesis

Large Neighborhood Search

Very Large-Scale Neighborhood Search for Steel Hot Rolling Scheduling Problem With Slab Stack Shuffling Considerations

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