A Multistart Alternating Tabu Search for Commercial Districting

Gliesch, Alex; Ritt, Marcus; Moreira, Mayron César O.

doi:10.1007/978-3-319-77449-7_11

Cited by 6 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the publication of this thesis, we have adapted some of our methods and proposed a tabu search heuristic for a problem in commercial districting that significantly outperformed existing methods in the literature. This work was a best paper candidate at the European Conference on Evolutionary Computation in Combinatorial Optimisation (Qualis B1) [Gliesch et al 2018].…”

Section: Publicationsmentioning

confidence: 99%

A Genetic Algorithm For Fair Land Allocation

Gliesch

Ritt

2019

Anais Do Concurso De Teses E Dissertações Da SBC (CTD-SBC)

Self Cite

View full text Add to dashboard Cite

The goal ofagrarian reform projects is the redistribution offarmland from large latifundia to smaller, often family farmers. One ofthe main problems the Brazilian National Institute ofColonization and Agrarian Reform (INCRA) has to solve is to subdivide a large parcel ofland into smaller lots that are balan- ced with respect to certain attributes. This problem is difficult since it considers several constraints originating from legislation as well as ethical considerati- ons. Current solutions are computer-assisted, but manual, time-consuming and error-prone, leading to rectangular lots ofsimilar areas which are uneven with respect to soil aptitude and access to hydric resources. In this thesis, we propose a genetic algorithm to produce fair land subdivisions automatically. We pre- sent a greedy randomized constructive heuristic based on location-allocation to generate initial solutions, as well as mutation and recombination operators that consider characteristics of the problem. Experiments on real-world and artificial instances confirm the effectiveness of the different components of our method, and show that it leads to more fair solutions than those currently ap- plied in practice.

show abstract

Section: Publicationsmentioning

confidence: 99%