A mixed‐integer linear programming model and a metaheuristic approach for the selection and allocation of land parcels problem

This article is about the current agricultural scenario, where large‐scale production causes large amounts of food to be transported to various points of consumption, causing substantial emissions of so‐called greenhouse gases and increasing the carbon footprint. Land use optimization and land parce...

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Bibliographic Details
Published in:International transactions in operational research 2023-07, Vol.30 (4), p.1730-1754
Main Authors: Gil, Alejandro Fernández, Sánchez, Mariam Gómez, Castro, Carlos, Pérez‐Alonso, Alain
Format: Article
Language:English
Subjects:
Adaptive search techniques
agriculture
Combinatorial analysis
combinatorial optimization
Food
GRASP
Greenhouse gases
Heuristic methods
Integer programming
Land use
Linear programming
metaheuristic
mixed integer linear programming
Operations research
packing problem
selection and allocation of land parcels problem
Shape optimization
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Summary:This article is about the current agricultural scenario, where large‐scale production causes large amounts of food to be transported to various points of consumption, causing substantial emissions of so‐called greenhouse gases and increasing the carbon footprint. Land use optimization and land parcel allocation are essential areas of agriculture research that currently represent relevant challenges and are classified as combinatorial optimization problems. In this context, the Selection and Allocation of Land Parcels Problem (SA‐LPP) is proposed; its goal is to optimize the selection and allocation of land parcels with rectangular shapes in small areas available for food production. We propose a reformulation of the SA‐LPP as a variant of the two‐dimensional orthogonal packing problem (2OPP), called Group‐2OPP. This problem was solved through a Mixed‐Integer Linear Programming (MILP) model, but due to the model complexity, we also propose a Greedy Randomized Adaptive Search Procedure metaheuristic approach. Some sensitivity analyses were performed as well to evaluate the impact of parameters on the solutions. Computational results show that the proposed metaheuristic outperforms the MILP model in terms of solution quality and computational times.
ISSN:0969-6016
1475-3995
DOI:10.1111/itor.13115