Exact Zoning Optimization Model for Marine Spatial Planning (MSP)
Marine spatial planning (MSP) has recently attracted more attention as an efficient decision support tool. MSP is a strategic and long-term process gathering multiple competing users of the ocean with the objective to simplify decisions regarding the sustainable use of marine resources. One of the c...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:bea73a07dba14c0fb1c4996897aa090c2021-11-18T09:31:22ZExact Zoning Optimization Model for Marine Spatial Planning (MSP)2296-774510.3389/fmars.2021.726187https://doaj.org/article/bea73a07dba14c0fb1c4996897aa090c2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmars.2021.726187/fullhttps://doaj.org/toc/2296-7745Marine spatial planning (MSP) has recently attracted more attention as an efficient decision support tool. MSP is a strategic and long-term process gathering multiple competing users of the ocean with the objective to simplify decisions regarding the sustainable use of marine resources. One of the challenges in MSP is to determine an optimal zone to locate a new activity while taking into account the locations of the other existing activities. Most approaches to spatial zoning are formulated as non-linear optimization models involving multiple objectives, which are usually solved using stochastic search algorithms, leading to sub-optimal solutions. In this paper, we propose to model the problem as a Multi-Objective Integer Linear Program. The model is developed for raster data and it aims at maximizing the interest of the area of the zone dedicated to the new activity while maximizing its spatial compactness. We study two resolution methods: first, a weighted-sum of the two objectives, and second, an interactive approach based on an improved augmented version of the ϵ-constraint method, AUGMECON2. To validate and study the model, we perform experiments on artificially generated data. Our experimental study shows that AUGMECON2 represents the most promising approach in terms of relevance and diversity of the solutions, compactness, and computation time.Mohadese BasiratiRomain BillotPatrick MeyerErwan BocherFrontiers Media S.A.articlemarine spatial planningmulti-objective integer linear optimizationbufferinginterestcompactnessraster dataScienceQGeneral. Including nature conservation, geographical distributionQH1-199.5ENFrontiers in Marine Science, Vol 8 (2021) |
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DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
marine spatial planning multi-objective integer linear optimization buffering interest compactness raster data Science Q General. Including nature conservation, geographical distribution QH1-199.5 |
| spellingShingle |
marine spatial planning multi-objective integer linear optimization buffering interest compactness raster data Science Q General. Including nature conservation, geographical distribution QH1-199.5 Mohadese Basirati Romain Billot Patrick Meyer Erwan Bocher Exact Zoning Optimization Model for Marine Spatial Planning (MSP) |
| description |
Marine spatial planning (MSP) has recently attracted more attention as an efficient decision support tool. MSP is a strategic and long-term process gathering multiple competing users of the ocean with the objective to simplify decisions regarding the sustainable use of marine resources. One of the challenges in MSP is to determine an optimal zone to locate a new activity while taking into account the locations of the other existing activities. Most approaches to spatial zoning are formulated as non-linear optimization models involving multiple objectives, which are usually solved using stochastic search algorithms, leading to sub-optimal solutions. In this paper, we propose to model the problem as a Multi-Objective Integer Linear Program. The model is developed for raster data and it aims at maximizing the interest of the area of the zone dedicated to the new activity while maximizing its spatial compactness. We study two resolution methods: first, a weighted-sum of the two objectives, and second, an interactive approach based on an improved augmented version of the ϵ-constraint method, AUGMECON2. To validate and study the model, we perform experiments on artificially generated data. Our experimental study shows that AUGMECON2 represents the most promising approach in terms of relevance and diversity of the solutions, compactness, and computation time. |
| format |
article |
| author |
Mohadese Basirati Romain Billot Patrick Meyer Erwan Bocher |
| author_facet |
Mohadese Basirati Romain Billot Patrick Meyer Erwan Bocher |
| author_sort |
Mohadese Basirati |
| title |
Exact Zoning Optimization Model for Marine Spatial Planning (MSP) |
| title_short |
Exact Zoning Optimization Model for Marine Spatial Planning (MSP) |
| title_full |
Exact Zoning Optimization Model for Marine Spatial Planning (MSP) |
| title_fullStr |
Exact Zoning Optimization Model for Marine Spatial Planning (MSP) |
| title_full_unstemmed |
Exact Zoning Optimization Model for Marine Spatial Planning (MSP) |
| title_sort |
exact zoning optimization model for marine spatial planning (msp) |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/bea73a07dba14c0fb1c4996897aa090c |
| work_keys_str_mv |
AT mohadesebasirati exactzoningoptimizationmodelformarinespatialplanningmsp AT romainbillot exactzoningoptimizationmodelformarinespatialplanningmsp AT patrickmeyer exactzoningoptimizationmodelformarinespatialplanningmsp AT erwanbocher exactzoningoptimizationmodelformarinespatialplanningmsp |
| _version_ |
1718420926081531904 |