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This article describes a decision support system for land-use structure optimization and land-use allocation. This system was established for the rural land managers to explore their land use options. It integrated database technology, expert system technology and spatial decision support system technology. The DSS consist of four components: a geo...
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... A recent survey of current heuristic approaches has recorded over 130 algorithms that the majority of them designed and applied to solve real-parameter function optimization problems (Nyarko et al. 2014). Some of these methods include Multidimensional Choice (MDCHOICE) (Nath et al. 2000), Genetic Algorithm (GA) (Matthews et al. 2000;Schwaab et al. 2018;Paritosh et al. 2019), use of different algorithm such as NSGA-II-LUM (Datta et al. 2007), NSGA-II (Lubida et al. 2019;Sharmin et al. 2019;Gao et al. 2021), Genetic land ), design of decision support system or DSS (Xiaoli et al. 2009), multi-objective land allocation (MOLA) (Hajehforooshnia et al. 2011;Kabodi et al. 2012;Shayegan et al. 2012) and design of decision support system or DSS (Xiaoli et al. 2009). ...
... A recent survey of current heuristic approaches has recorded over 130 algorithms that the majority of them designed and applied to solve real-parameter function optimization problems (Nyarko et al. 2014). Some of these methods include Multidimensional Choice (MDCHOICE) (Nath et al. 2000), Genetic Algorithm (GA) (Matthews et al. 2000;Schwaab et al. 2018;Paritosh et al. 2019), use of different algorithm such as NSGA-II-LUM (Datta et al. 2007), NSGA-II (Lubida et al. 2019;Sharmin et al. 2019;Gao et al. 2021), Genetic land ), design of decision support system or DSS (Xiaoli et al. 2009), multi-objective land allocation (MOLA) (Hajehforooshnia et al. 2011;Kabodi et al. 2012;Shayegan et al. 2012) and design of decision support system or DSS (Xiaoli et al. 2009). ...
The main viewpoint of land use planning is to resolve the conflict between competing land uses in one place that is carried out based on their economic, social and environmental suitability. Many different approaches have been designed to resolve this conflict but any of them have done. This study aimed to assess accuracy and validity compared four optimization algorithms, including linear programming (LP), simulated annealing (SA), multi-objective land use allocation (MOLA) and multidimensional choice (MDCHOICE) for land use planning in Gorgan Township. At first, land use allocation was done through the LP algorithm. Then, using achieved pixels quantity from LP optimization, the land uses was optimized in term of spatial suitability by other algorithms. The allocation maps generated through these algorithms were compared using statistical methods and landscape metrics. The map provided through MDCHOICE was used as a reference map and that generated through MOLA, SA and LP as a comparative one in a validation process. The similarities and agreements of the two maps were evaluated with various algorithms in IDRISI and MCK software. The results demonstrate that: (1) It follows that the LP algorithm to determine the optimal number of pixels to lead MDCHOICE has helped in the spatial allocation properly. (2) MOLA had a better performance for development in terms of all landscape metrics and SA algorithm was better in forestry land use allocation. Therefore the quality of spatial allocation both of them needs to be improved. (3) Although the LP algorithm is not superior to the algorithms based on the dimensions studied in this research, in terms of achieving the defined goals and the degree of deviation from the number of cells defined for each land use has the least violation. (4) The superiority of MDCHOICE over MOLA, SA and LP and applicability of various kappa statistics for comparing the maps for a more in depth analysis of agreements and disagreements.
... Beijing and surroundings, China [35] Optimization of the allocation of industries ...
A transition to a bioeconomy implies an increased focus on efficient and sustainable use of biological resources. A common, but often neglected feature of these resources is their location dependence. To optimize their use, for example in bioeconomic industrial clusters, this spatial aspect should be integrated in analyses. Optimal design and localization of a bioeconomic cluster with respect to the various biological and non-biological resources required for the cluster, the composition of industrial facilities in the cluster, as well as the demands of the outputs of the cluster, is crucial for profitability and sustainability. We suggest that optimal design and location of bioeconomic clusters can benefit from the use of a Multicriteria Decision Analysis (MCDA) in combination with Geographic Information Systems (GIS) and Operations Research modeling. The integration of MCDA and GIS determines a set of candidate locations based on various criteria, including resource availability, accessibility, and usability. A quantitative analysis of the flow of resources between and within the different industries is then conducted based on economic Input-Output analysis. Then, the cluster locations with the highest potential profit, and their composition of industrial facilities, are identified in an optimization model. A case study on forest-based bioeconomic clusters in the Østfold county of Norway is presented to exemplify this methodology, the expectation being that further implementation of the method at the national level could help decision makers in the planning of a smoother transition from a fossil-based economy to a bioeconomy.
... Then, the model was developed by Yao, et al. (2017) in which this model considers the types of land-use. In addition, Li, et al. (2009) developed a spatial optimization model for landuse planning that aims to maximize the comprehensive index and density index. State of the art about land-use allocation problem can be seen in Table 1. ...
... Based on the explanation above, this paper discusses about Robust Optimization model for spatial land-use allocation problem in Jatinangor. In this research, spatial optimization model for land-use allocation by Yao, et al. (2017) and Li, et al. (2009) will be reformulated and uncertainty data will be considered. The uncertainty set used in this research is box uncertainty. ...
... In this paper, optimization model for land-use allocation is based on the spatial optimization model by Yao, et al. (2017) and Li, et al. (2009). ...
Land-use planning become an important thing to do because some types of land-use can have an impact to environment and life quality. Land-use planning is generally an activity that involves the allocation of activities in a particular land. Spatial Optimization can be applied in land-use planning activity. This research aims to make Robust Optimization model for spatial land-use allocation problem in Jatinangor. Optimization model for land-use allocation problem aims to determine the percentage of land-use changes that can maximize comprehensive index and compactness index. In land-use planning, there are several uncertainty factors. Therefore, it's needed an approach that can handle uncertainty factor, the approach used in this research is Robust Optimization. The result of Robust Optimization Model for land-use allocation problem which is solved by the box uncertainty set approach is a computationally tractable optimization model.
... Land-use allocation is a complex and challenging spatial optimization problem. First, the interest conflicts among multiple objectives should be resolved (such as ecological protection and economic development) (Chandramouli et al. 2009;Li et al. 2009;Cao et al. 2011;Ma et al. 2011;Masoomi et al. 2013). Second, spatial characteristics should be considered simultaneously with attribute characteristics during the decision-making process (Chandramouli et al. 2009;Li et al. 2009;Cao et al. 2011;Ma et al. 2011;Masoomi et al. 2013). ...
... First, the interest conflicts among multiple objectives should be resolved (such as ecological protection and economic development) (Chandramouli et al. 2009;Li et al. 2009;Cao et al. 2011;Ma et al. 2011;Masoomi et al. 2013). Second, spatial characteristics should be considered simultaneously with attribute characteristics during the decision-making process (Chandramouli et al. 2009;Li et al. 2009;Cao et al. 2011;Ma et al. 2011;Masoomi et al. 2013). Third, diversity of land-use types and locations creates massive feasible land-use allocation schemes. ...
The harmonization of environmental protection with land use for socioeconomic development is one of the major challenges that most Chinese cities are facing in the rapid urbanization and industrialization process. This paper uses a particle swarm optimization algorithm combined with multiple objectives to find the optimum land-use arrangement considering both development and protection objectives and land-use policy constraints simultaneously. The authors consider four objective functions for land-use allocation: maximizing land-use suitability, maximizing land ecosystem service value, maximizing land transformation benefit, and maximizing spatial compactness of land use. These four objective functions are combined into an integrated single-objective function which is used as the fitness function for the particle swarm optimization algorithm in land-use allocation optimization. The authors demonstrate an application of the method with respect to optimizing the arrangement of land-use in Changzhou, China. A set of optimum land-use arrangements is provided by setting varied weight sets to the four objective functions, which are analyzed in terms of the priorities of the four objective functions. Decision makers can select the most appropriate land-use arrangement based on their priorities. The results indicate that the multiobjective particle swarm optimization approach is a promising method for generating land-use spatial allocation alternatives for land-use planning and management.
... In such situations, multiobjective optimization (MOO) techniques are usually used. They can find nondominated solutions (in this case sites) that best satisfy the different and sometimes conflicting objectives of decision makers (Zitzler and Thiele 1998, Coello Coello, Lamount, and Veldhuizen 2007, Datta, Deb, and Fonseca 2007, Xiaoli, Chen, and Daoliang 2009. When using MOO, the simplest way to handle multiple objectives is to incorporate all of them in a single function, and to optimize that function afterwards. ...
Usually, allocation of resources is an optimization problem which involves a variety of conflicting economic, social, and ecological objectives. In such a process, advanced geographic analyst tool for manipulation of spatial data and satisfaction of multiple objectives is essential to the success of decision-making. The present research intends to demonstrate the application of a multiobjective optimization method based on NSGA-II (we call it HNSGA-II), along with Geographical Information System (GIS) to select suitable sites for the establishment of large industrial units. Having defined the elements of HNSGA-II for the site selection of industrial units, the method is tested on the data of Zanjan province, Iran, as the case study. The results showed that the proposed approach can easily find a variety of optimized solutions, giving the decision-makers the possibility to opt for the most propitious solution. Using this method, the achievement level regarding each objective function can be studied for any of the nondominated solutions.
... People generally migrate towards the urban areas for better health, education, transportation and employment opportunities, etc. Protecting agricultural land however should be the highest priority to sustain agriculture and to secure enough food (SEMCOG, 2003). Land-suitability analyses are often performed to find agricultural zones to be protected on the priority bases (Xiaoli et al., 2009). This however should be combined with the Land Use and Land Cover (LULC) change analysis to predict the agricultural zones that are at a risk of being converted into the built-up zones. ...
Agricultural land needs to be protected for food production. Our objective is to provide a decision support for protecting the agricultural land in Lahore, Pakistan. To do so, first we classified the Land Use and Land Cover (LULC) from Landsat images for the years 2009 and 2012. Second, we performed Markov chain analysis to simulate the LULC change over time. The resultant probability of LULC inter conversion was then combined with the Cellular Automata (CA). Third, the spatio-temporal patterns of LULC change from CA-Markov were integrated with the land fitness map obtained through the analysis of soil chemical properties. We observed a gradual increase in built-up land and a decrease in agricultural land from years 2009 to 2012, with an increase of 18.8% to 60.3% in the built- up land, and a decrease of 43.5% to 35.9% in the agricultural land. The Markov-CA analysis further predicted a significant (p = 0.69) LULC change from year 2012 to year 2015, with an increase of 2% in built-up land, and a decrease of 1.3% in agricultural land. The resultant map shows zones to be predicted on priority bases, which can be useful in making comprehensive land policies to protect agricultural land and to secure food in developing countries
... Approaches are usually based on combinations of map overlay modeling, multi-criteria evaluation using landscape metrics, and optimization methods or artificial intelligence techniques (Malczewski, 2004). The different optimization methods include linear programming (e.g., Romanos and Hatmaker, 1980;Zhang and Wright, 2004), tabu search (Qi et al., 2008), simulated annealing (e.g., Aerts et al., 2003;Duh and Brown, 2007;Sante-Riveira et al., 2008), and evolutionary algorithms (e.g., Aerts et al., 2005;Bennett et al., 2004;Holzkämper and Seppelt, 2007;Matthews et al., 2006;Xiao, 2008;Xiaoli et al., 2009). Holzkämper et al. (2006) introduced a map to genome transformation that is based on patches in order to optimize land use patch configurations within an evolutionary algorithm, and a genome to map algorithm that allows for evaluation of individuals on a spatially explicit basis. ...
The reuse of underused or abandoned contaminated land, so-called brownfields, is increasingly seen as an important means for reducing the consumption of land and natural resources. Many existing decision support systems are not appropriate because they focus mainly on economic aspects, while neglecting sustainability issues. To fill this gap, we present a framework for spatially explicit, integrated planning and assessment of brownfield redevelopment options. A multi-criteria genetic algorithm allows us to determine optimal land use configurations with respect to assessment criteria and given constraints on the composition of land use classes, according to, e.g., stakeholder preferences. Assessment criteria include sustainability indicators as well as economic aspects, including remediation costs and land value. The framework is applied to a case study of a former military site near Potsdam, Germany. Emphasis is placed on the trade-off between possibly conflicting objectives (e.g., economic goals versus the need for sustainable development in the regional context of the brownfield site), which may represent different perspectives of involved stakeholders. The economic analysis reveals the trade-off between the increase in land value due to reuse and the costs for remediation required to make reuse possible. We identify various reuse options, which perform similarly well although they exhibit different land use patterns. High-cost high-value options dominated by residential land use and low-cost low-value options with less sensitive land use types may perform equally well economically. The results of the integrated analysis show that the quantitative integration of sustainability may change optimal land use patterns considerably.
... Land-use optimization is a method of resource allocation, in which different activities or land uses are allocated to specific units of land area. These kinds of problems need multiple and often conflicting objectives (such as ecological and economic objectives) to be considered simultaneously (Chandramouli et al. 2009, Xiaoli et al. 2009, Cao et al. 2011, Shifa et al. 2011. Therefore, land-use allocation can be considered as an optimization problem. ...
Considering the ever-increasing urban population, it appears that land management is of major importance. Land uses must be properly arranged so that they do not interfere with one another and can meet each other's needs as much as possible; this goal is a challenge of urban land-use planning. The main objective of this research is to use Multi-Objective Particle Swarm Optimization algorithm to find the optimum arrangement of urban land uses in parcel level, considering multiple objectives and constraints simultaneously. Geospatial Information System is used to prepare the data and to study different spatial scenarios when developing the model. To optimize the land-use arrangement, four objectives are defined: maximizing compatibility, maximizing dependency, maximizing suitability, and maximizing compactness of land uses. These objectives are characterized based on the requirements of planners. As a result of optimization, the user is provided with a set of optimum land-use arrangements, the Pareto-front solutions. The user can select the most appropriate solutions according to his/her priorities. The method was tested using the data of region 7, district 1 of Tehran. The results showed an acceptable level of repeatability and stability for the optimization algorithm. The model uses parcel instead of urban blocks, as the spatial unit. Moreover, it considers a variety of land uses and tries to optimize several objectives simultaneously.
... Model dan metode-metode optimisasi telah banyak digunakan pada masalah alokasi penggunaan lahan dan masih terus berkembang sampai saat ini. Telah banyak penelitian yang dilakukan mengenai optimisasi penggunaan lahan, beberapa diantaranya Verburg, et al. [9] yang membahas mengenai pemodelan spasial dinamis untuk penggunaan lahan regional dengan menggunakan model CLUE-S, Stewart, et al. [7] membahas pendekatan Algoritma Genetika untuk masalah alokasi penggunaan lahan multiobjektif, Zielinska et al. [11] yang memaparkan optimisasi spasial sebagai salah satu teknik generatif untuk alokasi penggunaan lahan multiobjektif yang berkesinambungan, serta Li, et al. [4] yang membahas sistem pendukung keputusan spasial untuk optimisasi terstruktur dari alokasi penggunaan lahan. Penelitian-penelitian mengenai penggunaan lahan di atas bertujuan untuk mencari alokasi penggunaan lahan yang optimal. ...
... Penelitian-penelitian mengenai penggunaan lahan di atas bertujuan untuk mencari alokasi penggunaan lahan yang optimal. Menurut Li, et al. [4] , model alokasi penggunaan lahan (APL) dapat digunakan untuk meminimumkan fungsi tujuan (misalnya minimumkan biaya) ataupun memaksimumkan fungsi tujuan (misalnya maksimumkan indeks kepadatan). Dalam makalah ini dilakukan penentuan indeks komprehensif dan indeks kepadatan dari fungsi objektif pada model APL yang digunakan oleh Li, et al. [4], dimana dalam makalah tersebut tidak disebutkan bagaimana cara penentuan kedua koefisien tersebut. ...
... Menurut Li, et al. [4] , model alokasi penggunaan lahan (APL) dapat digunakan untuk meminimumkan fungsi tujuan (misalnya minimumkan biaya) ataupun memaksimumkan fungsi tujuan (misalnya maksimumkan indeks kepadatan). Dalam makalah ini dilakukan penentuan indeks komprehensif dan indeks kepadatan dari fungsi objektif pada model APL yang digunakan oleh Li, et al. [4], dimana dalam makalah tersebut tidak disebutkan bagaimana cara penentuan kedua koefisien tersebut. Penentuan kedua indeks tersebut dilakukan dengan menggunakan analisis data spasial dengan cara menentukan matriks bobot seragam untuk menentukan. ...
A good land-use allocation is an important effort to create a safe urban space, comfortable, productive, and sustainable. This is as stipulated in UU RI No. 26 of 2007 regarding spatial planning in Indonesia. Therefore the optimization on allocation land use is important to do. In this paper we present a different approach to solve the land-use allocation problem, i.e,, by using multiobjective optimization, branch and bound methods and generating spatial data analysis via uniformly weighted matrix. In this problem, the objective function is to maximize the total density index and total comprehensive index of the land-use types. An illustrative data that refer to Region Regulation for Bandung No. 09 of 2009 is presented.
Increasing population causes escalation in demand of land for food production, settlements, and public facilities. Meanwhile, land availability is fixed and limited which encourage marginal or unsuitable land utilization. Land utilization for food production which not comply its capability would have negative effect both physically and economically. To avoid those effects, optimal cropping pattern should be determine to support sustainable agricultural development. This research aims to determine optimal land for food production areas by considering the potential erosion, land rent, and rice sufficiency. Linear goals programming is employed to devise the optimal choice of land use pattern. The study area includes four sub-districts namely Cipeundeuy, Kalijati, Pabuaran, and Patokbeusi that is situated in Subang, West Java. Optimum cultivation pattern on the agricultural land was organized to achieve three targets including (1) to minimized erosion for land preservation, (2) to provide the highest economic benefits for farmers, and (3) to meet rice sufficiency of study area. This study designed twelve scenarios with different targets combination. It is showed that scenario VI and XII is the best combination comply the expected targets. Both of these scenarios produce optimal cropping patterns with the lowest erosion values of 85.528,10 tons/year, generate the highest economic benefit for farmers at Rp 525.890.970.000,-and yield 46.598 tons rice for scenario VI and 181.730 tons rice for scenario XII. Decision tree analysis shows that spatial distribution pattern of land optimal optimization were strongly influenced by the economic benefits.
Keywords: cropping pattern, erosion, land rent, linear goals programming, rice sufficiency
