Multi-objective location modeling of urban parks and open spaces: Continuous optimization

Neema, Meher Nigar; Ohgai, Akira

doi:10.1016/j.compenvurbsys.2010.03.001

Cited by 51 publications

(39 citation statements)

References 30 publications

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“…So, a set of solutions is said to be a Pareto front or Pareto solution if no element of this set dominates any other solutions [50]. For acquisition more detail about on Pareto optimal solutions, one can be referred to [31,51] .…”

Section: Constrain Handling Methods For the Mocssmentioning

confidence: 99%

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

2018

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Section: Constrain Handling Methods For the Mocssmentioning

confidence: 99%

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

2018

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Section: Non-dominated Solutions Retaining the Existing Fire Stationsmentioning

confidence: 99%

“…Among the independent implementations of the model, the best implementation or run is run 5, as it has the lowest value of the sum and the highest number of non-dominated solutions compared to the other runs [14]. Figure 8 shows the non-dominated solutions in 150 repetitions of each model's Table 5 shows the solutions of several independent implementations of the model with 150 repetitions, including the total number of non-dominated solutions in each implementation of the model, the given weights for each objective function, the minimum and maximum distances between adjacent stations, the demand allocations of f3, and the sum.…”

Section: Non-dominated Solutions Retaining the Existing Fire Stationsmentioning

confidence: 99%

“…A genetic algorithm was applied for solving the location-allocation problem for parks and open spaces optimization, but the capacity of facilities and the order of demands were not considered, and the problem space was continuous [14]. Bashiri and Bakhtiarifar used simulated annealing for a problem with one objective, but the present study has three objectives, which complicates the problem space [15].…”

Section: Introductionmentioning

confidence: 99%

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The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

Bolouri

Vafaeinejad

Alesheikh

et al. 2018

IJGI

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Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such as minimizing the distance and time as well as maximizing the coverage. After tuning the parameters of the algorithms using sensitivity analysis, they were used separately to process data for Region 11, Tehran. The results showed that the genetic algorithm was more efficient than simulated annealing, and therefore, the genetic algorithm was used in later steps. Next, we increased the number of stations. Results showed that the model can successfully provide seven optimal locations and allocate high demands (280,000) to stations in a discrete space in a GIS, assuming that the stations' capacities are known. Following this, we used a weighting program so that in each repetition, we could allot weights to each target randomly. Finally, by repeating the model over 10 independent executions, a set of solutions with the least sum and the highest number of non-dominated solutions was selected from among many non-dominated solutions as the best set of optimal solutions.

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“…In reality, numerous objectives, constraints, and stakeholders are involved in planning, which can be conflicting [12]. Researchers have called this a "nonlinear problem," which cannot be solved with qualitative knowledge or traditional linear modeling.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Land-Use Allocation Considering Landslide Risk under Climate Change: Case Study in Pyeongchang-gun, Korea

et al. 2017

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Abstract:Extreme landslides triggered by rainfall in hilly regions frequently lead to serious damage, including casualties and property loss. The frequency of landslide occurrences may increase under climate change, due to the increasing variability of precipitation. Developing urban areas outside landslide risk zones is the most effective method of reducing or preventing damage; however, planning in real life is a complex and nonlinear problem. For such multi-objective problems, genetic algorithms may be the most appropriate optimization tools. Therefore, in this study, we suggest a comprehensive land-use allocation plan using the Non-dominated Sorting Genetic Algorithm II to overcome multi-objective problems, including the minimization of landslide risk, minimization of change, and maximization of compactness. Our study area is Pyeongchang-gun, the host city of the 2018 Winter Olympics in Korea, where high development pressure has resulted in urban sprawl into the hazard zone where a large-scale landslide occurred in 2006. We obtain 100 Pareto plans that are better than the actual land use data for at least one objective, with five plans that explain the trade-offs between meeting the first and second objectives. The results can be used by decision makers for better urban planning and for climate change-related spatial adaptation.

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Multi-objective location modeling of urban parks and open spaces: Continuous optimization

Cited by 51 publications

References 30 publications

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

Multi-Objective Land-Use Allocation Considering Landslide Risk under Climate Change: Case Study in Pyeongchang-gun, Korea

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