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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.

The present study aimed at assessing tourism potential of a place to meet requirements of sustainable development policies. We studied the Haraz watershed because of its particular environmental characteristics and a high potential for ecotourism. The required data for this descriptive-analytical research were collected by combining field and desktop studies. First, the ecotourism capability assessment of the area was done using Arc GIS 10.3 software based on the Hyrcanian Forest Tourism Development Model for concentrated tourism and extensive tourism. Next, the most important effective indices included (i.e., 19 indices) were determined by Delphi questionnaire and SPSS 17. Finally, AHP technique was applied to analyze the body mass of the indices in order to verify the validity of the model. The results show that 0.0044, 01.3, 3.52, and 37.71% of the study area is suitable for concentrated ecotourism (grade 1), concentrated ecotourism (grade 2), extensive ecotourism (grade 1), and extensive ecotourism (grade 2), respectively. Based on the model applied, slope, direction, and fundamentals (infrastructure) with the body masses of 0.232, 0.116, and 0.115 were identified as the first priorities. Comparing the results of this model and AHP confirms the validity of the model. To strengthen the tourism development potential of the watershed and protect its ecosystems and biodiversity, it is necessary to choose a proper development model. Failure to identify the existing capacities and the field's sensitivities can cause dissatisfaction of local residents and also damage to the ecosystem of the area.

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