Simplex Method Research Articles

FACTORS ON THE BIOSYNTHESIS OF AMYLOLITIC ENZYMES OF STREPTOMYCETE ORIGIN

Modern enzyme biotechnology is a promising and rapidly developing field that requires the latest research on the conditions of enzyme biosynthesis. Optimizing the composition of the nutrient medium depending on the needs of microorganisms and physicochemical factors directly affect the increase in the efficiency of the biosynthesis of amylolytic enzymes, namely the biosynthetic capacity of the strain Streptomyces recifensis var. lyticus 2P-15. Modulation of the biosynthetic activity of strains producing amylolytic enzymes will allow to significantly increase their economic yield. Aim. The purpose of this work is to optimize the biosynthetic capacity of the strain Streptomyces recifensis var. lyticus 2P-15 in terms of the synthesis of amylolytic enzymes and the study of the dynamics of the influence of physical and chemical factors on optimization. Methods. The object of the study is the strain Streptomyces recifensis var. lyticus 2P-15, obtained by three-stage selection of the producer. The simplex method of selecting the composition of the environment was used for the research. The ratio of amylolytic activity to the level of biomass accumulation was taken as the biosynthetic capacity of the strain. A photocolometric method was used to determine amylolytic activity. The level of biomass accumulation was determined by the weight method. Results. It was established that as a result of optimizing the composition of the simplex nutrient medium by the method of mathematical modeling, the biosynthetic capacity increased by 3.63 compared to the control variant. It was also investigated that the optimal concentration of such a component of the nutrient medium as monosodium glutamate С5Н8NO4Na・H2O is 1.5%, which increases the amylolytic activity by 2.63 and increases the accumulation of biomass. Separately, it should be noted the obtained results of the study of the optimal concentrations of heavy metal ions added to the optimized version of the nutrient medium, which allow further research in this aspect to be continued and the use of Co, Mo, Cd ions in the composition of the nutrient medium. With the obtained results, there is an increase in amylolytic activity in the best response by 3.54. The obtained results have theoretical and practical significance for further research in the biotechnology of enzymes. Conclusions. The prospect of further research into the optimization of the biosynthesis of actinomycetes by the simplex method of other aspects of its regulation will be to increase the biosynthetic capacity of the studied strain, which will have a positive effect on the economic output of the production of amylolytic enzyme preparations by obtaining microbial synthesis.

Pengoptimalan Hasil Penjualan Kue Dengan Menggunakan Metode Simpleks

This research aims to maximize profits from selling cakes and find out the optimal number of each type of cake to be produced according to the limits available to Mrs. Farida Bako. The data collection technique was carried out by direct interviews with the respondent, namely Mrs. Farida Bako. The data analysis technique used is the simplex method in order to produce maximum profits by knowing the number of cake types produced. The research results show that maximizing and knowing the number of production results can maximize profits of IDR. 1,153,000

Systematic Determination of Optimal Design-Points of Fully Integrated Power-to-SNG Process Chains Via Detailed Simulation of SOEC Stacks

The target of carbon neutrality by 2050 set in the 2015 Paris agreement requires ambitious investments in renewable energy plants. Most potential lies in solar and wind energy, though their intermittent nature needs to be compensated by means of large-scale energy storage technologies. Power-to-SNG (Synthetic Natural Gas) enables the production of the versatile energy carrier CH4 through the use of electrical energy, allowing long-term storage of energy in chemical form. SNG produced using captured CO2 and green hydrogen is carbon-neutral and could therefore help to reduce the greenhouse gas emissions of the transportation and chemical industry [1].In a Power-to-SNG plant comprising a CO2 capture unit, an electrolyzer and methanation reactors, electrical power is the largest contributor to the levelized cost of the produced SNG [2]. This makes the optimization of the system efficiency a critical step towards achieving commercial viability. Since the water splitting reaction is the largest energy sink in the cascade of conversion steps, the use of Solid Oxide Electrolysis Cells (SOEC) is promising because their energy requirement can be partially covered by high temperature heat provided by the methanation reactors. When optimizing the performance of the plant, the operating conditions of the electrolyzer must therefore be considered in conjunction with other parameters contributing to the thermal balance of the plant, which calls for dedicated system modeling approaches.In this work, multiple Power-to-SNG process chains with integrated SOEC module are developed by means of detailed, yet highly customizable plant and component models. Optimal design points of each plant configuration are determined and comparatively assessed. The different process chains are generated by varying CO2 sources and methanation technologies, whilst processes are resolved down to the individual pumps, heat exchangers, cleaning and conditioning steps. Good comparability between configurations and operating conditions of the plant is guaranteed through ideal thermal integration using the pinch method [3]. The SOEC module is simulated based on high-accuracy, multivariate performance maps obtained from a multi-physics 3D stack simulation tool [4,5] computed on a high-performance cluster. For the simulations, two types of SOEC technologies are considered: Ni-GDC/3YSZ/LSCF-electrolyte supported cells and Ni-YSZ/8YSZ/LSC cathode-supported cells.The plant configurations are optimized independently with a simplex algorithm and thereby, the results of investigations focusing on standalone SOEC stacks or using low level-of-detail stack models in system simulations are ascertained. Results demonstrate the importance of balance-of-plant trade-offs in order to achieve best possible efficiencies. For example, in cases with direct air capture (DAC) of CO2, best results are obtained with endothermal operation of the stack and elevated air flow through the anode. Thus, using electrolyte supported cells, power-to-SNG efficiencies (based on HHV) of 67% (fixed-bed methanation) to 69% (fluidized-bed methanation) can be reached.

Advanced strategies on update mechanism of tree-seed algorithm for function optimization and engineering design problems

Tree-seed algorithm (TSA) is one of the meta-heuristic algorithms proposed recently. It has a simple but effective structure. However, for complex high-dimensional functions and constrained optimization problems, TSA has the defects of slow convergence rate and low optimization accuracy. To overcome these issues, an improved tree-seed algorithm (SATSA) based on Nelder-Mead simplex method is proposed. Firstly, appropriately increase the value of the control parameters that balance exploration and exploitation, thereby improving the local search ability of TSA, and introduce an adaptive stochastic component of Gaussian distribution in the seed generation formula of the local search stage. Then, set the maximum retention times of each tree, for trees that exceed the maximum retention times in continuous iterations, increase the number of seeds produced by this tree, then use a new equation based on the global optimal location to generate seeds, thereby speeding up the convergence of the algorithm. Finally, after all trees produce seeds, introduce the improvement strategy with Nelder-Mead simplex method into some poor quality solutions, so that the solution with poor quality is searched deeply in the neighborhood, speeding up the process of moving the overall solution to the optimal solution. Its convergence is proved by theoretical analysis, and certifies that the time complexity of SATSA is not increased compared to TSA. The simulation results of 6 advanced algorithms on CEC2017 test suite and 10 challenging engineering constrained optimization problems demonstrate that the three strategies proposed in this study are feasibility and effectiveness.

Performance analysis of PID controller-based metaheuristic optimisation algorithms for BLDC motor

ABSTRACT Today, the use of permanent magnet brushless DC (PMBLDC) motors in vehicles is increasing due to the characteristics of sensorless operation. PMBLDC motor controllers can control the speed and position in the closed-loop feedback system without the need for a position sensor mounted on the shaft. Proportional – Integral – Derivative (PID) controller is one of the most common feedback control algorithms used in PMBLDC motor controllers. Optimizing problems using deterministic methods such as Lagrange and simplex methods requires basic information and complex calculations. Meta-heuristic algorithms are a type of stochastic algorithm that is used to find the optimal solution. Meta-heuristic algorithms are divided into three general categories: evolutionary algorithms, swarm intelligence algorithms, and stochastic algorithms. In this paper, using 14 metaheuristic optimisation algorithms, PID control parameters including settling time, time rise, overshoot, and stability of step response of the mentioned system are optimised. In this paper, 14 meta-heuristic algorithms are simulated and evaluated to optimise the PID coefficients of the controller, including settling time, rising time, excessive increase, and step response stability. The simulation result shows that the genetic algorithm (GA) has the best performance in terms of cost function and biogeography-based optimisation (BBO) in terms of settling time and rising time parameters. Finally, the simulation results are confirmed using experimental results.

Quasi-maximum likelihood-based estimator of the hyperbolic frequency modulated signals

The quasi-maximum likelihood (QML) algorithm has been extended to estimate the phase parameters and the instantaneous frequency (IF) of the hyperbolic frequency modulated (HFM) signals. The Nelder-Mead simplex algorithm (NMSA) is adopted for a fine estimation. The obtained accuracy is excellent with the estimates achieving the Cramer-Rao lower bound (CRLB) above the signal-to-noise (SNR) threshold of 0 dB. The proposed technique is capable of functioning with signals that are sampled below the requirements of the sampling theorem, which is particularly important for HFM signals. Additionally, there are no limitations for signals with monotonic IF and/or group delay (GD) functions.

Approximating Linear Programming by Geometric Programming and Its Application to Urban Planning

In this study, we approximated linear programming by geometric programming; the developed method converts linear programming to geometric programming. We applied the developed method to Neighbourhood planning, a vital aspect of urban planning, and obtained the optimal cost of Neighbourhood designs. The method demonstrated that geometric programming is a robust non-linear optimization model that can be extended to approximate linear optimization problems. This method has obvious advantages in the sense that it allows every decision variable to contribute to the optimal objective function. This is not the case with the known regular Simplex method and the Interior Point Algorithm of solution to linear programming which assign zeros to some variables when the matrix of the non-basic variables is rectangular or when some of the non-basic variables did not enter the basis. The developed method was used to find the global optimal solution, optimal primal and dual decision variables. The solution was better compared to the linear programming method via Simplex method or Interior Point Algorithm because it achieved the global optimal solution. We observed that in addition to achieving the global optimal solution, we obtained the optimal dual decision variables which was absent in the other methods and all the primal decision variables have value against the other methods that assigned some of the variables with zeroes.

OPTIMIZATION METHODS IN CALCULATIONS OF MECHANISMS OF VEHICLES OF TRANSPORT

Purpose. The optimal design of vehicle mechanisms is a detailed review and evaluation of the scientific and technical literature and individual publications on the methodology for optimizing the inherent calculations of these types of mechanical structures. The main purpose of this review material is to consider a number of directions for designing mechanisms of means of transport, especially highlighting the use of optimization methods. Research in the field of designing mechanisms is carried out in two directions: by the synthesis method according to given provisions and by the method of mathematical programming. Methods. Methods for the synthesis of hinged-lever mechanisms can be divided into two groups. In the first one, methods of approximation of functions are used, in the second one, optimization methods. The search for the optimal solution in the second group means the simultaneous search for the correct formulation of the problem, in which the parameters of the mechanisms should be sought under conditions of the best satisfaction of the requirements of the technological process. The problems of optimal design of mechanisms most often continue to be formulated as problems of the best approximation of functions. Optimization synthesis is a relatively new direction in the synthesis of lever mechanisms. Optimization synthesis is carried out on the basis of nonlinear optimization methods. At present, the complexity and complexity of the problems that arise and require solution in the process of designing mechanisms have significantly increased. A certain amount of research has been carried out within the framework of the classical theory of finite and infinitely small plane displacements. In these works, the theory of spatial motion of a general form is developed in detail and applied to the problems of kinematic synthesis. Results. Since the simplex method is quite effective in solving problems of linear programming, it became the founder of a number of methods of sequential linearization or methods of secant planes for solving nonlinear programming. Practical significance. With an extensive list of modern literature and scientific publications on the synthesis of mechanisms and mathematical programming, a complete chronological review of the achievements of the last decade would be quite loud. Therefore, individual trends in the synthesis of mechanisms were only noted, focusing on the desire to obtain a “better” design, taking into account practical requirements. The synthesis of mechanisms is an area in which the application of mathematical programming methods is quite promising due to their free formulation. The constraints and requirements of engineering practical problems can often be taken into account in forms consistent with the data of modern methods. The main purpose of this study is to make widely known the achievements in the two fields with the hope that currently existing optimization tools will be applied to these synthesis problems and that, conversely, the optimization tools will be further improved as a result of research on the design of vehicle mechanisms. This will contribute to a better understanding and accelerated development of both mechanisms calculation methods and optimization methodology.

Modeling the Spread of COVID-19 Using Nonautonomous Dynamical System with Simplex Algorithm-Based Optimization for Time-Varying Parameters

The S I R D V (Susceptible, Infected, Recovered, Death, Vaccinated) compartmental model along with time-varying parameters is used to model the spread of COVID-19 in the United States. Time-varying parameters account for changes in transmission rates, people’s behaviors, safety precautions, government regulations, the rate of vaccinations, and also the probabilities of recovery and death. By using a parameter estimation based on the simplex algorithm, the system of differential equations is able to match real COVID-19 data for infections, deaths, and vaccinations in the United States of America with relatively high precision. Autoregression is used to forecast parameters in order to forecast solutions. Van den Driessche’s next-generation approach for basic reproduction number agrees well across the entire time period. Analyses on sensitivity and elasticity are performed on the reproduction number with respect to transmission, exit, and natural death rates in order to observe the changes from a small change in parameter values. Model validation through the Akaike Information Criterion ensures that the model is suitable and optimal for modeling the spread of COVID-19.

Feature selection based on a multi-strategy African vulture optimization algorithm and its application in essay scoring

Reducing the dimensions of the original data set while preserving the information as much as possible is conducive to improving the accuracy and efficiency of the model. To achieve this, this paper presents a multi-strategy African vulture optimization algorithm that is the chaotic and elite opposition-based African vulture optimization with the simplex method and differential evolution strategy(CESDAVO). Three main improvements are introduced into African vultures optimization(AVO) to improve its capabilities in this study. Firstly, the chaotic elite opposition-based learning strategy is used to initialize and diversify individual positions of vultures. Secondly, the simplex method is used to optimize those poor individuals so as to further improve the local exploitation ability of the algorithm. Thirdly, the differential evolution strategy is used to make the algorithm escape from the local optimum and improve the global optimization capability of the algorithm. The results of the ablation experiments show that mixing the three strategies greatly improves the optimization performance of the algorithm. In addition, Nine algorithms are compared with CESDAVO on 15 benchmark functions, and this experimental result shows that its optimization capability is superior to the others. Then, the proposed CESDAVO is employed for feature selection, and 12 standard datasets are used for experiments. According to the experimental results, CESDAVO obtained the highest average classification accuracy on 11 datasets and the highest feature selection rate on 8 datasets, which is significantly better than other algorithms. Finally, CESDAVO is also applied to feature reduction for essays, removing 24 features and significantly improving the classification accuracy on multiple classifiers.

A NON-LINEAR OPTIMISATION APPROACH TO IN-PIT HAUL ROAD DESIGN

Truck haul costs, as one of the predominant operational costs for mining and quarrying operations, are known to be heavily dependent on the design parameters of haul roads. Furthermore, in-pit haul road design parameters determine the pit limits and therefore, the potential feasibility of the mining operation. Thus, when in search of an optimal solution in terms of in-pit haul roads, one should primarily consider the location of the in-pit haul road, its design features and the deriving operational costs regarding extraction and haul costs. A suitable objective function in this case may be the undiscounted profit for the ultimate pit design. However, for each considered scenario, truck and excavator operational costs can be calculated using simulation techniques for better accuracy. Furthermore, finding an optimal solution requires the execution of a reliable and efficient algorithm, depending on the shape of the objective function. Hence, a non-linear optimisation approach was proposed in this paper for solving the in-pit haul road optimisation problem, based on a simulation of the materials allocation, which was used for calculating the objective function. Design parameters were assumed to be predetermined, while the only variable used for finding an optimal solution was the location of the in-pit haul road inside the pit contour. In addition, two 1-D algorithms were compared for finding the optimal solution (Search with accelerated step size and 1-D Simplex method). Furthermore, two regression models are proposed (Multiple Linear Regression – MLR and Non-Linear Regression - NLR), which could identify the more feasible region for the in-pit haul road location and reduce the number of iterations required for convergence.

Mixed Network of Hydrogen Bonds in Aqueous Solutions of 3-Amino-1-propanol: Results of Molecular Dynamic Modeling

The H2O–3-amino-1-propanol (3AP) system at 300 K was studied by molecular dynamics, graph theory, and Delaunay simplex methods. All the molecules were shown to be bound into a three-dimensional network of hydrogen bonds over the entire range of concentrations in the system. The characteristics of the networks and their concentration dependences were determined. The frequency at which the environment of molecules changes is discussed. The results were compared with those for mixed networks in aqueous solutions of 1,3-propanediol and monoethanolamine.

Waste corn husk fibers for sound absorption and thermal insulation applications: A step towards sustainable buildings

In the last decade, noise pollution and global warming and their effects on human health and the environment have received much attention. Building sectors are one of the most important areas for potential improvements. To this end, sound-absorbing and thermal insulation construction materials are being used effectively. Recently, a great deal of interest has arisen in using various natural fibers as sound-absorbing and/or thermal insulation materials. In line with these studies, this work investigates the acoustic absorption and thermal insulation characteristics of corn husk fiber (CHF). The results showed that the samples enjoy excellent noise reduction coefficients of 0.36–60 and effective thermal conductivities of 0.038–0.042 W/mK. It was found that the thermal insulation properties of CHFs are not significantly influenced by the moisture content. The Dunn and Davern (DD) model and a modified model of DD based on the Nelder-Mead simplex algorithm were also used to predict the acoustic behavior of the samples. It was found that the proposed model provides very excellent prediction accuracy.

Maximizing Barry Furniture UMKM Profits with the Linear Simplex Program Method

In today's era, furniture is a type of business that has big profits, however, it requires a large amount of capital to start the business. Many furniture entrepreneurs are competing to be the best by improving the quality of their products in order to obtain results that are worthy of competing with other products. In addition to quality, entrepreneurs also need to improve performance in order to achieve effectiveness and efficiency in running their business. This performance improvement aims to obtain the maximum possible profit. Barry Furniture UMKM also wants to experience this. To obtain maximum profit, proper analysis is needed so that this analysis can help entrepreneurs to achieve effectiveness and efficiency in running a business. In conducting the analysis, the Linear Programming analysis tool with the Simplex Method was used. From the results of this study it can be concluded that in order to obtain maximum profit, Barry Furniture needs to produce 60 tables and 60 chairs. If Barry Furniture follows the results of this analysis, it is estimated that in less than one month they will earn a profit of IDR 1,800,000.00.

Modeling and IAHA Solution for Task Scheduling Problem of Processing Crowdsourcing in the Context of Social Manufacturing

The paper addresses the discrete characteristics of the processing crowdsourcing task scheduling problem in the context of social manufacturing, divides it into two subproblems of social manufacturing unit selecting and subtask sorting, establishes its mixed-integer programming with the objective of minimizing the maximum completion time, and proposes an improved artificial hummingbird algorithm (IAHA) for solving it. The IAHA uses initialization rules of global selection, local selection, and random selection to improve the quality of the initial population, the Levy flight to improve guided foraging and territorial foraging, the simplex search strategy to improve migration foraging to enhance the merit-seeking ability, and the greedy decoding method to improve the quality of the solution and reduce solution time. For the IAHA, orthogonal tests are designed to obtain the optimal combination of parameters, and comparative tests are made with variants of the AHA and other algorithms on the benchmark case and a simulated crowdsourcing case. The experimental results show that the IAHA can obtain superior solutions in many cases with economy and effectiveness.

An Evolutionary Analysis of Higher-Order Interaction Collaborative Innovation Networks in China’s New Energy Vehicle Industry

As a manifestation of technological innovation achievements, patents reflect the frontier of technological development in the field. The aim of this research is to investigate the spatial evolution of patent collaboration networks and cooperation activities in the Chinese new energy vehicle (NEV) industry. We hypothesize that the higher-order collaboration networks will exhibit the formation of triangle groups centered around core nodes and the emergence of key edges indicating their importance. Key organizations and partners will play a significant role in shaping the future direction of cooperative innovation. The research utilizes data on collaboration patents in the NEV industry in China and constructs higher-order interaction collaboration innovation networks. The spatial evolution of networks and patent cooperation activities are examined using simplex methods. The results indicate that the generalized degree distribution of nodes and edges follows a power-law distribution. Two-dimensional simplex networks gradually form triangle clusters centered on core nodes and key edges. Large companies and research institutes prefer high-depth collaboration, while universities prefer high-breadth collaboration. Furthermore, the development of the new energy vehicle industry has gradually shifted from the eastern region to the central region. In the two-dimensional simplex network, Beijing and Jiangsu play a crucial role as key bridges in fostering stable and deep collaborations. The findings of this study shed light on the spatial evolution of patent collaboration networks and cooperation activities in the Chinese NEV industry. The identification of key organizations and partners, as well as the central positions of certain regions, provides valuable insights for innovation organizations in navigating innovation development and selecting collaboration partners.

Optimal Volume Planning and Scheduling of Paper Production with Smooth Transitions by Product Grades

The article deals with the problem of calculating the volume calendar plan of a paper mill. The presented mathematical model and methods make it possible to schedule paper production orders between several paper machines (PM) to even their loading, devise cutting plans for each winder and arrange the order of their implementation. When forming cutting plans, orders are grouped in accordance with such parameters as grammage, roll diameter, core diameter, product type and number of layers. Deadlines and volumes in customer orders are taken into account. The cutting plans for each winder account for the allowable roll width limits and the maximum number of knives. To find the optimal schedule, a combination of the following criteria is used: minimal trim loss, minimal changes to the knives’ setup and smooth transitions by product grades. Solution algorithms are presented that use a combination of the simplex method, the column generation, the branch and bound methods, the greedy algorithm and the local search procedure. We tested the solution approach on real production data from a paper mill in European Russia and obtained the production sequence that better matches deadlines in customer orders compared to the plan devised manually by production planners.

A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment.

Coal is an important resource that is closely related to people's lives and plays an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. Therefore, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and a simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model is established using the improved algorithm to classify and predict the safety of the coal mine environment as one of four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved and that the performance of the IAHA-SVM model is significantly improved.

Optimization of Parameters Related to Grain Growth of Spring Wheat in Dryland Based on the Next-Generation APSIM

To improve the applicability of crop models, this study compared two algorithms for optimizing the single objective parameters of the spring wheat in the dryland grain growth sub-model to identify the more efficient algorithm for application in future model parameter optimization. Based on field experiments from 2015 to 2021 in Gansu Province, this study combined weather data and yearbook yield data from 1984 to 2021 to optimize parameters related to grain growth of spring wheat in dryland based on the next-generation APSIM using two algorithms: the Nelder–Mead simplex algorithm and the DREAM-zs algorithm. The results were as follows: the optimization results of both algorithms were the same, but the DREAM-zs algorithm converged faster; the optimized parameters for the grain growth stage of Dingxi35 spring wheat were: a grain number per gram stem of 25 grains, an initial grain proportion of 0.05, and a maximum grain size of 0.049 g; after optimization, the root mean square error (RMSE) of observed and simulated yield values decreased from 186.84 kg/hm2 to 115.71 kg/hm2, and the normalized root mean square error (NRMSE) decreased from 10.33% to 6.40%. The optimized results were consistent with the growth and development process of wheat and had high applicability.

Combining Markowitz Portfolio Model and Simplex Algorithm to Achieve Sustainable Land Management Objectives: Case Study of Rivadavia Banda Norte, Salta (Argentina)

Land use planning involves making an appropriate decision and selecting a use over other alternatives. A step-by-step methodology was developed to evaluate the optimal combination of regional land use technologies and the spatial allocation. For a realistic approach, a case study (specifically Rivadavia department, Salta, Argentina) is considered, which has deforestation problems and the advance of intensive and extractive agriculture. Five management techniques are considered for the area: precision agriculture (T1), advance livestock farming (T2), payment for ecosystem service (T3), traditional agriculture–livestock farming—Criollo (T4), and traditional forest management—Wichi (T5). A land evaluation on a GIS model is carried out to obtain the land suitability for each technique. Analyzing local experts’ opinions using the Markowitz portfolio methodology allows us to obtain an optimal combination of techniques. Finally, a Simplex method analysis linked with the GIS is performed to allocate the five techniques over the territory maximizing land suitability and in compliance with percent surface assignments. The result assigns each GIS polygon to a specific technique, reaching optimal land suitability in 92% of the territory. Natural capital and social attributes had a significant and complex impact on technology choice, but objective and optimized approaches in their allocation were possible and provides valuable information to guide public policies.