Sensitivity Analysis Of Parameters Research Articles

Advancing hydrogen storage predictions in metal-organic frameworks: A comparative study of LightGBM and random forest models with data enhancement

The escalating consumption of fossil fuels has given rise to a substantial upsurge in greenhouse gas concentrations and global temperatures, which, in turn, has triggered severe climate-related consequences. The critical imperative to reduce CO2 emissions and combat global warming has spurred extensive investigations into clean energy alternatives, with hydrogen emerging as a compelling zero-emission energy source. As a pivotal component of clean energy strategies, hydrogen requires designing compact, lightweight, and efficient storage systems. This study focuses on the development and evaluation of machine learning models for predicting the efficiency of Metal-Organic Frameworks (MOFs) in hydrogen storage, a key aspect of advancing clean energy technologies. MOFs, a class of nanoporous materials, show remarkable potential for hydrogen storage due to their high surface area and porosity. However, selecting the most suitable MOF for this application from a vast array of possible structures is a daunting task. In this context, machine learning algorithms offer an efficient alternative for predicting MOF suitability by considering their structural and chemical properties. We used ensemble learning methods, specifically Light Gradient Boosting Machine (LightGBM) and Random Forest (RF), to predict hydrogen uptake of MOFs based on a dataset of 219 experimentally tested samples. Two modeling scenarios were considered: one using the entire dataset, and the other involving strategic data pre-processing, including outlier removal and feature engineering. The results demonstrate that the measures taken to refine the dataset significantly enhance the predictive performance of the developed models, reducing prediction errors and improving overall goodness of fit. Specifically, the Mean Absolute Error (MAE) values for both the LightGBM and random forest models were reduced from 0.48 and 0.94, respectively, to 0.16 for both models, and the coefficients of determination (R2) increased substantially from 0.84 and 0.72 to 0.95, in both cases. Moreover, feature importance analysis unveiled that pressure-related features make the most significant contributions to the formation of tree ensembles during the model training process. A parametric sensitivity analysis was conducted revealing that H2 uptake in MOFs is most sensitive to changes in adsorption enthalpy, followed by surface area and temperature, while showing lower sensitivity to variations in pressure, consistent with established literature. These results underscore the pivotal role of data enhancement methods in refining machine learning models and can be instrumental in accelerating the development and optimization of MOF materials for clean energy applications.

Population viability analysis on the Iranian endangered goat breeds

Because of the downward trend of breeding goats and flocks, habitat loss, using other breeds in reproduction, and the presence of survival-threatening factors, Iranian goat breeds, including Markhoz, Adani, and Najdi, are considered endangered populations. This research aims to investigate the current status and provide the necessary information to conserve these three endangered populations. For this purpose, the census data and the required parameters were collected from the available resources and flocks during the years of the project implementation. Then, two scenarios, the past and future dynamics of the population assuming continuing existing conditions, were simulated using the population viability analysis (PVA) method. Finally, the sensitivity analyses of parameters affecting the viability criteria were done. PVA was conducted using the Vortex simulation program version 10.5.6. The past dynamics simulation of the population showed that the biological parameters and assumptions used in the model have valid and reliable values and frequencies, and they can be used in the simulation of different management scenarios. Based on this scenario, the inbreeding rates (∆F) for the Markhoz and Adani populations have reached above the critical level of 0.5%. The future dynamics simulation of the population showed that the process of increasing inbreeding and decreasing genetic diversity would accelerate, and the breeds' extinction probability would start in 2026, 2033, and 2031 for Markhoz, Adani, and Najdi, respectively. The mean time to the first extinction was also 15.4 ± 2.53, 26.68 ± 3.75, and 13.96 ±1.78 years, respectively. The sensitivity analyses indicated that adult does overharvest, the frequency of feed limitation, and competition with other breeds had the highest effect on the population viability criteria of the investigated breeds. Markhoz, Najdi, and Adani goats need urgent conservation measures. The results obtained in this research may prepare a suitable framework for providing conservation programs.

Theoretical model and parameter sensitivity analysis of the filter cake formation during slurry shield tunnelling

The filter cake formation during slurry shield tunneling in high permeability soil layers is complex and difficult to monitor. There is no reliable method to evaluate the mechanical characteristics during the filter cake formation. A theoretical model is proposed to describe the filter cake formation and development in this study. The finite difference method is used to simulate the whole filter cake formation process. In addition, the influence of the key bentonite slurry parameters and of the deep infiltration on the filter cake formation is investigated based on the proposed model. The results show that the excess pore water pressure within the filter cake is not uniformly distributed. The slurry support effect is manifested by a pressure drop generated on both sides of the filter cake. The development process of the filter cake can be summarized into three stages: prior infiltration filling of the filter cake, accumulation and thickening of the new filter cake, and compression and consolidation of the prior filter cake. The slurry deep infiltration results reflected by the membrane specific resistance have the most significant effect on the filter cake development. Compared with the slurry concentration, the viscosity has a greater influence on the formation and final thickness of the filter cake.

Lap-slip model of rebar-to-concrete in RC/ECC/UHPC based on GA-BP neural network

Rebar lap splicing is a widely used method of reinforcement connection in precast concrete structures, and the lap-slip model serves as an important prerequisite for conducting detailed mechanical calculations of reinforced concrete lap splice members. Most traditional bond-slip mechanical models are semi-empirical and semi-theoretical formulas derived from experimental data; they do not consider the effect of reinforcement lap spacing, which prevents them from accurately reflecting the force transfer characteristics of lap reinforcement in concrete. In this paper, a Back Propagation (BP) neural network based on a Genetic Algorithm (GA) is used to establish a lap-slip model of reinforced concrete considering rebar spacing, which improves on the current bond-slip mechanical model. Several sets of rebar lap tests based on RC/ECC/UHPC were designed, and full-sample parameter sensitivity analyses were carried out using the lap length, the thickness of the concrete cover, the rebar spacing and the stirrup ratio as test parameters. The results showed that rebar lap spacing was an important influencing factor in the proposed lap-slip model, with a sensitivity coefficient reaching 0.471. The lap-slip model, based on the GA-BP neural network, was developed and compared with existing models; the predictive ability of the model was verified by the ten-fold crossover method. The generalizability of the GA-BP model was verified by randomly selecting non-training test data as well as lap test data of rebar in different types of concrete. The results indicated that the model exhibits high predictive and generalization abilities, with most correlation coefficients exceeding 0.95. The proposed model was compared with a mechanical model established using the traditional fitting formula method, and the results showed that the prediction accuracy of the lap-slip model established based on the GA-BP neural network is slightly lower than that of the mechanical model obtained using the fitting formula method, but it still offers high prediction accuracy, and the prediction results are more objective and realistic, which is of guiding significance for practical applications and related research.

Numerical simulations of dam-break flows of viscoplastic fluids via shallow water equations

Abstract This paper presents simulations of dam-break flows of Herschel–Bulkley viscoplastic fluids over complex topographies using the shallow water equations (SWE). In particular, this study aims to assess the effects of rheological parameters: power-law index (n), consistency index (K), and yield stress ($$\tau _{c}$$ τ c ), on flow height and velocity over different topographies. Three practical examples of dam-break flow cases are considered: a dam-break on an inclined flat surface, a dam-break over a non-flat topography, and a dam-break over a wet bed (downstream containing an initial fluid level). The effects of bed slope and depth ratios (the ratio between upstream and downstream fluid levels) on flow behaviour are also analyzed. The numerical results are compared with experimental data from the literature and are found to be in good agreement. Results show that for both dry and wet bed conditions, the fluid front position, peak height, and mean velocity decrease when any of the three rheological parameters are increased. However, based on a parametric sensitivity analysis, the power-law index appears to be the dominant factor in dictating fluid behaviour. Moreover, by increasing the bed slope and/or depth ratio, the wave-frontal position moves further downstream. Furthermore, the presence of an obstacle is observed to cause the formation of an upsurge that moves in the upstream direction, which increases by increasing any of the three rheological parameters. This study is useful for an in-depth understanding of the effects of rheology on catastrophic gravity-driven flows of non-Newtonian fluids (like lava or mud flows) for risk assessment and mitigation. Graphical abstract

Advancements in design of radiant floor heating systems with an innovative simplified calculation method

The calculation accuracy of existing simplified methods for radiant floor heating systems (RFHS) is far from meeting the growing demand for energy saving. Therefore, an innovative steady-state simplified method is proposed based on conduction shape factors, equivalent thickness, and mirror principle. The new model is validated by experimental data and compared with an equally simple simplified method. Meanwhile, the effects of the total heat-transfer coefficient of radiant surfaces and fluid flow rate in heating pipes on the calculated results are discussed. The calculation flowchart is given. Parameter influence and sensitivity analysis are then performed. The results show that the new simplified model agrees well with experimental data, and the maximum relative error is 7.9% for heat flux and 2.0% for surface temperature. Standard deviation reduces by 68.5% and 66.8% for the heat flux and surface temperature, respectively, compared to the equally simple existing simplified method. With the same absolute increment of pipe pitch, the smaller the pipe spacing, the faster is the heat flux and surface temperature change. The order of importance of influential parameters is mean water temperature > indoor air temperature > pipe pitch > thermal conductivity of filling layer > thermal conductivity of pipe wall.

Comparison of two hydrodynamic models for their rain-on-grid technique to simulate flash floods in steep catchment

Comparison of two hydrodynamic models for their rain-on-grid technique to simulate flash floods in steep catchment

Dynamical behavior of fractal-fractional order monkeypox virus model

A virus that infects both humans and animals, monkeypox is common in many West African countries and has sporadically spread to other parts of the world. There are serious public health concerns around the globe as a result of the recent spike in cases of monkeypox among both endemic and non-endemic populations. This paper proposes to use the Caputo sense of fractal fractional-order derivatives to examine the dynamics of monkeypox transmission. The study uses Schauder’s fixed point theorem to evaluate the solutions qualitatively and establish their uniqueness inside the model. The next generation matrix technique is used to compute the essential reproduction number. The stability of equilibrium points is further investigated in the research, and a sensitivity analysis of model parameters is carried out. When the basic reproduction number R0 is less than 1, the equilibrium without infections is locally stable. Also, when R0 exceeds 1, this equilibrium becomes unstable. The proposed model incorporates Ulam–Hyers stability through nonlinear functional analysis. To estimate solutions for the fractal-fractional order monkeypox model, the Lagrange’s interpolation method is utilized. In addition, numerical simulations are carried out to examine the influence of some parameters on the overall dynamics of the model. Numerical simulations are performed using MATLAB software to exemplify the model behavior in the context of the Nigerian case study. The graphical representations suggest that the fractal fractional order affects the dynamics of the monkeypox. The findings indicate that isolation of infected individuals in the human population helps to reduce disease transmission.

Кількісне оцінювання функціональної ефективності мережевих структур за аналізом чутливості оптимізаційної моделі до зовнішніх впливів

The real network organizational structure is constantly under the influence of external influences, which can cause unexpected consequences due to the cascading spread of relevant disturbances directed at critically important parameters. Parametric sensitivity analysis is the latest computational procedure, the results of which provide the researcher with an idea and quantitative assessment of the appropriate response of the structure in the form of an analytical platform as a composition and interaction of scenario analysis, cascade process development and optimization modeling technology. The main task is timely adaptation to expected changes in business analytics, oriented to optimal resources distribution. The generalized minimum cost problem is a typical example of widespread problems of optimal distribution and use of limited, in particular, energy resources, of any nature and purpose, using the scientific management and analysis of decisions, in its network model attention is paid to the potentials of nodes. The maximum flow problem is an example of the active and effective application of methods and models of flows optimization, where the processes and objects used a network organization, the energy systems and complexes investigated, where arc parameters are critical in its model. In the article, for these classic optimization problems, mathematical and spreadsheet models were built on concrete examples. Direct and dual mathematical programming problems were solved, the following stages were carried out - the organization of a computer experiment and the construction of an analytical platform for evaluating the behavior of the network structure under the influence of external influences and disturbances as their consequences. To take into account the specific conditions of real objects with a network structure at the stage of modification, the model supplemented with appropriate restrictions and correction of input data sets. The obtained results should be useful for planning and management personnel and top-management for discussion and decision-making regarding, for example, the optimal placement of energy equipment, the weighted distribution of energy flows in the "source-sink" system or the appointment of executors for work, automated project management using project networks, etc. Keywords: optimization modeling, maximum network flow, minimum cost flows, network organizational structures, decision making, spreadsheet modeling and analytics.

An improved dynamic analytical model and the parameter sensitivity analysis of spherical stacking latent thermal energy storage devices

Thermal energy storage technology plays an important role on improving energy usage flexibility for the end users. The spherical stacking latent thermal energy storage devices inherits the advantages of direct thermal energy usage efficiency and simplified practical applications that received widely attentions. However, due to dispersed and multi-layer structural arrangement, the non-ignorable dynamic heat transfer process poses difficulties in the device design and operation control. In this study, an improved analytical model for predicting the transient heat transfer process of spherical stacking devices is developed. After verification, the parameter sensitivity analysis of the device is conducted by using the model. The results show that the dynamic change trend of thermal resistance is distinct for the melting and the solidification process. The developed model can dynamically predict the outlet temperature of heat transfer fluid and the heat transfer efficiency in a good agreement, and the prediction error of outlet temperature during solidification can reach −2.49 %–4.70 %. The correlation between sphere radius and heat transfer rate is negative and reaches a kind of high correlation above 0.8 for solidification process. The inlet temperature and the mass flow rate of heat transfer fluid are the most influential parameters for melting process.

Integrated decision of production scheduling and condition-based maintenance planning for multi-unit systems with variable replacement thresholds

In practice, production scheduling and maintenance planning are two popular technologies that are interdependent, but they are most often performed independently. In this study, we have developed an integrated model for coordinating single-machine scheduling and condition-based maintenance planning. Firstly, we build upon existing scheduling research that often views the system as a whole and further consider the degradation and maintenance of multiple units within the system during the scheduling process. Secondly, in contrast to traditional degradation modeling which usually assumes a single threshold to trigger maintenance/replacement, we also consider the variability of replacement thresholds caused by the heterogeneity of scheduling tasks. Based on this foundation, we propose an integrated policy that considers both scheduling and condition-based maintenance. Furthermore, we have developed an integrated decision model for multi-unit systems. The model aims to minimize the total weighted expected completion time over the entire scheduling time horizon. Moreover, the genetic algorithm was employed to optimize the integrated model. Finally, we present an example of an SA adjustable multi-axis machine and conduct a sensitivity analysis of key parameters to validate the integration policy.

Evaluating spray drift from Uncrewed Aerial Spray Systems: A machine learning and variance-based sensitivity analysis of environmental and spray system parameters

Uncrewed Aerial Spray Systems (UASS), commonly called drones, have become an important application technique for plant protection products in Asia and worldwide. As such, environmental variables and spray system parameters influencing spray drift deserve detailed investigations. This study presents the data analysis of 114 UASS drift trials conducted between December 2021 and December 2022 in China. Study design was based on the ISO 22866:2005 protocol for spray drift trials and considered different UASS platforms, nozzles, and release heights, and specifically continuously measured weather conditions. The relative importance of the environmental variables and spray system parameters was evaluated by a random forest (RF) feature importance analysis, a Sobol sensitivity analysis and partial dependence plots. This approach was preferred to linear ranking techniques such as ANOVA (analysis of variance) due to the non-linearity of the system. In addition, partial dependence plots are proposed to visualize the relationship between specific input parameters within the system.Drift deposition curves calculated from the 114 trials show good agreement with previous UASS trials reported in the literature. As reported in previous studies, spray drift following UASS applications is lower than for manned aerial vehicles, greater than for ground spray applications, and similar to drift observed from orchard air blast applications. In addition, 9 trials were conducted on corn fields in order to evaluate the potential effect of crop cover on spray drift. Spray drift was observed to be reduced over the cropped soil, suggesting that plant cover might possibly reduce spray drift. These findings could help supporting drift mitigation policies, stewardship advice and product labelling around the world.

Neural Network Approach for Cholera Dynamics: Integrating Deterministic and Stochastic Insights

In this article, a comprehensive qualitative analysis framework is presented that integrates stochastic and deterministic models to explore the transmission dynamics of Cholera disease. The significance of the reproduction number in understanding the spread of the disease and in formulating effective control strategies is emphasized. The sensitivity analysis of model parameters is conducted to identify those that critically influence transmission. By converting the deterministic model into a stochastic form, Brownian motion and stochastic parameters are incorporated to capture the inherent randomness in disease dynamics. This approach is further enriched by the application of neural networks (NN) to simulate and validate the complex interactions involved in Cholera transmission. The analysis extends to the global positive solution, stochastic reproduction number, and virus-free stochastic dynamics, providing an improved understanding of the disease's behavior. The critical conditions necessary for the extinction and long-term elimination of the virus are also addressed, highlighting the importance of controlling its spread. The stationary distribution of the disease is examined through the lens of a regular Markov process, enhancing the understanding of the stochastic model's dynamics. The results verified by neural networks, compared with those obtained from conventional numerical methods, underscore the accuracy and versatility of neural networks in epidemiological research, offering new perspectives and methodologies for tackling this global health challenge.

Decarbonizing Airport Using Solar and Wind Farm: A Case of Biratnagar, Nepal

Airports strive for a greener energy transition to minimize their carbon footprint and greenhouse gas emissions. The installation of solar or wind turbines is gaining significance among stakeholders working in sustainable airports. This study focuses on developing Biratnagar airport as a completely greener airport by identifying suitable sites for installing Solar and wind farms within the premises without compromising on safety aspects, including glare occurrence and environmental and electrical safety. PVSYST V6.8.7 and HOMER V2.81 software are used to simulate the performance of PV and wind turbine power plants. Three sizes of Vertical axis wind turbines (VAWT) of EOLO 15 kW, Tree tree-shaped wind Turbine (TSWT) 58.5 kW and TSWT 585 kW have been used for the Simulation. A sensitivity analysis of various parameters, including interest rates, wind speed, and wind turbine types, has been carried out to study the cost of energy generation in grid-connected mode. According to the study, a 157 kWp solar PV power plant is sufficient for Biratnagar Airport to become Nepal's first fully green-powered airport. The project's Levelized Cost of Electricity (LCOE) equals 0.141 $/kWh if wind energy is used to supply part of the electricity with the annual wind electricity production of 23,184 kWh. The combined 15 kW wind turbine-grid scenario is superior to the grid scenario for wind speeds of more than nine m/s and interest rates of less than 10%. These findings increase airport authorities' and stakeholders' confidence in the investment in greener airports.

Effect of geothermal heat transfer on performance of the adiabatic compressed air energy storage systems with the salt cavern gas storage

The temperature and pressure of compressed air influence the output performance of the adiabatic compressed air energy storage system with salt cavern gas storage. However, current studies often overlook the impact of geothermal heat transfer on system performance. In this paper, a mathematical model of the energy storage system with salt cavern gas storage is developed, considering the geothermal heat transfer in wellbore and salt carven. The model is used to illustrate the effects of geothermal heat transfer on the system performance through a sensitivity analysis of some key parameters. The results show that geothermal heat transfer will significantly reduce the gas and energy storage capacity, with a maximum reduction of 15.3 % and 11.1 % at a depth of 2000 m, respectively. Furthermore, this reduction would increase with the depth of the salt cavern. The geothermal energy reduces the round-trip efficiency from 68.7 % to 66.26 % and the energy storage density from 5.18 kWh·m−3 to 4.32 kWh·m−3. For common salt cavern gas storage at a depth of about 600 m ∼ 1200 m, the negative impacts of geothermal heat transfer on round-trip efficiency and energy storage density are less than 2.2 % and 0.5 kWh·m−3, respectively. Sensitivity analysis also demonstrates that the primary factors influencing the system’s performance are the thermal conductivity of the surrounding rock, the volume of the salt cavern, and the length of the wellbores. The secondary factors include the heat transfer coefficient on the inner wall of the salt cavern, the inner pipe radius, and the roughness of the inner pipe. Irrelevant factors include the thermal conductivity of the protective fluid and the thermal conductivity of the cement layer. The proposed comprehensive mathematical model of the system and simulation findings aid in forecasting the performance of storage systems and offer theoretical guidance for the design of large-scale compressed air energy storage systems.

Nonlinear dynamics and optimal control of a fractional order cotton leaf curl virus model incorporating climate change influences

This work investigates the transmission dynamics of a proposed fractional-order cotton leaf curl virus (CLCuV) model considering the effects of climate change. High temperatures resulting from climate change and global warming significantly impact the cotton crop and CLCuV transmission, causing substantial economic losses for farmers and agricultural industries. The study aims to address these impacts and contribute to the Sustainable Development Goals (SDGs) by promoting well-being and healthy lives. The characteristics of the CLCuV model’s solution are examined, including equilibrium points and the basic reproduction number. The stability of equilibrium points is analyzed in relation to key parameters, and sensitivity analysis of controllable parameters is performed. Additionally, optimal management strategies for controlling CLCuV spread are explored, aiming for minimal cost, improved health, and reduced climate change hazards. Numerical simulations are conducted to validate the theoretical findings.

Sensitivity analysis of parameters affecting the seismic performance of RC columns strengthened by fabric-reinforced cementitious mortar

In recent years, fabric-reinforced cementitious mortar (FRCM) has emerged as a popular choice for strengthening reinforced concrete and masonry structures due to several advantages over conventional fiber-reinforced polymer (FRP) composites. Particularly, the enhancement of Reinforced Concrete (RC) columns using FRCM composites has garnered significant attention. While experimental investigations are crucial for assessing the effectiveness of FRCM, physical experiments are often resource-intensive and time-consuming. Therefore, this study seeks to investigate the impact of various design parameters on the performance of RC columns strengthened with FRCM under low-amplitude cyclic loads simulating earthquakes through Finite Element (FE) analysis. The FE model, incorporating columns and FRCM strengthening materials, was developed using the DIANA 10.5 program. To assess the reliability of the model, analytical results were compared with experimental findings from a previous study, focusing on lateral strength, hysteresis behavior, and failure modes. The validation outcomes demonstrated a reasonable correlation between the test and numerical results. Subsequently, a sensitivity analysis was conducted to explore the influence of input parameters, such as concrete compressive strength, fabric reinforcement quantity, longitudinal reinforcement ratio of the columns, and pre-axial loading levels, on the seismic performance of RC columns reinforced with FRCM. The findings of the sensitivity analysis were discussed in detail.

Uncertainty and sensitivity analysis of a travel‐time distribution‐based stream water electrical conductivity model

AbstractIn this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2‐year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute‐balance approach and (2) a water age‐based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age‐based model than the solute‐balance model; 4. using time‐variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS‐TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute‐balance model, the rainfall concentration Crain and in the age‐based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute‐balance model. Little change in parameter sensitivity over time was found for the age‐based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute‐balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions.

Application of SWAT Model for Hydrological Simulation of Rapti River Basin

This study aimed at application of SWAT model for hydrological simulations of Rapti River Basin (RRB) water systems. The Rapti River originates from Nepal and then it comes in India. SWAT (Soil and Water Assessment Tool) model was used for hydrological simulation of the RRB surface and sub surface water systems. SWAT is a comprehensive, semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration. The RRB was discretised into 4 sub-basins and 630 hydrological response units (HRUs) and calibration and validation was carried out at Bagasoti using monthly flow data of 11 years, respectively. We first calibrated the model in SWAT-CUP which is a decision-making framework that incorporates a semi-automated approach (SUFI2) using manual calibration and incorporating sensitivity and uncertainty analysis. Parameter sensitivity analysis helps focus the calibration and uncertainty analysis and is used to provide statistics for goodness-of-fit. In this study Calibration has been done between simulated and observed discharge data (1974-1985) for 50 simulations with 6 parameters that is Curve number (CN2 = 0.945), Groundwater delay (GW_DELAY = 50), Baseflow alpha factor (ALPHA_BF = 0.58), Manning's "n" value for the main channel (CH_N2 = 0.15), Effective hydraulic conductivity in main channel alluvium (CH_K2 = 10.20) and Available water capacity of the soil layer (SOL_AWC = 0.28). The results were analysed and compared with the observational data. The model performance evaluation showed acceptable ranges of values (i.e., Nash Sutcliff was 0.75 and R2 was 0.71). After model calibration, in order to predict water balance, the model was validated by using the best parameter.

Supply Chain Inventory Optimization With Multiple Types of Customers and Equal Priorities: A Study Using Heuristic and Simulation Methods

In this study, the optimal inventory policies for a supply chain with multiple types of customers is investigated to minimize the total inventory cost. Each customer type has specific demand patterns in terms of ordering frequency and order quantity; moreover, there is no difference in priority among the customers. Due to the computational complexity of this optimization problem, we develop a reduced form of the original problem and propose a heuristic that is easily implementable by practitioners. In addition, a simulation model is developed on the same problem setting as a benchmark. To illustrate the implementation and the effectiveness of the proposed heuristic, a numerical example is studied, followed by a sensitivity analysis of the key parameters. The numerical results show that our heuristic provides a comparable performance as simulation-based optimization (a 2.6% difference), and the sensitivity analysis shows a reliable robustness of the model (less than 5.2% difference).