Simulation-optimization Model Research Articles

Hybrid Cost-Tolerance Allocation and Production Strategy Selection for Complex Mechanisms: Simulation and Surrogate Built-In Optimization Models

Abstract In manufacturing companies, assembly is an essential process to obtain the final product. The life cycle of an assembly product depends on various production strategies, e.g., resource allocation, rework decision, selection strategy, etc. In this regard, achieving a reliable assembly product commence with engineering a comprehensive design plan which can mitigate various uncertainties a company can face. The counteraction of uncertainties can be altered by introducing a set of tolerances into the design of the components. Tolerances define a practical margin on components design without downgrading the required performance of products. Thus, producers are confronted with high-quality requirements, cost pressure, and a rising number of demands. On these bases, this paper aims at modeling a statistical framework for a set of production strategies, including resource allocation (as a decision to assign practical resources to components) and reworking decision (as a decision to improve components’ conformity rate). Moreover, a generic simulation and surrogate approach are established to evaluate the performance of the assembled product. Within this approach, simulation and surrogate models can be used to investigate a variety of deviations over components’ geometries within the process deviation domain and deploy reworking decision. Ultimately, a modular costing system is developed, and a genetic algorithm is adapted to locate optimal solutions. In addition, the applicability of the statistical model is studied on an assembly product.

Convective drying of mango stone for use as biomass

Mango stone is an interesting biomass by-product with a considerable net calorific value. Mango production has significantly risen in the last few years, meaning that mango waste has increased as well. However, mango stone has a moisture content of about 60% (wet basis) and it is very important to dry the samples for using them in electrical and thermal energy production. In this paper, the main parameters involved in the mass transfer during drying are determined. Drying was carried out in a convective dryer through a set of experiments based on five drying air temperatures (100 °C, 125 °C, 150 °C, 175 °C and 200 °C) and three air velocities (1 m/s, 2 m/s and 3 m/s). Drying times ranged between 2 and 23 h. The drying rate was calculated from the Gaussian model whose values ranged from 1.5·10−6 to 6.3·10−4 s−1. Effective diffusivity was obtained as an overall parameter in the mass diffusion for each test. These values were found between 0.71·10−9 and 13.6·10−9 m2/s. The activation energy was calculated from the Arrhenius law for each test, made at different air velocities. These values were 36.7, 32.2 and 32.1 kJ/mol for 1, 2 and 3 m/s, respectively. This study provides information for future works on design, optimization and numerical simulation models in convective dryers for standard mango stone pieces according to industrial drying conditions.

Optimization of low-impact development facilities in urban areas using slime mould algorithm

Qualitative and quantitative evaluation of urban runoff is a major concern for urban management, imposing significant costs on the urban management system every year. Numerous; factors affect the quality, and quantity of urban runoff, such as population growth, urbanization, land use change and climate change. This paper aims to optimally design low-impact development (LID) strategies for qualitative and quantitative management of urban areas. Three runoff management methods, including vegetated swale, bioretention system, and porous pavement, were considered along with an urban drainage network. The stormwater management model (SWMM) was adopted to simulate urban runoff, and the slime mould algorithm (SMA) was employed to optimize LIDs. An urban area in Tehran, Iran, was examined using the proposed SWMM-SMA model, and the result was an optimal combination of LIDs. Three LIDs were individually defined for each subcatchment, and the results were compared to the combination of all LIDs in the whole catchment. The results indicated that the bioretention system was the most effective LID in improving the water quality in the case of individual LIDs. The system could reduce total suspended solids (TSS), total nitrogen (TN); and zinc (Zn) concentrations by 91, 78, and 74%. The results also suggested that the optimal combination of LIDs was more efficient, and it was able to further reduce TSS concentration by 14–16% compared to individual bioretention systems. Furthermore; the optimal combination of LIDs was successful in minimizing TSS concentration in the runoff by using the SWMM-SMA model. By using the simulation-optimization model (SWMM-SMA), optimal LIDs and runoff quality could be effectively designed and controlled.

Reconciling crop production and ecological conservation under uncertainty: A fuzzy credibility-based multi-objective simulation-optimization model

To cope with the problems of agricultural water conflicts and secondary soil salinization in arid regions, a fuzzy credibility-based multi-objective simulation-optimization model is proposed for optimizing irrigation water allocation and crop area planning under uncertainty. This model combines simulation module of enabling to quantify daily physical process of water-salt movement among soil water, crop root zone and groundwater aquifers, optimization module of managing water resources and fuzzy credibility-constrained programming into a general framework. It's applied to a case study in the Jiefangzha Irrigation Subarea in Hetao Irrigation District, Northwest China. Three objectives encompassing maximizing net economic benefits, maximizing nutritional water productivity and minimizing carbon emissions from agricultural system are interconnected through decision variables. Four credibility scenarios of fuzzy constrains including β = 0.6 to 0.9 are presented for obtaining decision-making solutions. Through NSGA-III, such a high-dimensional multi-objective problem is solved. This study uses the multi-objective constraint-handling strategy to handle constraints, which exploits the effective information within infeasible solutions. Moreover, it emphasizes the importance of soil water-salt movement processes in determining optimal solutions and helps decision makers weigh the system outputs and risk level of violating constraints. Results illustrate that when β increases from 0.6 to 0.9, net economic benefit decreases from 1.742 × 109 Yuan to 1.706 × 109 Yuan, nutritional water productivity decreases from 9136.0 kcal/m3 to 8819.6 kcal/m3, and carbon emissions increase from 439.6 × 106 kg. C to 441.6 × 106 kg.C, which shows that an increasing credibility level leads to lower system benefits and conservative system outputs. The results can provide valuable information for managing irrigation water resources and controlling salinity accumulation. Furthermore, dynamic decisions related to water-salt movement processes can be readily generated. These findings show that the developed approach is globally applicable for managing irrigation water in arid and semiarid regions that face similar problems.

A Novel Wastewater Load Allocation Approach for River Basins Using Simulation-Optimization Models

In this study, a new wastewater load allocation approach using a linked simulation-optimization model is proposed to determine the receiving body-based discharge limits by considering the discharge standards used by the European Union Water Framework Directive. By using the proposed approach, wastewater loads of point sources can be determined in such a way that the parameters exceeding the water quality targets (WQT) in receiving water bodiesmeet the relevant WQT. The simulation part is used to determine pollutantconcentrations throughout the river system using the AQUATOX water quality simulation model. However, since AQUATOX is an independent simulation modeland its source code is not publicly available, it is not possible to execute it with the optimization model for the generated load combinations. Therefore, a concentration-response matrix (CRM) is developed as a surrogate water simulation model by using the outputsoftheAQUATOX model. After this process, the developed CRMis integrated into an optimization model where the heuristic differential evolution (DE) optimization approach is used. The performance of the proposed simulation-optimization approach is evaluated on a sub-watershed of the Kucuk Menderes River Basin by considering different wasteload allocation scenarios for the CBOD5 water quality parameter. The results showed that the proposed simulation-optimization approach can effectively allocate the wastewater loads among different point sources by considering the WQT values of the CBOD5 parameter.

Evaluating the effect of aquifer heterogeneity on multiobjective optimization of in-situ groundwater bioremediation

In-situ bioremediation is a cost-effective technique to eliminate petroleum hydrocarbons from groundwater. In previous studies, this has been mostly applied under the approximation of a homogeneous porous medium, even though in reality aquifers are often characterized by significant heterogeneities. Here, different heterogeneous and equivalent homogeneous hydraulic conductivity fields are considered to study the effect of aquifer heterogeneity on optimal in-situ bioremediation. For this, the multiobjective simulation-optimization (S/O) model referred to as BIOEFGM-NSGA II is proposed — which uses the element-free Galerkin method (EFGM) for discretization of the governing equations, and the non-dominated sorting genetic algorithm II (NSGA II) for multiobjective optimization. This model is then applied to different heterogeneous conductivity fields generated through a pseudo-random correlated field generator for different combinations of variance and correlation lengths with constant mean. Results show that the optimal pumping policy for an equivalent homogeneous conductivity field violates the constraint of maximum allowable concentration in all the studied heterogeneous fields. To satisfy this constraint, in-situ bioremediation cost increases by 8.47% to 56.83% to that of a homogeneous field. It shows the significance of aquifer heterogeneity in designing an optimized in-situ bioremediation system and hence, should be incorporated in the S/O model for in-situ groundwater bioremediation.

Artificial Intelligence Based Smart Home Energy Management System

Recently, countries are faced with an unreliable electric power supply to homes and the industry due to insufficient supply from the utility, causing load shedding. With the power utilities struggling to keep the lights on, there are initiatives to invest in backup power solutions and home energy management systems (HEMS) to manage this development. This paper analyzes energy consumption with the existing infrastructure of typical equipment used in homes. An approach called smart home energy management systems (SHEMS) is implemented to balance the power demand and the supply more effectively throughout the smart micro-grid by utilizing control algorithms simulated using MATLAB Simulink. This is achieved by utilizing real data from a home with commercial and residential load features in an urban area. The SHEMS revealed greater management and efficiency in electric power savings by reducing the utilities power demand. The framework adopts a local information management terminal as the core of data storage and scheduling in the home. Based on the timely purchase of electricity from the grid and the generation of electricity in combination with PV systems, an optimized simulation model for the scheduling of a new home energy management system is established. In addition, the application prospects of artificial intelligence in the HEMS are overviewed. Key Words: Balance of Power Demand, Energy efficiency, Smart Distribution Board (SDB), Smart Home Energy Management System (SHEMS), Artificial Intelligence.

A simulation-optimization approach for integrating physical and financial flows in a supply chain under economic uncertainty

In the last decade, increasing costs and organizational concerns regarding the funding and allocation of financial resources have led to significant attention being given to financial flow and its effects on planning decisions throughout supply chain networks. This study aims to develop a simulation-optimization model to integrate the financial and physical flows in a supply chain planning problem under economic uncertainty. The simulation-optimization model includes a mixed-integer linear programming model and a simulation-based optimization model that are connected through an iterative process. The economic value added (EVA) index is used to measure the financial performance of the supply chain. This study extends the literature on two research domains namely supply chain planning and finance and simulation-optimization modelling for supply chain management. The proposed model applies a scenario approach to cope with economic uncertainty in the supply chain. To demonstrate the efficiency of the proposed model, the performance of the proposed model in solving a test problem from the recent literature is compared with the performance of a conventional simulation-based optimization and mixed-integer linear programming approaches. The results of the study show a minimum of 6% improvement in the EVA obtained from the proposed simulation-optimization model compared to the EVA obtained from the simulation-based optimization model in all the studied scenarios. Moreover, the standard deviation of the EVA obtained from the proposed simulation-optimization model is at least 69% lower than the EVA obtained from the mixed integer programming model in all the studied scenarios. This shows that the proposed simulation-optimisation approach is more robust to economic uncertainty than the mixed-integer linear programming approach.

A Novel Approach for Optimum Conjunctive Use Management of Groundwater and Surface Water Resources under Uncertainty

Uncertainty in determining optimum conjunctive water use lies not only on variability of hydrological cycle and climate but also on lack of adequate data and perfect knowledge about groundwater-surface water system interactions, errors in historic data and inherent variability of system parameters both in space and time. Simulation-optimization models are used for conjunctive water use management under uncertain conditions. However, direct application of such approach whereby all realizations are considered at every-iteration of the optimization process leads to a highly computational time-consuming optimization problem as the number of realizations increases. Hence, this study proposes a novel approach—a Retrospective Optimization Approximation (ROA) approach. In this approach, a simulation model was used to determine aquifer system responses (draw-downs) which were assembled as response matrices and incorporated in the optimization model (procedure) as coefficients in the constraints. The sample optimization sub-problems generated, were solved and analyzed through ROA-Active-Set procedure implemented under MATLAB code. The ROA-Active Set procedure solves and evaluates a sequence of sample path optimization sub-problems in an increasing number of realizations. The methodology was applied to a real-world conjunctive water use management problem found in Great Letaba River basin, South Africa. In the River basin, surface water source contributes 87% of the existing un-optimized total conjunctive water use withdrawal rate (6512.04 m3/day) and the remaining 13% is contributed by groundwater source. Through ROA approach, results indicate that the optimum percentages contribution of the surface and subsurface sources to the total water demand are 58% and 42% respectively. This implies that the existing percentage contribution can be increased or reduced by ±29% that is groundwater source can be increased by 29% while the surface water source contribution can be reduced by 29%. This reveals that the existing conjunctive water use practice is unsustainable wherein surface water system is overstressed while groundwater system is under-utilized. Through k-means sampling technique ROA-Active Set procedure was able to attain a converged maximum expected total optimum conjunctive water use withdrawal rate of 4.35 × 104 m3/day within a relatively few numbers of iterations (6 to 8 iterations) in about 2.30 Hrs. In conclusion, results demonstrated that ROA approach is capable of managing real-world regional aquifers sustainable conjunctive water use practice under hydro-geological uncertainty conditions.

Sustainable planning of multipurpose hydropower reservoirs with environmental impacts in a simulation–optimization framework

Abstract Hydropower projects involve enormous investments that require an efficient cost–benefit framework and optimization model for proper development. Dams and hydropower plants have many impacts on the environment. These environmental impacts are often not included in the economic calculations and planning of the projects, which leads to the loss of natural resources. The primary purpose of this research is to incorporate environmental impacts into optimization and decision-making. A comprehensive simulation–optimization model is developed to optimize hydropower decisions. The positive and negative values of environmental impacts are incorporated into an economic objective function under different scenarios, and optimal design was done for each scenario. The results show that considering environmental economics affects the multipurpose hydropower project's NPV and decision outcomes. Considering environmental impacts compared to not considering them has reduced NPV of the project by 13.9%. The results emphasize the importance of including these impacts to achieve sustainable development and management.

Incorporating ecosystem services value into the optimal development of hydropower projects

Using an efficient cost-benefit analysis framework and optimization model is essential for the proper planning of hydropower projects. The implementation of hydropower has different effects on the environment, which undermine the delivery of ecosystem services. The change in ecosystem services is often not included in hydropower projects' economic analysis and planning. Understanding the quantity of ecosystem services and their associated economic value is an important step to protection of ecosystems and provides an opportunity to enter ecosystem services into economic relations. Thus, the main purpose of this study is to investigate how to incorporate ecosystem services into project optimization models and decision-making. So, an attempt is made to quantify these environmental effects to develop a comprehensive simulation-optimization model and achieve a more sustainable development of hydropower projects in the system. The costs and benefits from the environmental economy include hydropower advantages and loss of ecosystem services added to the objective function. The results show that considering the environmental affects the NPV and decision variables such as the normal water level and installed capacity. The economic value of negative environmental impacts was more than its advantages and caused about 27% and 14.5% decrease in NPV and energy generation of the HPP project.

Thermo-hydraulic condition optimization of large-scale complex district heating network: A case study of Tianjin

District heating (DH) networks are indispensable infrastructure for space and domestic heating with high energy efficiency. As the structures of DH networks are gradually becoming complex, efficient and accurate simulation model for the operational optimization of the DH network is crucial. In this paper, an optimization method for the DH network operation is proposed. The method is based on the thermo-hydraulic coupled dynamic model, sequential quadratic programming (SQP) and particle swarm optimization (PSO), which is applied to a large-scale DH network in Tianjin, China. With the proposed method, 6.7%∼11% energy consumption can be reduced, under the operation condition of 80%∼100% design flow rate. The transmission and distribution cost can be reduced with an average of 6.2% at the outdoor temperature ranging from −5 to 5 °C.

Artificial Intelligence Based Smart Home Energy Management System: A Review

Recently, countries are faced with an unreliable electric power supply to homes and the industry due to insufficient supply from the utility, causing load shedding. With the power utilities struggling to keep the lights on, there are initiatives to invest in backup power solutions and home energy management systems (HEMS) to manage this development. This paper analyzes energy consumption with the existing infrastructure of typical equipment used in homes. An approach called smart home energy management systems (SHEMS) is implemented to balance the power demand and the supply more effectively throughout the smart micro-grid by utilizing control algorithms simulated using MATLAB Simulink. This is achieved by utilizing real data from a home with commercial and residential load features in an urban area. The SHEMS revealed greater management and efficiency in electric power savings by reducing the utilities power demand. The framework adopts a local information management terminal as the core of data storage and scheduling in the home. Based on the timely purchase of electricity from the grid and the generation of electricity in combination with PV systems, an optimized simulation model for the scheduling of a new home energy management system is established. In addition, the application prospects of artificial intelligence in the HEMS are overviewed. Key Words: Balance of Power Demand, Energy efficiency, Smart Distribution Board (SDB), Smart Home Energy Management System (SHEMS), Artificial Intelligence.

Multi-period consequence management of contaminations in water distribution networks: application of regret-based optimization model

ABSTRACT This study presents a Regret-based optimization model for multi-period consequence management of contaminations entering the WDNs considering the pollutants loads’ entry uncertainties. For this purpose, first, the EPANET software is utilized as a water quality-quantity simulation modeling tool. Then, an optimization model based on a genetic algorithm using the primary objective functions of minimizing the return time of the contaminated WDNs to the normal mode, the amount of pollution, and the number of polluted nodes. Besides, the pollutant load-related uncertainties are included in the presented simulation-optimization model by minimizing the maximum Regret (MMR) and the total Regret (MTR) in two steady and dynamic status for all three mentioned objective functions. Using these nine objective functions, three management instruments are utilized: quick closing valves, discharging hydrants, and boosting pumps. The results show that the proposed model can effectively prevent harmful pollution by offering effective management strategies, mainly in dynamic status scenarios.

Optimization of Irrigation Scheduling for Improved Irrigation Water Management in Bilate Watershed, Rift Valley, Ethiopia

The availability of water for agricultural production is under threat from climate change and rising demands from various sectors. In this paper, a simulation-optimization model for optimizing the irrigation schedule in the Bilate watershed was developed, to save irrigation water and maximize the yield of deficit irrigation. The model integrated the Soil and Water Assessment Tool (SWAT) and an irrigation-scheduling optimization model. The SWAT model was used to simulate crop yield and evapotranspiration. The Jensen crop-water-production function was applied to solve potato and wheat irrigation-scheduling-optimization problems. Results showed that the model can be applied to manage the complicated simulation-optimization irrigation-scheduling problems for potato and wheat. The optimization result indicated that optimizing irrigation-scheduling based on moisture-stress-sensitivity levels can save up to 25.6% of irrigation water in the study area, with insignificant yield-reduction. Furthermore, optimizing deficit-irrigation-scheduling based on moisture-stress-sensitivity levels can maximize the yield of potato and wheat by up to 25% and 34%, respectively. The model developed in this study can provide technical support for effective irrigation-scheduling to save irrigation water and maximize yield production.

The construction and application of event logic graph for pedestrian flow evacuation in typical scenarios

An event logic graph is a kind of knowledge mapping technology for knowledge inference and simulation analysis, which takes events as the core and portrays the hierarchical system and logical evolution pattern between events. In order to apply it to further improve the accuracy of related studies, such as pedestrian flow evacuation, simulation model optimization and risk prediction. In this paper, we use social network resources, media resources and journal database resources to build our corpus and adopt the explicit event relationship extraction method based on syntactic dependency and the implicit event relationship extraction method based on BERT+Bi-LSTM+Attention+Softmax for the characteristics of explicit event relationship and implicit event relationship, respectively. This paper constructs a pedestrian flow evacuation matter mapping for three typical scenarios and discusses its application path. It is found that once a sound knowledge base of logical reasoning and event logic graph is established, both research on optimization of pedestrian flow evacuation simulation models and research on identification and assessment of pedestrian flow evacuation safety risks will receive excellent support.

Modelling Techniques for Conjunctive Water Management: A Review

Optimal allocation of surface water and groundwater is the key factor for conjunctive water management. Proper planning is necessary for optimal allocation of water for maximizing crop yield benefits and/or minimizing water quality problems. With the advent of computers, by the late 1960s, computer-aided mathematical models came into use to help decision-makers in planning conjunctive water management. Optimization models were popular in the early decades. Then researchers came to know the advantage of mimicking the situations and analyzing them before arriving at optimal solutions. Thus, simulation models and integrated simulation-optimization models came into existence. Multi-objective models were developed to solve more complicated physical situations. The computational burden of running thousands or lakhs of simulations before getting an optimum solution, generated evolution algorithms like ANN and GA to reduce the running time of models and obtain a more accurate solution. Even now we can’t say the modelling approach is perfect to solve the real-world water-related issues that involve complicated physical phenomenon. A model which is more accurate, simpler and very near to real situations, may be developed in near future.

Effect of Magnetic Shielding on Levitation Characteristics of Superconducting Gravimeter

The superconducting gravimeter is currently the most accurate instrument in relative gravity measurement, so the high-precision calculation and analysis of superconducting magnetic levitation system is crucial to the development work of superconducting gravimeter. Shielding material can weaken the influence of external magnetic field source fluctuation on the internal levitation magnetic field and provide a stable electromagnetic environment for the magnetic levitation system. But at the same time, shielding material will also have an impact on the internal suspended magnetic field itself, which will lead to large deviations in the study of electromagnetic characteristics of the magnetic levitation system. This article establishes and optimizes the simulation calculation model of a superconducting gravimeter magnetic levitation system based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> method, and studies the effect of magnetic shielding on the magnetic field, levitation force, and magnetic force gradient of the internal levitation system. After that, the simulation calculation results are compared with the experimental results, and the simulation calculation error of superconducting levitation force and magnetic gradient is less than 1%, which verifies the correctness of the optimized simulation model. This study is an important guide for the accurate calculation of the electromagnetic properties of all superconducting systems containing magnetic shielding materials, such as superconducting gravimeter, superconducting gravity gradiometer, etc.

Simulation-optimization based real-time irrigation scheduling: A human-machine interactive method enhanced by data assimilation

Efficient irrigation scheduling is crucial for both improving crop production and saving irrigation water use in arid/semi-arid agricultural regions threatened by water shortage and soil salinity. However, irrigation scheduling optimization is hindered by the uncertainties of data and optimization model, and adopting the optimal irrigation scheduling is subject to farmers' acceptance. To effectively tackle these challenges, this paper presents a novel human-machine interactive framework for real-time irrigation scheduling (RIS). The developed modeling framework couples a simulation-optimization model, irrigation farmers, and a data assimilation procedure within the human-machine interactive framework for RIS. The proposed approach is capable of: 1) searching optimal irrigation scheduling through the simulation-optimization model; 2) making actual irrigation decisions based on farmers' experiences, knowledge, behaviors, or optimal solutions; and 3) updating soil water content based on the model simulations and real-time observations at each time period. The RIS is applied to a real-world case in a typical arid agricultural region of China. Based on the comparisons with historical irrigation records and a tradition simulation-optimization model, the proposed RIS can not only achieve higher economic benefit with less irrigation water allocation quotas, but also improve irrigation efficiency. This study contributes to the methodology by integrating computer model, real-time observations and farmers' experiences to optimization modeling framework for supporting sustainable irrigation water management.

Modeling and economic optimization of an industrial site for natural gas processing: A nonlinear optimization approach

In this work an integrated simulation-optimization model to determine the optimal economic operating point of an industrial site for natural gas processing with multiple process units and feedstock. Natural gas industrial facilities have the intrinsic characteristic of dealing with dynamic scenarios, with no control over raw inlet gas, which might lead the plant to suboptimal conditions. In this context, an integrated simulation-optimization framework was proposed, using Aspen HYSYS for process simulation, Python for optimization and Microsoft Excel as data transfer interface. Process model comprises three slug catchers, five gas processing units, three liquid fractionating units, compression systems, auxiliary equipment and gas treating units. The optimization problem was formulated based on the maximum business profit, incorporating, as penalty functions, inequality constraints: product specifications, logistic bottlenecks and plant physical limits. Optimization model was validated against actual data from an industrial facility responsible for processing over one quarter of all natural gas used in Brazil. The nonlinear programming was successfully executed using Particle Swarm Optimization method in series with Nelder-Mead flexible polyhedron for ten normalized decision variables indicating a potential increase of 7.3 % in variable profit, as well as the existence of local minima in the optimization space.