Optimization Model Research Articles

Optimization model of storage detention tanks based on nonlinear time-varying process of surface runoff under low impact development mode

Optimization model of storage detention tanks based on nonlinear time-varying process of surface runoff under low impact development mode

Stochastic optimisation model to optimise the contractual generation capacity of a battery-integrated distributed energy resource in a balancing services contract

Stochastic optimisation model to optimise the contractual generation capacity of a battery-integrated distributed energy resource in a balancing services contract

A Novel Multi-objective Optimization Model for Production Routing Problem with Sustainability Dimensions

A Novel Multi-objective Optimization Model for Production Routing Problem with Sustainability Dimensions

Deployment strategy of PV-ESS for industrial and commercial electricity users with consideration of carbon benefits

As the global shift away from fossil fuels intensifies, distributed photovoltaics (PV) have emerged as the most significant and swiftly expanding renewable energy source accessible to end-users due to their convenience in flexible deployment. Coupled with the steep decline in energy storage costs, the co-deployment of PV and energy storage systems (PV-ESS) has become a preferred option for electricity users, especially large ones. The PV-ESS investment decision-making model is encountering new obstacles stemming from the gradual withdrawal of governmental subsidies and the swift transition of electricity and carbon markets. To address the pressing requirement for investment in PV-ESS for industrial and commercial users, this paper introduces an improved capacity configuration model for PV-ESS that incorporates carbon benefits into its considerations. First, we constructed a cost-benefit analysis model for industrial and commercial users investing in PV-ESS. Second, we proposed a capacity optimization model for maximizing annual returns as its objective function. Finally, to validate this model, we conducted case studies across various typical scenarios to explore optimal configurations and investment returns. The results indicate that within the existing market framework, achieving optimal return on investment for PV-ESS is challenging. However, incorporating carbon benefits can significantly enhance system configuration and investment returns. Specifically, carbon emissions decrease by 23.84% under a low carbon price scenario and by 50.91% under a high carbon price scenario, while the net present value increases by 67.98% and 941.96%, respectively. This study can shed fresh insights for industrial and commercial users and policy-makers for the deployment of PV-ESS.

Improving Efficiency and Sustainability of the Waste-Economy-Environment Nexus in Isolated Island Communities: A Stochastic Inexact Mixed-Integer Fractional Optimization Model

Improving Efficiency and Sustainability of the Waste-Economy-Environment Nexus in Isolated Island Communities: A Stochastic Inexact Mixed-Integer Fractional Optimization Model

OPTIMIZING JOB CREATION AND SOLID WASTE MANAGEMENT SYSTEM: A GOAL PROGRAMMING APPROACH

Unemployment and solid waste management (SWM) are pressing challenges in Nigeria, with youth unemployment reaching critical levels and waste disposal practices straining environmental sustainability. This study explores the integration of job creation and SWM through a goal programming framework, aiming to address resource allocation, sustainability, and managerial decision-making. By leveraging the potential of integrated SWM, such as recycling and material recovery, this research identifies innovative pathways to transform waste into wealth, generate employment opportunities, and promote environmental conservation.Using secondary data from the National Bureau of Statistics (NBS) and Abuja Environmental Protection Board (AEPB), alongside stakeholder interviews, the study develops a multi-objective optimization model using goal programming approach. The results indicate that optimal allocation of resources in waste collection and recycling could create over 1,400 jobs, providing a practical solution to Nigeria's unemployment crisis while fostering sustainable waste management practices. This research underscores the need for stakeholder collaboration, policy reforms, and investments in infrastructure to maximize the dual benefits of job creation and environmental sustainability in the waste management sector.The findings have significant implications for policymakers and waste management stakeholders, offering a replicable framework to balance economic growth and ecological preservation.

Maximization of renewable generation hosting capacity in power transmission grids considering participation in energy and flexibility markets: A bilevel optimization model

Maximization of renewable generation hosting capacity in power transmission grids considering participation in energy and flexibility markets: A bilevel optimization model

Economic gains from geographic shifts in international sourcing

Economic gains from geographic shifts in international sourcing

An integrated Artificial Intelligence and optimization model for operational efficiency and risk reduction in Letter of Credit examination process

An integrated Artificial Intelligence and optimization model for operational efficiency and risk reduction in Letter of Credit examination process

A stomatal optimization model integrating leaf stomata-photosynthetic capacity regulation in response to soil water stress

A stomatal optimization model integrating leaf stomata-photosynthetic capacity regulation in response to soil water stress

A resource bi-level optimization model of water-energy-food-carbon nexus in a regional agricultural system under multi-stakeholder conditions

A resource bi-level optimization model of water-energy-food-carbon nexus in a regional agricultural system under multi-stakeholder conditions

SyMO: A Hybrid Approach for Multi‐Objective Optimization of Crystal Growth Processes

AbstractCrystal growth, particularly silicon, is pivotal in the semiconductor industry. It serves as the foundation for electronic devices, solar cells, and various advanced technologies. The Czochralski method is a prominent technique for producing large single silicon crystals, well‐known for its complexity due to the precise control required over temperature gradients, interface dynamics, and impurity incorporation— all critical factors for growing uniform, high‐quality crystals. This paper proposes a hybrid SyMO (Symbolic regression Multi‐objective Optimization) framework that combines Computational Fluid Dynamics (CFD), machine learning, and mathematical optimization techniques to investigate the effects of various process parameters, furnace geometries, and radiation shield material properties on key crystal quality metrics. The data set created from axisymmetric CFD simulations is used to fit symbolic regression models to effectively capture complex nonlinear relationships, ensuring accurate interface deflection and ratio predictions. The SR equations are integrated into a multi‐objective optimization model that simultaneously optimizes crystal quality and process efficiency. The obtained results are validated through additional CFD simulations to confirm the accuracy of the solution. It is demonstrated that the SyMO successfully generalizes the critical dependencies across various parameters and provides robust, high‐quality solutions.

A Sample Average Approximation Approach for Aircraft Product Configuration Optimization with Customer Order Uncertainty

Commercial aircraft manufacturers often face order uncertainty in particular situations, such as quantity or demand change, or lack of confirmed customer options. As a countermeasure, aircraft manufacturers can adopt a two-stage strategy to produce batch General-Configuration Aircraft (GCA), so as to maintain continuous aircraft production. Nevertheless, additional work on disassembling and re-assembling must be performed, to convert the GCA into specific configurations specified later by the customer. Thus, an appropriate GCA that leads to a minimal overall manufacturing workload is essential. In this paper, a Sample Average Approximation (SAA) model for GCA optimization is proposed, to obtain a robust GCA whose prediction results can help minimize the total production time. Compared with the empirical method, the proposed SAA approach significantly accelerates the production operation and is adaptive to various scenarios. The robustness of the SAA approach was evaluated, and the results prove that the general configuration obtained by the SAA approach has sustainable variances in the manufacturing workload.

Integrated Bayesian and Particle Swarm Approaches for Enhanced Gas Leak Detection in Complex Commercial Structures

During gas leak detection and risk monitoring of commercial sealed areas, different types of sensors are deployed to monitor leak signals. The arrangement of a limited number of sensors in the most strategic positions, solving the problem of optimal sensor placement, is key to improving detection efficiency. Aiming at gas leak detection in commercial areas, this paper proposes a sensor placement methodology based on a particle swarm optimization algorithm to determine the optimal number and position of sensors. First, Bayesian networks assess the gas leak risk levels. Second, a discrete optimization model for sensor placement is established. Finally, the particle swarm optimization algorithm is applied to calculate the optimal sensor placement solution. In the iterative process, partial differential equation models simulate gas diffusion paths to verify the effectiveness of the sensor layout, followed by computational fluid dynamics simulations for further validation of the optimization results. The simulation case of a commercial gas system demonstrates that the proposed method achieves fast convergence and significant optimization results. A real case study shows that the method reduces the number of sensors and data redundancy. Compared with traditional methods, the robustness and efficiency of the system under the optimal solution are significantly improved.

A Bi-Level Programming-Based Method for Service Composition Optimization of Collaborative Manufacturing of Sewing Machine Cases

Aiming at the problem of optimizing the composition of manufacturing resources in part-level outsourcing of sewing machine case manufacturing, this paper proposes a service composition optimization method based on bi-level programming. We analyze the structure and production process of sewing machine cases, determine the required service composition of manufacturing resources, and establish the evaluation indicator system of manufacturing service composition in line with the interests of multiple parties. We also introduce the idea of bi-level programming, construct the optimization model of manufacturing service composition of sewing machine cases based on bi-level planning, analyze the characteristics of NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm II) algorithm and the improvement strategy, and complete the solution of the optimization model of manufacturing service composition of sewing machine cases. The experimental results show that the composition optimization method and the algorithm improvement strategy can well solve the bi-level programming model of sewing machine cases in the case of complex feature information and avoid falling into the local optimal solution.

Marginal abatement costs for greenhouse gas emissions in the United States using an energy systems approach

Abstract Deep decarbonization requires fundamental changes in meeting energy service demands, with some efforts increasing overall costs. Examining abatement measures in isolation, however, fails to capture their interactive effects within the energy system. Here we show the abatement costs of decarbonization in the United States using an energy system optimization model to capture technological interactions, multi-decadal path dependence, and endogenous end-use technology selection. Energy-system-wide net-zero CO2-eq emissions are achieved in 2050 at a cost under $400 per tonne CO2-eq, led by emissions reductions in power generation, end-use electrification of ground transportation, space heating, and some industrial applications. Differences in mitigation costs and CO2 geological storage potential lead to regional heterogeneities in mitigation rates and residual emissions. The marginal abatement cost curves show that additional decarbonization comes at higher incremental costs, this cost penalty decreases over time, and substantially greater abatement occurs in future time periods at the same abatement cost.

Enhancing Efficiency and Resilience Multiobjective Optimization Model and Algorithm for Site Selection of Emergency Material Storage Sites

Enhancing Efficiency and Resilience Multiobjective Optimization Model and Algorithm for Site Selection of Emergency Material Storage Sites

Research on autonomous control strategy for low-voltage station area considering new energy sources and loads uncertainty

ABSTRACT To improve the autonomous level of low-voltage station areas under the uncertainty of new energy resource generation and load electricity consumption, a novel autonomous control model of low-voltage station areas is established. Firstly, the characteristic of this model is the integration of the maximum deficit and maximum surplus generated by the uncertainty of new energy resources and load. Then, the impact of uncertainty is analyzed and an uncertain operation model is proposed by integrating two extreme scenarios. Finally, in a low-voltage station area, the control effects of the proposed method and the determined optimisation model are compared in two extreme scenarios. The results indicate that the proposed method effectively mitigates the impact of uncertainty on line loss, node voltage, and power interaction between low-voltage station area and medium-voltage distribution network. It has a positive promoting effect on the autonomous operation of low-voltage station areas.

Mathematical-Programming-Based Optimization of Alkaline Water Electrolyzer for Green Hydrogen Production

Abstract Hydrogen plays a crucial role in the energy sector toward sustainability and is essential for decarbonizing various sectors of national and international economics. Water electrolysis powered by renewable sources is an environmentally friendly way to produce hydrogen. However, there is room for improvement in the components’ design, dimensions, materials, and electrolysis system. This article focuses on model-based simultaneous optimization of geometric dimensions and operating conditions, such as cell temperature, electrolyte concentration, applied electrolyte pressure, and current density, in an alkaline water electrolysis process. A nonlinear mathematical programming optimization model has been developed. After successful validation against experimental results from the literature, the model was employed for optimization purposes using a gradient-based deterministic optimization approach. The study identifies the operating conditions and geometric dimensions that lead to maximizing cell efficiency and explores the impact of critical operating variables on the optimal solution. The model is implemented in gams software and solved using CONOPT.

Drilling Parameter Control Based on Online Identification of Drillability and Multi-Objective Optimization

Aiming at the problem that drilling parameters are difficult to adjust in time for the driller due to the complex geological environment in underground coal mines, a drilling parameter control method based on online identification of drillability and multi-objective optimization of drilling parameters is proposed. A drillability grade identification model is established, with rotational speed and torque as input parameters, which can accurately identify the current drilling state. A multi-objective optimization model of the optimal drilling parameters is established with the mechanical specific energy and drilling speed prediction model as the objective functions, and the NSGA-II algorithm and TOPSIS algorithm are used for solutions and decision-making. A fuzzy PID controller is established. For the control of rotational speed parameters and drilling pressure parameters, the advantages and disadvantages of the fuzzy PID control method and the traditional PID control method are compared through simulation and experiments. A control method based on the drillability identification model and the multi-objective optimization model is established. According to the different drillability grades, the drilling parameters are adjusted in time to ensure the normal drilling state. By constantly approaching the optimal parameters through the drilling parameters, the drilling efficiency is improved. Through experimental verification, this control method effectively prevents the occurrence of drilling speed reduction and intermittent sticking and can adjust the drilling parameters to continuously optimize.