Existing Optimization Models Research Articles

Combination of Site-Wide and Real-Time Optimization for the Control of Systems of Electrolyzers

The integration of renewable energy sources into an energy grid introduces volatility, challenging grid stability and reliability. To address these challenges, this work proposes a two-stage optimization approach for the operation of electrolyzers used in green hydrogen production. This method combines site-wide and real-time optimization to manage a fluctuating energy supply effectively. By leveraging the dual use of an existing optimization model, it is applied for both site-wide and real-time optimization, enhancing the consistency and efficiency of the control strategy. Site-wide optimization generates long-term operational plans based on long-term forecasts, while real-time optimization adjusts these plans in response to immediate fluctuations in energy availability. This approach is validated through a case study showing that real-time optimization can accommodate renewable energy forecast deviations of up to 15%, resulting in hydrogen production 6.5% higher than initially planned during periods of increased energy availability. This framework not only optimizes electrolyzer operations but can also be applied to other flexible energy resources, supporting sustainable and economically viable energy management.

Reservoir Optimization Scheduling Driven by Knowledge Graphs

As global climate change intensifies, the challenges of water scarcity and flood disasters become increasingly severe. This severity makes efficient reservoir scheduling management crucial for the rational utilization of water resources. Due to the diverse topological structures and varying objectives of different watersheds, existing optimization models and algorithms are typically applicable only to specific watershed environments. This specificity results in a “one watershed, one model” limitation. Consequently, optimization of different watersheds usually requires manual reconstruction of models and algorithms. This process is not only time-consuming but also limits the versatility and flexibility of the algorithms. To address this issue, this paper proposes a knowledge graph-driven method for reservoir optimization scheduling. By improving genetic algorithms, this method allows for the automatic construction of optimization models tailored to specific watershed characteristics based on knowledge graphs. This approach reduces the dependency of the optimization model on manual modeling. It also integrates hydrodynamic simulations within the watershed to ensure the effectiveness and practicality of the genetic algorithms. Furthermore, this paper has developed an algorithm that directly converts optimized reservoir outflow into actionable dispatch instructions. This method has been applied in the Pihe River Basin, optimizing flood control and resource management strategies according to different seasonal demands. It demonstrates high flexibility and effectiveness under varying hydrological conditions, significantly enhancing the operational efficiency of reservoir management.

Design of an improved process optimization model for enhancing the efficiency of the wastewater treatment process

ABSTRACT Efficient wastewater treatment is crucial for environmental protection and ensuring the availability of potable water. However, existing optimization models often fall short in effectively analyzing historical data, optimizing dosages, or predicting optimal process parameters. This paper aims to address these challenges by proposing an advanced process optimization model that enhances the efficacy of wastewater treatment processes. Our objectives include the development of a novel method for optimizing various aspects of the treatment procedure using Auto Encoders (AE), Genetic Algorithm (GA), and Vector Autoregressive Moving Average (VARMA) models. Extensive experimentation with multiple datasets and samples, including the Melbourne Wastewater Treatment Dataset, Urban Wastewater Treatment, and Global Wastewater Treatment, was conducted to validate our model. The results demonstrate significant improvements in our proposed model, with an average improvement of 8.5% in treatment quality, a 4.9% reduction in delay, and a 9.5% increase in water purity compared to recently proposed models. We conclude that our model provides a valuable framework for optimizing wastewater treatment in a variety of settings due to its adaptability and effectiveness. Future research could explore the integration of additional advanced techniques and real-time monitoring systems to further enhance the model's precision, efficiency, and robustness.

Low-cost and flexible generation expansion planning with localization of power plants in a test bus system

ABSTRACT Generation Expansion Planning (GEP) is a challenge in electrical power systems because the size of the generating unit is large in scale, non-linear, long-term, and discontinuous. The existing GEP models use an array of methodological techniques. These models, however, primarily focus on the type of generation unit to be installed and when to be installed so as to reduce pollution and overall costs. They do not focus on the optimal location for installation. This research work proposes an IEEE bus-30 and IEEE bus-14 merged bus systems to fulfil the electrical load demand during the 5th and 10th years of planning. In stage 1, the GEP problem is resolved using Black Widow Optimization (BWO). In stage 2, the optimal location for generating units in the proposed bus system is resolved using a Chimp Optimization Algorithm (ChoA). The best location reduces the objective function (real power loss) and satisfies the voltage and power flow limits of the electrical power system. The performance of the proposed model is compared to that of existing optimization models. The results demonstrate that the proposed work reduces costs and provides flexible operations with reduced real power loss.

A data-driven DRL-based home energy management system optimization framework considering uncertain household parameters

With the rise in household computing power and the increasing number of smart devices, more and more residents are able to participate in demand response (DR) management through the home energy management system (HEMS). However, HEMS has encountered challenges in developing the most effective energy management strategies, including the complexity of modeling user comfort, uncertainty in electricity price and photovoltaic (PV) output, and the challenge of solving high-dimensional time-coupled decision problems. To address these challenges, a novel data-driven deep reinforcement learning (DRL)-base HEMS optimization framework considering uncertain household parameters is proposed. Firstly, a thermal comfort evaluation model based on integrated learning is proposed. Then, a prediction model based on the bidirectional gated recurrent unit neural network (BiGRU-NN) algorithm is proposed to mine the time series PV output and electricity price data. Finally, combining the PV output and electricity price forecasting, along with the thermal comfort evaluation, an optimal decision-making method based on soft actor-critic (SAC) algorithm for the HEMS is established. The results of numerical experiments show that the proposed method can effectively solve the high-dimensional integrated decision-making problem with uncertainty. By participating in DR, the household electricity cost can be reduced by 17.7% and the total cost can be reduced by 8.4%. Furthermore, the comparison result shows that the method proposed in this paper performs better than the existing optimization models based on proximal policy optimization (PPO) algorithm and twin-delayed depth deterministic policy gradient (TD3) algorithm.

Metro stop-skipping rescheduling optimization considering passenger waiting time fairness

ABSTRACT In urban metro systems, unexpected events often occur to cause discordance in the planned timetable and delay in passenger travel. Therefore, this study focuses on the timetable rescheduling problem by adopting a stop-skipping strategy during the off-peak period. In addition, there are a number of existing optimization models that minimize the total passenger waiting time, which can cause fairness issues for a small group of passengers having longer waiting time at some stations. In this study, a rescheduling model is proposed to consider passenger waiting time fairness and train travel time cost. This model is tested with a randomly designed delay scenario of Nanjing Metro Line 2, which can be efficiently solved by a designed iterative algorithm based on decomposition method. Compared to the standard stop pattern, the result shows that the stop-skipping pattern can better alleviate the impact of sudden delays, while simultaneously ensuring the passenger waiting time fairness.

Optimization Configuration of Distributed Multi-Energy Complementary Energy Systems

This paper points out the uncertainty of existing optimization models due to factors such as environmental and equipment output, which have a significant impact on the accuracy and rationality of system planning. Therefore, this paper establishes a full-working-condition-mathematical model for distributed multi-energy complementary energy systems and constructs an optimization configuration mathematical model with the total annual cost of the system as the optimization objective. Subsequently, an energy supply equipment optimization configuration method for distributed multi-energy complementary energy systems is proposed. Finally, the effectiveness of the proposed optimization configuration method is verified through case studies. This research will have important guiding significance for the construction and operation of distributed multi-energy complementary energy systems.

An Autonomic Workload Prediction and Resource Allocation Framework for Fog-Enabled Industrial IoT

The Internet of Things (IoT) has revolutionized the industrial field with numerous facilities and advancements. The industrial IoT system demands delay-aware workload execution with the aid of a fog computing platform, and precise resource allocation is required in fog nodes (FNs) to execute the fluctuating industrial IoT workloads with minimal cost and delay. In view of the issue mentioned above, we introduce an autonomic workload prediction and resource allocation framework that efficiently allocates resources among fog nodes. In the proposed framework, the workloads are predicted in the analysis phase with the guidance of the Deep Auto Encoder (DAE) model, and the fog nodes are scaled based on the demand of Industrial IoT workloads. The crow search algorithm (CSA) is integrated with the framework for optimal fog node selection to improve cost and delay objectives. The proposed scheme is evaluated and compared with the existing optimization models in terms of execution cost, request rejection ratio, throughput, and response time. The simulation results establish that the proposed scheme outperformed other optimization models. The method provided a suitable solution for the optimal fog node placement problems in efficiently executing dynamic industrial IoT workloads.

Pricing and advertising decisions in a direct-sales closed-loop supply chain

Remanufacturing and recycling of end-of-life products have changed the structure of supply chain networks, and the option of closed-loop supply chains gains popularity. Growing strict environmental and social legislations are expected to enhance sustainability of closed-loop supply chains. This study focuses on the pricing and advertising decisions in a closed-loop supply chain network. Although pricing decisions have been well-studied in this area, there is a little attention to advertising decisions. It is well-known that advertising plays a significant role in influencing customer behavior in returning end-of-life products for the closed-loop supply chain. Therefore, this research develops an operational and tactical plan for promoting advertising programs considering different elasticity effects. As such, the proposed optimization plan considers the pricing decisions in a more comprehensive view, where the price of similar products in the market and their substitution degree have a high impact on the profitability of manufacturers in a direct-sales closed-loop supply chain. Hence, the main novelty of this paper is to develop a new optimization model with pricing and advertising decisions in a direct-sales closed-loop supply chain. Since the proposed model is more complex than the majority of existing optimization models in the area of closed-loop supply chains, another novelty of this paper is to propose an improvement to the standard particle swarm optimization algorithm using the crowd-learning theory. The developed algorithm is validated by the exact solver and compared with the state-of-the-art algorithms in this research area. An extensive computational experiment is performed considering a number of comparative metrics. The findings show the superior performance of the proposed metaheuristic against the alternative solution approaches in terms of computational time and solution quality. Moreover, some important insights are obtained from this research, which could provide a basis for configuration of pricing schemes and advertising campaigns to improve the efficiency of closed-loop supply chains.

Matching functions for free-floating shared mobility system optimization to capture maximum walking distances

Shared mobility systems have become a frequently used inner-city mobility option. In particular, free-floating shared mobility systems are experiencing strong growth compared to station-based systems. For both, many approaches have been proposed to optimize operations, e.g., through pricing and vehicle relocation. To date, however, optimization models for free-floating shared mobility systems have simply adopted key assumptions from station-based models. This refers, in particular, to the models’ part that formalizes how rentals realize depending on available vehicles and arriving customers, i.e., how supply and demand match. However, this adoption results in simplifications that do not adequately account for the unique characteristics of free-floating systems, leading to overestimated rentals, suboptimal decisions, and lost profits.In this paper, we address the issue of accurate optimization model formulation for free-floating systems. Thereby, we build on the state-of-the-art concept of considering a spatial discretization of the operating area into zones. We formally derive two novel analytical matching functions specifically suited for free-floating system optimization, incorporating additional parameters besides supply and demand, such as customers’ maximum walking distance and zone sizes. We investigate their properties, like their linearizability and integrability into existing optimization models. Our computational study shows that the two functions’ accuracy can be up to 20 times higher than the existing approach. In addition, in a pricing case study based on data of Share Now, Europe’s largest free-floating car sharing provider, we demonstrate that more profitable pricing decisions are made. Most importantly, our work enables the adaptation of station-based optimization models to free-floating systems.

A bi-objective optimization model for the medical supplies' simultaneous pickup and delivery with drones

In the COVID-19 pandemic, it is essential to transport medical supplies to specific locations accurately, safely, and promptly on time. The application of drones for medical supplies delivery can break ground traffic restrictions, shorten delivery time, and achieve the goal of contactless delivery to reduce the likelihood of contacting COVID-19 patients. However, the existing optimization model for drone delivery is cannot meet the requirements of medical supplies delivery in public health emergencies. Therefore, this paper proposes a bi-objective mixed integer programming model for the multi-trip drone location routing problem, which allows simultaneous pick-up and delivery, and shorten the time to deliver medical supplies in the right place. Then, a modified NSGA-II (Non-dominated Sorting Genetic Algorithm II) which includes double-layer coding, is designed to solve the model. This paper also conducts multiple sets of data experiments to verify the performance of modified NSGA-II. Comparing with separate pickup and delivery modes, this study demonstrates that the proposed optimization model with simultaneous pickup and delivery mode achieves a shorter time, is safer, and saves more resources. Finally, the sensitivity analysis is conducted by changing some parameters, and providing some reference suggestions for medical supplies delivery management via drones.

A Risk-Controllable Day-Ahead Transmission Schedule of Surplus Wind Power with Uncertainty in Sending Grids

Transmitting the surplus wind power (SWP) to load centers is necessary for grids which would reduce the wind curtailment. The reliability and sharpness of the existing estimation models of SWP are not enough to support the optimization of the transmission schedule. Besides, the existing optimization models for wind power scheduling are insufficient in controlling the high risk of SWP transmission. To address the above problems, this paper proposes a random fuzzy model (RFM) for SWP estimation and a risk-controllable optimization (RCSO) model for the SWP transmission schedule. In the RFM, a conditional probability of SWP is utilized to represent the actual probability distribution. The impacts of wind power consumption that cause the misalignment of existing models are considered in RFM by a fuzzy set. In RCSO, the economic and security risks of SWP transmission are quantified by the conditional value at risk (CVaR) theory. Transmission risks are controlled through automatic adjustment of the risk factor. An equivalent transformation method of the RCSO model is derived which can solve the nonlinear stochastic optimization problem with multi-objective. A case based on the historical data in Northeast China is studied to verify the effectiveness of the proposed models.

A data-driven, variable-speed model for the train timetable rescheduling problem

Train timetable rescheduling — the practice of changing the routes and timings of trains in real-time to respond to delays — can help to reduce the impact of reactionary delay. There are a number of existing optimisation models that can be used to determine the best way to reschedule the timetable in any given traffic scenario. However, many of these models do not adequately account for the acceleration and deceleration required for trains to achieve the rescheduled timetable. The few models that do account for this are overly complex and cannot be solved to optimality in sufficiently short times. In this study, we propose a new model for train timetable rescheduling that uses statistical methods and historical data to parsimoniously take train speed into account. The model is tested using a new set of instances based on real data from Derby station in the UK. We show that the improved accuracy of the proposed model comes with little to no trade-off in terms of run time compared to fixed-speed timetable rescheduling models.

Design of Active Tyre-Suspension-Seat System Through Multibody Model and Genetic Algorithms

The tyre-suspension-seat dynamic system, driveline and engine vibrations are generally considered in the vibrational field as the main factors that influence the particular feeling of comfort perceived by passengers on a vehicle. Hence, the development of several criteria and models for the optimal estimation of the design parameters of such systems. Among these parameters, the most detrimental impacting on the passenger comfort are undoubtedly acceleration and its variation. The two types of suspension systems (conventional passive suspension system and active suspension system) differ as the first foresees the spring-damper characteristics to be adjusted so that only one of several conflicting objectives (such as passenger comfort, road holding, and suspension deflection) is followed. In active suspension systems, instead, these objectives are balanced by the designer in a more efficient manner thanks to the feedback-controller actuator assembly. However, this approach presents some limitations linked to the extremely wide spectrum of magnitude and frequency of external forces that the tyre-suspension-seat system has to efficiently control and mitigate. It remains that in the existing optimisation models and systems time exposure limits established by unification agencies and road authorities are not generally considered. This paper illustrates the development of an active tyre-suspension-seat system control for passenger cars, using both a non-linear multibody model and Genetic Algorithm (GA) controls. A benefit of the proposed active tyre-suspension-seat system control is also to consider various time exposure limits and an active damping element. The main innovative element introduced by this work consists in having coupled an active control to passive mechanical parameters in order to minimize the seat acceleration. The 3 DoF multibody model, applied to a quarter body for symmetry reasons, treated road roughness as an input variable in the GA control so as to determine the vertical component of acceleration. The numerical and experimental applications of the proposed model to a specific case study allowed to validate the effectiveness of the active system towards the vibrations transmitted to the passenger.

Robust Optimization Model for Single Line Dynamic Bus Dispatching

For effective bus operations, it is important to flexibly arrange the departure times of buses at the first station according to real-time passenger flows and traffic conditions. In dynamic bus dispatching research, existing optimization models are usually based on the prediction and simulation of passenger flow data. The bus departure schemes are formulated accordingly, and the passenger arrival rate uncertainty must be considered. Robust optimization is a common and effective method to handle such uncertainty problems. This paper introduces a robust optimization method for single-line dynamic bus scheduling. By setting three scenarios—the benchmark passenger flow, high passenger flow, and low passenger flow—the robust optimization model of dynamic bus departures is established with consideration of different passenger arrival rates in different scenarios. A genetic algorithm (GA) is improved for minimizing the total passenger waiting time. The results obtained by the proposed optimization method are compared with those from a stochastic programming method. The standard deviation of the relative regret value with stochastic optimization is 5.42%, whereas that of the relative regret value with robust optimization is 0.62%. The stability of robust optimization is better, and the fluctuation degree is greatly reduced.

A Robust Short-Term Oil Production under a Bow-Tie Uncertainty Set for the Gas Lift Performance Curve

SummaryThe method of continuous gas lift has been commonly used in the oil industry to enhance production. Existing optimization models consider an approximate performance curve anchored by production test data, often disregarding reservoir uncertainty. We propose a robust optimization model that jointly considers the most recent data and an uncertainty set for the reservoir pressure, a critical parameter that is usually not measured precisely. As a result, we obtain what we call a “bow-tie” uncertainty set for the performance curves, in which the performance uncertainty increases when we move away from the production test’s operational point. We test our model with real data from an offshore oil platform and compare it against a fully deterministic model. We show superior out-of-sample performance for the robust model under different probability distributions of the reservoir pressure.

A New Method for Optimization of Water Distribution Networks While Considering Accidents

Optimization of water distribution networks can effectively reduce their annual cost, which includes the average investment for each year the operational costs and depreciation costs. However, the existing optimization models rarely directly consider the basic flow of each node in case of accidents, such as pipe bursts. Therefore, it is necessary to check the flow requirements under accident conditions. In order to deal with these drawbacks, two optimization models are established considering accident conditions: a single-objective optimization model considering annual cost as an economic objective, and a multi-objective optimization model with a reliability objective defined by the surplus water head. These models are solved based on the genetic algorithm, non-dominated sorting genetic algorithm-II algorithm and Levenberg-Marquardt iterative method. Applying two cases of a single pump station and a multi pump water station water supply, it is shown that the annual cost when considering the accident conditions is higher than that without considering the accident conditions. Moreover, the annual cost obtained with the multi-objective optimization model is slightly higher than that obtained with the single-objective optimization model. The cost is higher because the former model reduces the average surplus water head, which can improve the water distribution network reliability. Therefore, the model and optimization algorithm proposed in this paper can provide a general and fast optimization tool for water distribution network optimization.

Methodology to Improve Services in Small IT Centers: Application to Educational Centers

Educational centers (schools, academies, high schools, etc.) are usually small companies, which make them special in terms of management. The management of IT services is far from standard and based in home solutions. The disadvantage of this approach is clear, as it happened during the COVID-19 pandemic period. The solution to properly managing IT services is based on the use of the ITIL (Information Technology Infrastructure Library). The question is how to apply this standard that only defines the processes to implement, but does not describe the way or the order to implement them. In this article it is shown which IT processes are really needed in any educational center and the order in which they should be implemented. The method used consists of fulfilling a knowledge database with extensive information from schools, academies, and other educational centers. After that, an existing optimization model is adopted and a representative learning center is defined, which is used to propose the IT processes sequence; finally, a set of optimal IT processes and the order to implement them is defined. These ordered processes optimize the quality of IT for learning services. The main result is an ordered set of IT processes that best fit the needs of IT departments in small educational centers.

Retracted] Low‐Latency and High‐Concurrency 5G Wireless Sensor Network Assists Innovation in Ideological and Political Education in Colleges and Universities

This paper first analyzes the existing optimization models and methods based on low‐latency and high‐concurrency 5G wireless sensor networks and focuses on some research results based on the physical layer, data link layer, and network layer. After that, the energy‐constrained cross‐layer optimization algorithm of the joint physical layer and network layer and the cross‐layer optimization algorithm based on reliable transmission are analyzed and compared. According to the ideological and political education cycle and network connectivity of wireless sensor networks, the existing definition of the ideological and political education cycle of wireless sensor networks was questioned and its irrationality was demonstrated. Firstly, we introduce the system model, energy capture model, and energy consumption model of this scheme; then, calculate the energy consumption of the node sending and receiving data and analyze the maximum throughput that the node can provide for data transmission to perform mathematical modeling. In a balanced ideological and political education cycle, the optimal solution is given. Finally, it introduces distributed high‐throughput routing for EH‐WSNs based on low latency and high concurrency and gives a performance comparison with the shortest path routing scheme. The energy consumption of each part of the node is analyzed in a comprehensive and in‐depth manner, and the direction of the energy optimization strategy of the network node and its sensor network is pointed out. On this basis, this article proposes a new definition of the network ideological and political education cycle: the ideological and political education cycle of the wireless sensor network is equal to the ideological and political education cycle of the first joint in the network that fails due to energy exhaustion. Based on this definition, the wireless sensor network is analyzed, and the cross‐layer (physical layer, data link layer, and network layer) optimization mathematical model of the wireless sensor network ideological and political education cycle is established. The two data collection schemes proposed in this paper are simulated under the conditions of no‐delay ideological and political education cycle and different time‐delayed ideological and political education cycles. The simulation results show that the model is better than both in terms of network life and data integrity.

A novel discrete evidence fusion approach by considering the consistency of belief structures

Fusion of discrete belief structures gets much attention owing to the widespread existence of discrete information in decision analysis, expert system and other fields. Recently, one method that combines discrete evidence was proposed by establishing an optimization model. However, the existing optimization model of discrete belief structure has a problem of high computational complexity since it needs to calculate all the cases of deterministic evidence combination to obtain maximum and minimum values of each focal element in combination result. In this paper, a novel method of discrete evidence fusion is proposed to reduce the computational complexity by considering the consistency of belief structures of evidence groups and finding the most consistent and most inconsistent cases. Example and application are given to illustrate the effectiveness and rationality of the proposed method.