Dynamic Optimization Framework Research Articles

A Framework for the Synthesis of Optimum Operating Profiles Based on Dynamic Simulation and a Micro Genetic Algorithm

This paper presents an approach to managing the thermal power plant’s flexible operation based on the steam generation process optimization. A strategy at the process level, as a first step in the operational optimization of the entire power plant, is proposed. The proposed approach focuses on minimizing the drum boiler startup time, since it is considered the most critical element in the steam generation process and in the thermal power plant’s efficient operation. An approach that addresses the problem to find the optimal sequences of control valves that minimize the drum boiler startup time as a dynamic optimization problem is proposed. To solve the optimization problem, a dynamic optimization framework based on a micro genetic algorithm (mGA) coupled with a dynamic simulation model is implemented. The dynamic simulation model is validated against data available in the literature, and the proposed optimization algorithm is characterized by the use of variable length chromosomes and the use of small population sizes. The results show that optimized operating profiles minimize the drum boiler startup time by at least 35 percent and generate control valve operating sequences that must be carried out to achieve the desired profile, while the structural integrity constraints are fulfilled at all times.

A Tchebycheff-based multi-objective combined with a PSO–SQP dynamic real-time optimization framework for cycling energy systems

The aim in this work is to develop an optimization framework to address cycling of baseload energy systems due to the penetration of renewables into the grid. The developed strategy corresponds to a multi-objective and dynamic real-time optimization (MOO-DRTO) framework applied to a postcombustion MEA-based CO2 capture process from a baseload coal-fired power plant under cycling conditions. A Tchebycheff-based method is used for the multi-objective optimization (MOO) component. Also, a hybrid approach consisting of Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) is implemented for the first time in process systems engineering to solve the dynamic real-time optimization (DRTO) component. The objectives considered in the MOO-DRTO framework are economic and environmental. The proposed MOO-DRTO strategy is successfully implemented and 24-h optimal output trajectories for the carbon capture system under cycling are generated. Also, the optimal compromise is chosen from the Pareto front according to a set of selected weights for the objectives with minimal interaction between the framework and decision maker. The results indicated that the developed framework has potential to be extended to plant-wide optimization applications under cycling.

Control of Lead Contamination in Water Distribution Networks: A Dynamic Optimization Framework

This study investigates the optimization of free chlorine-based disinfectant dosage for controlling the dissolution of lead (Pb) while limiting disinfection by-products (DBPs) formation in water distribution networks. A dynamic optimization framework is developed in this study to account for the impact of spatial and temporal variations of network hydraulics and water chemistry on dissolved lead concentrations. Accordingly, a dynamic schedule of disinfectant dosage is proposed to minimize water lead levels and two-point chlorine residual violations in the network. The results suggest that optimization of water chemistry parameters such as pH and orthophosphate dosage provide an additional degree of freedom to be explored in the problem of optimizing disinfectant dosage for compliance with conflicting mandates regulating lead levels and DBPs concentration in drinking water.

A Fourier-based control vector parameterization for the optimization of nonlinear dynamic processes with a finite terminal time

In this paper, a novel strategy for finding the optimal operation profiles for nonlinear dynamic processes is developed. Based on the direct sequential stochastic framework for dynamic optimization, this work proposes a technique based on Fourier series for the control vector parameterization, as an alternative to the traditional methods. This approach has the advantage of choosing a high degree of smoothness to avoid sharp changes for the input variables, which is preferred in most chemical and biological processes. On the other hand, when several arcs are present in the qualitative optimal profile, the number of parameters can be increased for a better approximation. The proposed strategy was applied to four well-studied nonlinear processes, covering batch and fed-batch reactors, and multi-input systems. The algorithm was tested through simulations. Good performances were obtained in comparison to some previous results available in the literature.

Mountain-to-sea ecological-resource management: Forested watersheds, coastal aquifers, and groundwater dependent ecosystems

Improving the understanding of connections spanning from mountain to sea and integrating those connections into decision models have been increasingly recognized as key to effective coastal resource management. In this paper, we aim to improve our understanding of the relative importance of linkages between a forested watershed, a coastal groundwater aquifer, and a nearshore marine groundwater-dependent ecosystem (GDE) using a dynamic groundwater optimization framework and simple ecosystem equations. Data from the Kīholo aquifer on the Kona Coast of Hawai‘i Island are used to numerically illustrate optimal joint management strategies and test the sensitivity of those strategies to variations in physical and behavioral parameter values. We find that for a plausible range of watershed management costs, protecting part of the recharge capture area is always optimal. Without watershed protection, maintaining a safe minimum standard growth rate for a GDE-dependent marine indicator species, reduces net present value non-trivially, but optimal investment in watershed conservation offsets that potential reduction by 75 %. In general, we find that optimal watershed management and groundwater pumping are most sensitive to changes in water demand growth and parameters that describe nearshore salinity.

Improving the Resilience of Postdisaster Water Distribution Systems Using Dynamic Optimization Framework

Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step toward sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events, and in an organized manner, to prioritize the use of available resources to restore service rapidly while minimizing the negative impacts. Many methods have been developed to evaluate the WDS resilience, but few efforts are made so far to improve the resilience of a postdisaster WDS through identifying optimal sequencing of recovery actions. To address this gap, the authors propose a new dynamic optimization framework in this study in which the resilience of a postdisaster WDS is evaluated using six different metrics. A tailored genetic algorithm is developed to solve the complex optimization problem driven by these metrics. The proposed framework is demonstrated using a real-world WDS with 6,064 pipes. Results obtained show that the proposed framework successfully identifies near-optimal sequencing of recovery actions for this complex WDS. The gained insights, conditional on the specific attributes of the case study, include the following: (1) the near-optimal sequencing of a recovery strategy heavily depends on the damage properties of the WDS; (2) replacements of damaged elements tend to be scheduled at the intermediate-late stages of the recovery process due to their long operation time; and (3) interventions to damaged pipe elements near critical facilities (e.g., hospitals) should not be necessarily the first priority to recover due to complex hydraulic interactions within the WDS.

OCD: Online Crowdsourced Delivery for On-Demand Food

Online-to-offline (O2O) commerce connecting service providers and individuals to address daily human needs is quickly expanding. In particular, on-demand food, whereby food orders are placed online by customers and delivered by couriers, is becoming popular. This novel urban food application requires highly efficient and scalable real-time delivery services. However, it is difficult to recruit enough couriers and route them to facilitate such food ordering systems. This paper presents an online crowdsourced delivery (OCD) approach for on-demand food. Facilitated by Internet-of-Things and 3G/4G/5G technologies, public riders can be attracted to act as crowdsourced workers delivering food by means of shared bicycles or electric motorbikes. An online dynamic optimization framework comprising order collection, solution generation, and sequential delivery processes is presented. A hybrid metaheuristic solution process integrating the adaptive large neighborhood search and tabu search approaches is developed to assign food delivery tasks and generate high-quality delivery routes in a real-time manner. The crowdsourced riders are dynamically shared among different food providers. Simulated small-scale and real-world large-scale on-demand food delivery instances are used to evaluate the performance of the proposed approach. The results indicate that the presented crowdsourced food delivery approach outperforms traditional urban logistics. The developed hybrid optimization mechanism is able to produce high-quality crowdsourced delivery routes in less than 120 s. The results demonstrate that the presented OCD approach can facilitate city-scale on-demand food delivery.

Assessing the benefits of capacity payment, feed‐in‐tariff and time‐of‐use programme on long‐term renewable energy sources integration

Recently, demand response programmes (DRPs) have captured great attention in electric power systems. DRPs such as time-of-use (ToU) programme can be efficiently employed in the power system planning to reform the long-term behaviour of the load demands. The composite generation expansion planning (GEP) and transmission expansion planning (TEP) known as composite GEP–TEP is of high significance in power systems to meet the future load demand of the system and also integrate renewable energy sources (RESs). In this regard, this study presents a dynamic optimisation framework for the composite GEP–TEP problem taking into consideration the ToU programme and also, the incentive-based and supportive programmes. Accordingly, the performances of the capacity payment and feed-in tariff mechanisms and the ToU programme in integrating RESs and reducing the total cost have been evaluated in this study. The problem has been formulated and solved as a standard two-stage mixed-integer linear programming model aimed at minimising the total costs. In this model, the ToU programme is applied and the results are fed into the expansion planning problem as the input. The proposed framework is simulated on the IEEE Reliability Test System to verify the effectiveness of the model and discuss the results obtained from implementing the mentioned mechanisms to support the RESs integration.

Unified Provisioning Framework for Dynamic Virtual Machine Placement Optimization in Cloud Data Center

Current techniques for Virtual machine placement in cloud data center is avoiding multiple resources due to its complexity and hardness of the problem. Due to this each Virtual Machine Placement increased overall frequency of server consolidation and migration. In this paper, we have overcome these limitations by providing local search-based unified approximation framework which utilized multiple resources of server and reduced the frequency of server consolidation and migration. The framework is evaluated on Azure cloud data center benchmark data sets and it has surpassed existing methods with improvement by 32% in overall virtual machine placement.

SDN-enabled distributed open exchange: Dynamic QoS-path optimization in multi-operator services

We propose an SDN-enabled distributed open exchange framework for dynamic optimization of end-to-end (E2E) quality of service (QoS) paths over multi-operator networks, which enables individual network service providers (NSP) to offer inter-operator services with E2E QoS guarantees while allowing each NSP to manage their own network resources. Different from the state of the art, there are no physical Internet exchange points in the proposed framework and NSPs are assumed to have direct inter-connections. In the proposed SDN-enabled “virtual” open exchange, the SDN controllers of all NSPs directly communicate with each other to advertise a set of QoS-enabled paths across their own network with ask prices, and each NSPs dynamically selects the best E2E price-performance path for its customers by bidding on a subset of these advertised paths, and form E2E paths by concatenating them. The main contributions of this paper are a distributed open exchange framework and a distributed procedure for E2E path computation by collaboration between SDN controllers of multiple NSPs, which is coordinated by the SDN controller of the NSP in which the managed service request originates. The proposed procedure has been verified over a multi-operator SDN environment, emulated by using the Mininet, which includes a controller-to-controller messaging protocol for provisioning multi-operator video services with E2E QoS.

A global climate-economy model including the REDD option

Compared to conventional abatement measures, Reducing Emissions from Deforestation and Forest Degradation (REDD) offers attractive cost savings while tackling the climate change problem. However, there exist challenges associated with the selection of the optimal level of REDD-based abatement given the risks and non-uniform costs of their implementation across countries. This paper develops an integrated assessment model of carbon mitigation, incorporating the REDD option. Using a dynamic optimization framework, it derives the optimal timing and level of REDD participation for key countries with REDD potential based on their opportunity costs and risks. Specifically, Brazil, Indonesia, the Democratic Republic of Congo, Cameroon and Papua New Guinea, are chosen for inclusion under the REDD-based abatement option. Together, these five countries account for roughly 20 percent of global forest area and 40 percent of current global deforestation. The relevance and contribution of REDD-based abatement is explored under the possibility of non-linear damages resulting from increasing concentrations of greenhouse gases. Results indicate that the REDD programme is an attractive option to consider despite the associated risks of impermanence. Including the REDD option, in fact, also increases conventional abatement efforts because low costs of REDD reduce the overall abatement costs, thereby making it optimal to abate more. Further, use of REDD option helps stabilise the atmospheric carbon stock in the long term. Without REDD, atmospheric carbon concentrations would be higher by 800 billion tonnes in the next 300 years. Whereas, optimal implementation of REDD in just five countries would help avoid the release of about 80 billion tonnes of carbon in the next 50 years.

Climate Policy Integration in the Land Use System with Ecological Thresholds

The land use sector is an area with high potential to pursue mitigation and adaptation goals alike. However, due to the complexity derived from managing landscapes with multiple objectives and the lack of tools to assess the outcome, this potential is presumably subtilized in practice. In order to contribute to fill in this knowledge gap, this paper analyzes climate policy integration –the joint implementation of mitigation and adaptation measures- in the presence ecological thresholds. Based on a hypothetical yet realistic economic-ecologic system, the synergic properties of different isolated and integrated policy configurations were analyzed using a dynamic optimization framework and simulation tools. The results indicate that, regardless of specific circumstances (e.g. observing or not a regime shift), the configuration that better compiled with the definition of a synergy corresponded to a cross sectorial approach: an intervention involving coordination between agriculture and forestry. This result suggests that harmonization among the elements that compose the land use sector is the main source of an enhanced policy outcome. Thus, effective integration requires looking at the land use sector as an entity (e.g. a landscape) rather than isolated components (e.g. agriculture and forestry sectors).

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Dynamic interval multiobjective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms (EAs) that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multiobjective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two subpopulations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances as well as a multiperiod portfolio selection problem and compared with five state-of-the-art EAs. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances.

Real-Time Demand Side Management for a Microgrid Considering Uncertainties

Different from most existing studies that focus on offline demand side management (DSM) in microgrids (MGs) while neglecting forecasting errors of uncertain renewable generations, this paper studies online DSM. A two-stage real-time DSM method for an MG including different time scales, integrated with schedulable ability (SA) and uncertainties, is proposed. In the first stage, a novel internal pricing model is developed. On this basis, a model predictive control-based dynamic optimization is applied to minimize the operation cost and maintain the power balance considering the uncertainties imposed by both supply and demand sides in the MG system. In the second stage, we define the concept of SA for response executors (REs) and also establish an SA evaluation system taking the real-time and history information of the REs into account. In doing so, a faster-time scale online power allocation among REs is carried out in the framework of dynamic optimization to further compensate for the uncertainties in real-time, based on the evaluated SA values of the REs and the required compensation power. Numerical simulations on a residential MG show the reasonableness and effectiveness of the proposed method.

Surrogate-Assisted Evolutionary Framework for Data-Driven Dynamic Optimization

Recently, dynamic optimization has received much attention from the swarm and evolutionary computation community. However, few studies have investigated data-driven evolutionary dynamic optimization, and most algorithms for evolutionary dynamic optimization are based on analytical mathematical functions. In this paper, we investigate data-driven evolutionary dynamic optimization. First, we develop a surrogate-assisted evolutionary framework for solving data-driven dynamic optimization problems (DD-DOPs). Second, we employ a benchmark based on the typical dynamic optimization problems set in order to verify the performance of the proposed framework. The experimental results demonstrate that the proposed framework is effective for solving DD-DOPs.

DSEOM: A Framework for Dynamic Security Evaluation and Optimization of MTD in Container-based Cloud

Due to the lightweight features, the combination of container technology and microservice architecture makes container-based cloud environment more efficient and agile than VM-based cloud environment. However, it also greatly amplifies the dynamism and complexity of the cloud environment and increases the uncertainty of security issues in the system concurrently. In this case, the effectiveness of defense mechanisms with fixed strategies would fluctuate as the updates occur in cloud environment. We refer this problem as effectiveness drift problem of defense mechanisms, which is particularly acute in the proactive defense mechanisms, such as moving target defense (MTD). To tackle this problem, we present DSEOM, a framework that can automatically perceive updates of container-based cloud environment, rapidly evaluate the effectiveness change of MTD and dynamically optimize MTD strategies. Specifically, we establish a multi-dimensional attack graphs model to formalize various complex attack scenarios. Combining with this model, we introduce the concept of betweenness centrality to effectively evaluate and optimize the implementation strategies of MTD. In addition, we present a series of security and performance metrics to quantify the effectiveness of MTD strategies in DSEOM. And we conduct extensive experiments to illustrate the existence of the effectiveness drift problem and demonstrate the usability and scalability of DSEOM.

Cloud resource orchestration optimisation based on ARIMA

The problem of resources management in the cloud environment, focusing on the platform as a service (PaaS), satisfying the demands of users, and relieving the load on the server in high concurrency, requires attention. After analysing the resource orchestration technology on PaaS, this paper presents a dynamic orchestration optimisation framework based on autoregressive integrated moving average model (ARIMA) model. The architecture is based on an OpenStack infrastructure as a service (IaaS), and combines with Cloudify, a resource orchestration software on the PaaS. Adjustments may be made in advance by predicting the resource consumption in the next time period. Experiments showed that this architecture can effectively shorten the concurrent response time and improve the utilisation of memory.

Cloud resource orchestration optimisation based on ARIMA

The problem of resources management in the cloud environment, focusing on the platform as a service (PaaS), satisfying the demands of users, and relieving the load on the server in high concurrency, requires attention. After analysing the resource orchestration technology on PaaS, this paper presents a dynamic orchestration optimisation framework based on autoregressive integrated moving average model (ARIMA) model. The architecture is based on an OpenStack infrastructure as a service (IaaS), and combines with Cloudify, a resource orchestration software on the PaaS. Adjustments may be made in advance by predicting the resource consumption in the next time period. Experiments showed that this architecture can effectively shorten the concurrent response time and improve the utilisation of memory.

Assessment of the role of cognitive control on monetary reward and loss anticipation: An electrophysiological study in subjects with schizophrenia

In this paper, we present a systematic robust dynamic optimization framework applied to the benzaldehyde lyase-catalyzed carboligation of propanal and benzaldehyde to produce (R)-2-hydroxy-1-phenylbutan-1-one (BA). First, the elementary process functions approach was used to screen between different dosing concepts, and it was found that simultaneously dosing propanal and benzaldehyde leads to the highest final concentration of BA. Next, we applied global sensitivity analysis and found that 10 out of 13 kinetic parameters are relevant. Time-varying back-offs were then used to handle parametric uncertainties due to these 10 parameters. A major contribution in our work is the use of the point estimate method instead of Monte Carlo simulations to calculate the back-offs in an efficient and reproducible manner. We show that this new approach is at least 10 times faster than the conventional Monte Carlo approach while achieving low approximation errors.

Robust dynamic optimization of enzyme-catalyzed carboligation: A point estimate-based back-off approach

In this paper, we present a systematic robust dynamic optimization framework applied to the benzaldehyde lyase-catalyzed carboligation of propanal and benzaldehyde to produce (R)-2-hydroxy-1-phenylbutan-1-one (BA). First, the elementary process functions approach was used to screen between different dosing concepts, and it was found that simultaneously dosing propanal and benzaldehyde leads to the highest final concentration of BA. Next, we applied global sensitivity analysis and found that 10 out of 13 kinetic parameters are relevant. Time-varying back-offs were then used to handle parametric uncertainties due to these 10 parameters. A major contribution in our work is the use of the point estimate method instead of Monte Carlo simulations to calculate the back-offs in an efficient and reproducible manner. We show that this new approach is at least 10 times faster than the conventional Monte Carlo approach while achieving low approximation errors.