Two-stage Multi-objective Optimization Research Articles

A holistic two-stage decision-making methodology for passive and active building design strategies under uncertainty

In the last decade, many studies focused on finding optimal design solutions considering trade-offs between different aspects of building design. Accordingly, multi-objective optimization (MOO) approaches have been increasingly applied in the building industry. However, certain aspects must be deepened to ensure a more effective decision-making process in the early planning phase. On the one hand, uncertainties should be considered before making decisions to ensure the robustness of the optimal solutions; on the other hand, decisions are made at different times in building planning, and the sequential order of making decisions should be modeled. This paper presents a holistic two-stage multi-objective stochastic optimization (MOSO)-II framework to minimize the environmental impact of global warming potential (GWP) and cost throughout the entire life cycle of a building (phases of A1-A3, B4, B6 and C3-C4), considering passive and active design strategies in two consecutive stages, under uncertainty. Herein, individual/use and political/market uncertainties are considered. As a proof of concept, the proposed framework is applied in a case study for a generic zone in a multi-family terraced residential building type with solid brick construction. The advantages of the proposed framework are validated by comparing it with alternative single-stage MOSO frameworks. Results show that the proposed two-stage MOSO-II framework can deliver a smaller range of solutions with a better performance in terms of lower GWP and cost. It indicates that the proposed framework can effectively assist planners in decision-making by reducing the effort in choosing the proper solution. Secondly, the results also emphasize the importance of passive design strategies in sustainable building planning. In addition, the energy mix structure and cost of energy sources should be carefully adjusted in the future to promote a more ecologically sustainable building design.

A New Gradient-Accelerated Two-Stage Multiobjective Optimization Method for CO2-Alternating-Water Injection in an Oil Reservoir

Summary CO2-water-alternating-gas (CO2-WAG) is a reservoir development method that can simultaneously enhance oil recovery and achieve CO2 storage. However, improperly designed parameters for CO2 injection and oil production may significantly reduce the oil displacement efficiency and CO2 storage. Furthermore, optimizing the injection parameters is computationally expensive due to the high computational cost of the compositional simulation. This work aims to propose an efficient optimization method to obtain a series of well-control schemes that balance maximizing net present value (NPV) and CO2 storage for decision-makers. Given the number of CO2-WAG cycles and the duration of each cycle, we optimize the water injection rate, gas injection rate, and half-cycle for the injection well and the bottomhole pressure (BHP) for the production well. In this paper, a two-stage optimization strategy is proposed to enhance the optimization efficiency. The first stage performs the surrogate-assisted single-objective optimizations of each considered objective. It is designed to find the endpoints of the Pareto front that connect all solutions of the multiobjective optimization; this stage not only provides important search directions for the subsequent multiobjective optimization but also improves the accuracy of the surrogate model near the Pareto front. The second stage is the surrogate-assisted multiobjective optimization, which aims to find all the solutions along the Pareto front based on the Pareto endpoints obtained from the first stage. In addition, this study successfully combines the gradient of the objective functions with the meta-heuristic algorithm during the multiobjective optimization, which ensures a faster convergence to the global optimum. The proposed multiobjective optimization algorithm shows faster convergence than the conventional optimization methods when applied to the three multiobjective optimization test functions. Finally, a comparison with the conventional multiobjective optimization is conducted based on one test function and two benchmark reservoir simulation models to verify the correctness and efficiency of the proposed method. It is confirmed that the proposed method outperforms the conventional ones for the optimization of CO2-WAG injection.

Two-stage multi-objective optimization for ICU bed allocation under multiple sources of uncertainty

Due to the impact of COVID-19, a significant influx of emergency patients inundated the intensive care unit (ICU), and as a result, the treatment of elective patients was postponed or even cancelled. This paper studies ICU bed allocation for three categories of patients (emergency, elective, and current ICU patients). A two-stage model and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to obtain ICU bed allocation. In the first stage, bed allocation is examined under uncertainties regarding the number of emergency patients and their length of stay (LOS). In the second stage, in addition to including the emergency patients with uncertainties in the first stage, it also considers uncertainty in the LOS of elective and current ICU patients. The two-stage model aims to minimize the number of required ICU beds and maximize resource utilization while ensuring the admission of the maximum number of patients. To evaluate the effectiveness of the model and algorithm, the improved NSGA-II was compared with two other methods: multi-objective simulated annealing (MOSA) and multi-objective Tabu search (MOTS). Drawing on data from real cases at a hospital in Lyon, France, the NSGA-II, while catering to patient requirements, saves 9.8% and 5.1% of ICU beds compared to MOSA and MOTS. In five different scenarios, comparing these two algorithms, NSGA-II achieved average improvements of 0%, 49%, 11.4%, 9.5%, and 17.1% across the five objectives.

Two-stage multi-objective optimization of reinforced concrete buildings based on non-dominated sorting genetic algorithm (NSGA-III)

The colossal environmental footprint of the construction industry is a paramount ecological concern, especially in the wake of climate change threats and natural resources depletion. The present study performs a novel two-stage optimization approach for the design of reinforced concrete (RC) frame buildings to minimize the water footprint (WF) and cost, based on non-dominated sorting genetic algorithm (NSGA-III). The first stage includes multi-objective optimization (MOO) with respect to the cost and capacity of cross-sections of RC beam and column elements for any possible combination of design variables using section-based database schema (SBDBS). The second stage is MOO based on NSGA-III to minimize the volumes of reinforcing steel and concrete in RC structure considering the optimal sections generated by SBDBS as a pre-determined list for elements. Four structural archetypes consisting of 4, 8, 12, and 16-story frames were considered in the design optimization problems. The main objective of this optimization is to calculate the cost and WF of RC structures for various unit cost and WF of materials, and as a result, identify the optimal design of RC structures. The result of this optimization framework allows the decision maker to identify the optimal RC structural design for any cost or WF condition by performing a sensitivity analysis, without need for additional structural analysis or optimization. The proposed approach further allows the decision maker to consider the uncertainty of WF and cost of materials that emanate from regional or spatiotemporal variables. The results show that the shape of the optimal design Pareto front (PF) is more related to the values of unit WF of materials than the costs. To highlight the advantages of the proposed procedure, probabilistic sensitivity analysis and decision-making strategy were performed and pertinent conclusions were drawn.

Two-stage Multi-objective Optimization Coordination of Electro-thermal Coupled Integrated Energy System Based on Improved NSGA-II Algorithm

With the growing proportion of clean energy in integrated energy systems (IES), energy supply uncertainty and spatial-temporal dispersion are becoming increasingly prevalent. System modeling and optimal scheduling are facing greater challenges. In this paper, we improve the non-dominated sorting genetic algorithm (NSGA-II) to address the above problems and propose a two-stage multi-objective benefit-equilibrium optimization coordination of the electric-thermal coupled integrated energy system. Firstly, this paper carries out the thermodynamic characteristics analysis of the equipment components of the electro-thermal coupled energy system, which reflects the structural features of the system, the performance of each equipment under different task conditions, and the mechanism of the system; based on the above characteristic analysis, a two-stage multi-objective optimization of electro-thermal coupled system optimization coordination is proposed to establish the objective function and carry out each objective balance constraint; the NSGA-II algorithm is as well as improved. According to the operation stage, operation generation and the NSGA-II algorithm are improved by dynamically adjusting the operating parameters of evolving individuals of the operation stage, operational generation, and the number of undominated individuals in the current temporary population. By making the algorithm adaptation to improve the adaptive capacity of the evolution operator, we solve the two-step model and obtain the Pareto optimal front for each energy device. In summary, the results of the analysis of the IES under the coupling of power system and thermal system show that the constructed model and the proposed algorithm can effectively improve the accuracy of the renewable energy system and the optimization decision. The results of the research further reflect the benefits of the proposed multi-objective optimization scheme in accounting for economic, renewable energy, and complex operating constraints which ensure the economical and stable operation of the system, as well as the robustness of optimal scheduling.

Reliable operation of an optimal self-scheduling multicarrier system with two alternatives: Multiple renewable utilities or hydrogen facilities

Multicarrier systems (MCSs) have been retrofitted with storage facilities to enhance their resilience to meet the fluctuating demands. Considering the complexity of the systems and the interactions among various carriers, optimal resource allocation is a serious challenge in the sustainable management of energy systems. This study proposes a new self-scheduling MCS to supply power, heating, cooling, and freshwater, without external cooling and nonrenewable heating facilities. Modeled based on circular integration principle, the MCS consists of various subsystems and storage facilities to treat the effluent and digest excess sludge of a wastewater treatment facility. The MCS is optimally designed and scheduled in a two-stage multi-objective optimization to satisfy the demand loads of a small community in two configurations: one with a material conversion unit (MCS-MC) to store and utilize hydrogen, and the other with weather-driven renewable energy units (MCS-RE) for a reliable operation. Accordingly, total annual cost (TAC), total exergy dissipation (nTED), and the mismatch between supply and demand (MSD) are minimized in a multi-objective optimization problem to obtain the optimal capacity of the equipment and the short-term storage facilities are scheduled in the first and second stages, respectively. The results show that the optimal MCS-MC and MCS-RE configurations reduced the TAC, nTED, and MSD by 30.2% and 12.4%, 32.9% and 23.4%, and 55.4% and 48.5%, respectively, compared to the base system. The new utility-free hydrogen-assisted multi-carrier systems are anticipated to substitute present systems with multiple renewable resources.

A novel two-stage multi-objective optimization model for sustainable soybean supply chain design under uncertainty

Soybean is one of the most important agricultural products in international markets due to its high global consumption. However, there are limited studies considering sustainability issues and global economic aspects of the soybean supply chain design and optimization. Moreover, it is challenging to integrate many sustainability criteria into a single-stage optimization model for a soybean supply chain configuration. This research proposes a novel two-stage approach, integrating a multi-criteria decision-making technique and a new multi-objective optimization model to design and optimize a soybean supply chain network in Canada, considering sustainability and global factors. In Stage 1, several qualitative and quantitative sustainability criteria are considered to calculate the sustainability scores for the potential suppliers, using the Interval Type 2 Trapezoidal Best Worst Method. Then, the scores are used as input in Stage 2, where a new optimization model with four objective functions is formulated to design and optimize a soybean supply chain. The objective functions include maximization of total profit, created job opportunities, and suppliers' sustainability, and minimization of CO2 emissions. Then, the Pareto frontier is generated, using the augmented ε-constraint method which helps policymakers to make appropriate decisions. Furthermore, four cases are proposed and analyzed to assess the impacts of the objective functions on strategic and tactical decisions. The results show the importance of the presented method as an integrated approach, considering sustainability pillars in agri-food supply chains. In addition, the results indicate that the proposed stochastic multi-objective model can handle fluctuations in uncertain parameters.

A Practical Approach to Select Representative Deterministic Models Using Multiobjective Optimization from an Integrated Uncertainty Quantification Workflow

Summary Selecting a set of deterministic (e.g., P10, P50, and P90) models is an important and difficult step in any uncertainty quantification workflow. In this paper, we propose to use multiobjective optimization to find a reasonable balance between the often conflicting features that must be captured by these models. We embed this approach into a streamlined uncertainty quantification workflow that seamlessly integrates multirealization history matching, production forecasting with uncertainty ranges, and representative deterministic model selection. Some uncertain parameters strongly impact simulated responses representing historic (production) data and are selected as active parameters for history matching, whereas others are important only for forecasting. An ensemble of conditional realizations of active history-matching parameters is generated in the multirealization history-matching stage using a distributed optimizer that is integrated with either a randomized maximum likelihood (RML) or a Gaussian mixture model (GMM). This ensemble is extended with unconditional realizations of forecast parameters generated by sampling from their prior distribution. Next, the petroleum engineer must select primary and secondary key performance indicators and identify models from this ensemble that optimally generate P10, P50, and P90 values for these indicators. In addition to matching target values of these key performance indicators (e.g., cumulative oil/gas/water production and recovery factor), selected representative models (RMs) typically must satisfy regulatory or management-imposed requirements or constraints (e.g., the value of some key parameters must be within a user-specified tight range). It can be quite difficult to find a set of RMs that satisfy all requirements. Even more challenging, some requirements may conflict with others, such that no single model can satisfy all requirements. To overcome these technical difficulties, we propose in this paper to formulate different requirements and constraints as objectives and develop a novel two-stage multiobjective optimization strategy to find a set of Pareto optimal solutions based on the concept of dominance. In the first stage, we propose selecting P10, P50, and P90 candidates by minimizing the indicator mismatch function and constraints violation function. In the second stage, we propose selecting combinations of P10, P50, and P90 candidates from the previously generated posterior ensemble, obtained in the first stage by optimizing other objectives. One or more sets of RMs can then be selected from the set of optimal solutions according to case-dependent preferences or requirements. Because the number of P10, P50, and P90 candidates selected in the first stage is much smaller than the number of all samples, the proposed two-stage approach performs much more efficiently than directly applying the traditional multiobjective optimization approach or clustering-based approaches. The proposed method is tested and validated against a realistic example. Our results confirm that the proposed method is robust and efficient and finds acceptable solutions with no or minimal violations of constraints. These results suggest that our advanced multiobjective optimization technique can select high-quality RMs by striking a balance between conflicting constraints. Thus, a better decision can be made while running much fewer simulations than would be required with traditional methods.

Multi-Objective Path Optimization in Fog Architectures Using the Particle Swarm Optimization Approach.

IoT systems can successfully employ wireless sensor networks (WSNs) for data gathering and fog/edge computing for processing collected data and providing services. The proximity of edge devices to sensors improves latency, whereas cloud assets provide higher computational power when needed. Fog networks include various heterogeneous fog nodes and end-devices, some of which are mobile, such as vehicles, smartwatches, and cell phones, while others are static, such as traffic cameras. Therefore, some nodes in the fog network can be randomly organized, forming a self-organizing ad hoc structure. Moreover, fog nodes can have different resource constraints, such as energy, security, computational power, and latency. Therefore, two major problems arise in fog networks: ensuring optimal service (application) placement and determining the optimal path between the user end-device and the fog node that provides the services. Both problems require a simple and lightweight method that can rapidly identify a good solution using the constrained resources available in the fog nodes. In this paper, a novel two-stage multi-objective path optimization method is proposed that optimizes the data routing path between the end-device and fog node(s). A particle swarm optimization (PSO) method is used to determine the Pareto Frontier of alternative data paths, and then the analytical hierarchy process (AHP) is used to choose the best path alternative according to the application-specific preference matrix. The results show that the proposed method works with a wide range of objective functions that can be easily expanded. Moreover, the proposed method provides a whole set of alternative solutions and evaluates each of them, allowing us to choose the second- or third-best alternative if the first one is not suitable for some reason.

Research on delivery problem based on two-stage multi-objective optimization for takeout riders

From the perspective of takeout riders, in order to ensure the safety of riders, reasonably arrange the distribution tasks of riders and improve the distribution efficiency of the platform, this paper proposes a new distribution mode. In the future, efficiency is the core competitiveness of the takeout platform, and humanization is an important condition for the long-term development of the takeout platform. Both are indispensable. This paper simplifies the traditional many to many distribution mode into one to many distribution mode, and realizes the new distribution mode through two-stage multi-objective optimization algorithm. In the first stage, genetic algorithm is applied to analyze and optimize the new distribution mode for the first time; In the second stage, the large-scale neighborhood search algorithm is applied to destroy and reorganize the distribution route obtained in the first stage according to the objective function to obtain the optimal distribution route. Aiming at the three scenarios of consumer group distribution generated by random simulation, high-density and low-density consumer group distribution in Shenyang, this paper uses MATLAB tools to simulate. Through the analysis of simulation results, the working hours and delivery distances of takeout riders are reduced, and the maximum delivery volume is limited. So the method proposed in this paper can not only improve the intelligent level of distribution, but also propose a more humanized distribution mode from the perspective of takeout riders, which greatly supports the sustainable development of the delivery system of network connected takeout platform.

Multi-objective Pigeon-inspired Optimized feature enhancement soft-sensing model of Wastewater Treatment Process

Under the increasingly severe fresh water supply pressure, wastewater treatment is considered to be the optimal strategy to satisfy the current and future water demand, thus being highly valued by most countries. As a complicated process, there are some hard-to-measure effluent indicators in wastewater treatment such as 5-day Biological Oxygen Demand (BOD5), which brings significant difficulties to the monitoring of key indicators in sewage disposal process, thus imposing massive constraints on evaluation of effluent quality. In order to realize the real time supervision of the water quality, data-driven artificial intelligence soft-sensing models have been widely considered as an active research field. However, the over-parameterization of artificial intelligence methods and the complication of sewage treatment environment lead to obvious non-Gaussian characteristics in the wastewater data, which makes it difficult to artificially set parameters to maintain the optimal values to satisfy the accuracy requirement in wastewater treatment process. In view of the aforementioned problems, a soft-sensing model for super-parameter intelligent setting of Broad Learning System based on Overcomplete Independent Component Analysis (OICA) is proposed in this paper. A two-stage multi-objective optimization algorithm is adopted in the model to set superparameters intelligently, which reduces human intervention and improves the accuracy of the model. Additionally, the adaptability of the proposed model to data is significantly enhanced through improvement of the feature extraction ability of the Broad Learning System (BLS) and the capture of peculiar non-Gaussianity in wastewater data with statistical methods. Comparative experiments are conducted on the sewage simulation platform BSM1 with state-of-the-art artificial intelligence soft measurement models, and the results show that the advantage of the presented model lies both in accuracy and in modeling speed, demonstrating the effectiveness of the proposed method.

Multiobjective Optimization of System Configuration and Component Capacity in an AC Minigrid Hybrid Power System

This article proposes a two-stage optimization algorithm to effectively determine the system configuration at one stage, as well as the capacity of components at the other stage in the middle of the former. This algorithm fits in best with the hybrid systems with more possible types of components. The studied system, in this article, includes diesel generators, wind turbines, photovoltaic arrays, and tidal generators as the power generation components, as well as battery banks and flywheels as the energy storage components. It also includes fuel cells and electrolyzers that either work as batteries or generate electricity in the presence of biomass. When the number of components (decision variables) increases, it becomes difficult to find an optimal solution by the conventional methods. Therefore, in this study, a two-stage multiobjective optimization algorithm is applied to design a cost effective and environmental friendly ac minigrid hybrid power system. In each iteration of the proposed algorithm, first, renewable energy sources and energy storage components are selected to form a hybrid power system along with the diesel generator. Then, the capacities of the components are optimized based on the two objective functions, including levelized cost of electricity and emissions. The optimization model uses real annual data in hourly time intervals for the load, solar insolation, ambient temperature, and wind speed. It is found that the proposed algorithm decreases the susceptibility of the solutions to multiple runs compared with the conventional algorithms and achieves a better optimized power system design.

Multi-objective optimization design of steel structure building energy consumption simulation based on genetic algorithm

Abstract In order to solve the problems of data acquisition, quantitative analysis and model solving in the field of construction schedule optimization, a construction schedule optimization system based on genetic algorithm was constructed. On this basis, the construction schedule two-stage multi-objective optimization models of “duration-cost” and “fixed duration-resource equilibrium” are established, which aim at the lowest cost and resource equilibrium. Through the investigation and analysis of the project contract documents, the energy consumption and cost of the normal construction and emergency construction state of the contract plan of the basic project part (from the beginning of precipitation activities to the end of +0) are obtained. This section was optimized for the analysis. The genetic algorithm is used to solve the model, and the optimal duration of each process and the optimal start time of non-critical process are determined. The feasibility and effectiveness of the system and model are verified by practical application in the actual project, which provides support for determining the construction schedule scientifically and reasonably and helps to improve the construction schedule technical application effect and construction schedule management level.

A two-stage multi-objective stochastic optimization strategy to minimize cost for electric bus depot operators

The electrification of large-scale bus depots will bring challenges for bus depot operators (BDOs) aiming to minimize operational costs. A potential solution for BDOs managing electric buses (EBs) with large batteries that are capable of vehicle-to-grid (V2G) operational functionalities, along with on-site generation facilities, is to actively participate in the energy market. This paper proposed a novel two-stage multi-objective stochastic optimization-based energy management system (EMS) for BDOs to trade energy in a day-ahead (DA) energy market while considering the expected cost of energy imbalances in the real-time (RT) market. The optimization model for charge–discharge scheduling is based on a mixed-integer linear programming approach. The uncertainties associated with the DA forecast are complemented by a stochastic model. Minimizing the cost of electricity and the penalty costs for battery capacity degradation are the objectives that need to be met simultaneously. The proposed EMS is evaluated using simulation studies considering a hypothetical bus depot in Australia. The effectiveness of the proposed EMS for overall financial savings is validated by comparing its performance with state-of-the-art scheduling strategies. Results show that the net energy cost for the proposed EMS is 38% lower compared to a deterministic scheduling strategy that does not allow V2G flexibilities, 33% lower compared to a deterministic scheduling strategy that allows V2G flexibilities and 17% lower compared to a stochastic scheduling strategy that minimizes overall costs.

Two-stage economic, reliability, and environmental scheduling of multi-microgrid systems and fair cost allocation

Microgrids (MGs) play a critical role to enhance the flexibility and efficiency of the distribution systems as well as reliability and environmental problems. In this paper, we propose a two-stage multi-objective optimization framework for optimal energy management of multi-microgrid systems (MMG). The proposed model considers the total cost, energy not supplied (ENS), and greenhouse gas emissions to determine the best plan for the MMG system. We propose a hybrid lexicography-compromise programming (CP) to handle non-homogeneous objective functions by the two-stage technique. At the first stage, the operation cost of the MMG system is minimized to determine the best plan from the economic perspective. At the second stage, the ENS and greenhouse gas emissions have been considered objective functions using CP method. The main advantage of the proposed model is that it remains the operation cost of the MMG system at its optimum. The proposed model is based on the cooperation of MGs to provide a stable and reliable solution, where the total cost of the MMG system has been allocated among MGs based on their contributions. The simulation results show the cost of the multi-microgrid system, ENS, and CO2 emissions are improved by 33.72%, 83.5%, and 8%, respectively. • Cooperative energy management for renewable MMG systems. • Proposing the HLCP approach to considers the preferences between objectives. • Presenting a two stage energy management that considers cost, emissions, and ENS. • Ensuring that the cost of the MMG remains at its optimum. • Presenting a fair cost allocation to divide the overall gain of coalition among MGs.

Method for Dynamic Service Orchestration in Fog Computing

Fog computing is meant to deal with the problems which cloud computing cannot solve alone. As the fog is closer to a user, it can improve some very important QoS characteristics, such as a latency and availability. One of the challenges in the fog architecture is heterogeneous constrained devices and the dynamic nature of the end devices, which requires a dynamic service orchestration to provide an efficient service placement inside the fog nodes. An optimization method is needed to ensure the required level of QoS while requiring minimal resources from fog and end devices, thus ensuring the longest lifecycle of the whole IoT system. A two-stage multi-objective optimization method to find the best placement of services among available fog nodes is presented in this paper. A Pareto set of non-dominated possible service distributions is found using the integer multi-objective particle swarm optimization method. Then, the analytical hierarchy process is used to choose the best service distribution according to the application-specific judgment matrix. An illustrative scenario with experimental results is presented to demonstrate characteristics of the proposed method.

A two-stage multi-objective optimization method for envelope and energy generation systems of primary and secondary school teaching buildings in China

A comfortable indoor environment plays an important role in improving students' learning efficiency and health. How to optimize the design of primary and secondary school education buildings to achieve a comfortable indoor environment, considering both energy and cost is a considerable challenge. This paper proposes a two-stage multi-objective optimization method based on a meta-model to obtain the optimal design scheme for primary and secondary school education buildings, based on daylighting, thermal comfort, energy savings and economy. The method has two stages: building envelope optimization and building generation system optimization. In the stage of building envelope optimization, an artificial neural network (ANN) model coupling optimization algorithm is used to optimize the building envelope. The performances of the non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm are compared in the optimization process. In the stage of building generation system optimization, the design optimization of the building photovoltaic generation system is studied. Finally, the effectiveness of the two-stage optimization method is verified by a typical teaching building in Nanjing. The results show that the optimal scheme set of building envelope design can be obtained by this optimization method, and the optimal tilt angle and azimuth angle of the photovoltaic generation system are 30° and 210°, respectively. The minimum payback periods of the photovoltaic generation system are 11.75 years and 9.32 years under the policy of selling electricity that is not permitted and selling electricity that is permitted, respectively.

Design and characterization of effective solar cells

We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency (Q_e) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, Q_e of a cell with its TCO layer’s doping methods and TCO and BR layer’s material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications.

Multiobjective Optimization-Aided Decision-Making System for Large-Scale Manufacturing Planning.

This work is geared toward a real-world manufacturing planning (MP) task, whose two objectives are to maximize the order fulfillment rate and minimize the total cost. More important, the requirements and constraints in real manufacturing make the MP task very challenging in several aspects. For example, the MP needs to cover many production components of multiple plants over a 30-day horizon, which means that it involves a large number of decision variables. Furthermore, the MP task's two objectives have extremely different magnitudes, and some constraints are difficult to handle. Facing these uncompromising practical requirements, we introduce an interactive multiobjective optimization-based MP system in this article. It can help the decision maker reach a satisfactory tradeoff between the two objectives without consuming massive calculations. In the MP system, the submitted MP task is modeled as a multiobjective integer programming (MOIP) problem. Then, the MOIP problem is addressed via a two-stage multiobjective optimization algorithm (TSMOA). To alleviate the heavy calculation burden, TSMOA transforms the optimization of the MOIP problem into the optimization of a series of single-objective problems (SOPs). Meanwhile, a new SOP solving strategy is used in the MP system to further reduce the computational cost. It utilizes two sequential easier SOPs as the approximator of the original complex SOP for optimization. As part of the MP system, TSMOA and the SOP solving strategy are demonstrated to be efficient in real-world MP applications. In addition, the effectiveness of TSMOA is also validated on benchmark problems. The results indicate that TSMOA as well as the MP system are promising.

Efficient integration of demand response and plug-in electrical vehicle in microgrid: Environmental and economic assessment

The microgrid has become a promising area to mitigate multiple challenges of the power system, resulting from renewable energy integration, climate change, as well as economic issues. However, the integration of flexible emerging technologies in microgrid operation to enhance the efficiency of energy supply, as well as environmental and economic challenges are urgently needed. Motivated by these challenges, this paper elaborates on the two-stage multi-objective optimization model for optimal scheduling of microgrid. The demand response program based on the time of use model, and plug-in electric vehicles have been integrated into the microgrid to contribute in economic and environmental goals. The proposed model extends the realistic EVs model based on the Wohler curve and AC-power flow equations. The proposed model is exposed to a wide range of random parameters, including wind output, load, price, and EVs’ behaviours. To handle the uncertainties, and solving problem, scenario-based stochastic and weighted sum based on fuzzy decision-making strategies are respectively applied. The proposed model is tested on the 33-bus microgrid and examined for different cases. Results prove the impacts of the efficient integration of demand response, and a plug-in electric vehicle in the reducing of operational cost, and emission by 3.57% and 3.4%, respectively. • Proposing a realistic model for EV based on DoD and battery degradation. • Providing high-level flexibility for MG with the integration of PEV and DR. • Emission-economic assessment of renewable-based MG with flexible resources. • Proposing a two-stage stochastic programming model to capture uncertainties.