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A flexible employee recruitment and compensation model: A bi-level optimization approach

COMPUTERS & INDUSTRIAL ENGINEERING 2022年 165卷

作者： Ben-Gal, Hila Chalutz Forma, Iris A. Singer, Gonen Tel Aviv Afeka Acad Coll Engn Sch Ind Engn & Management Tel Aviv Israel Bar Ilan Univ Fac Engn Ramat Gan Israel

The growing practice of flexible work following the COVID-19 pandemic is likely to have a significant impact on management and human resource (HR) practices. In this paper, we propose a novel bi-level mathematical programming model that can serve as a decision support tool for firms in real-life settings to improve recruitment and compensation decisions associated with hybrid and flexible work plans. The proposed model is composed of two levels: the first level reflects the company's goal of maximizing profitability by offering competitive salaries to candidates. The second level reflects the candidate's goal of minimizing the gap between their desired salary and the perceived benefits of a preferred flexible plan. We show that the model provides an exact solution based on a mixed integer formulation and present a computational analysis based on changing candidate behaviors in response to the firm's strategy, thus demonstrate how the problem's parameters influence the decision policy. Our proposed model leads to efficient managerial practices, compared to conventional models that utilize a single non-flexible plan. Results indicate that introducing a flexible work plan leads to an improvement of up to 59 percent in the firm's profitability. We apply the optimal solution of the bi-level model to a real-world case study of a company recruiting software engineers. Results demonstrate the applicability of the optimal solution to a real-world dataset. This paper advances knowledge by proposing a novel bi-level model for effective recruitment and compensation decisions in real-world flexible workforce settings.

关键词： Flexible work Hybrid work bi-level optimization model Mathematical programming Human Resource Analytics Recruitment Compensation

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Designing a Clearing model for the Regional Electricity Spot Market Based on the Construction of the Provincial Electricity Market: A Case Study of the Yangtze River Delta Regional Electricity Market in China

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PROCESSES 2025年第2期13卷 492-492页

作者： Li, Yunjian Zhang, Lizi Cong, Ye Chen, Haoxuan Zhang, Fuao North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China

Building the regional electricity spot market (RESM) in a representative area is an important move to promote the electricity market reform and new power system construction in China. In this paper, the RESM operation model and optimization method are established, which take into account the special power grid operation mechanism and market construction achievements in the provincial electricity spot market. Firstly, the influencing factors, core elements, market structure, and operation model of RESM construction in China are analyzed. Secondly, a bi-level optimization model of the RESM is established. The lower layer is the pre-clearing model of the provincial electricity spot market, which is used to optimize the unit combination strategy, considering unit operation constraints and power grid security constraints in the province. The upper layer is the optimization clearing model of the RESM, which is used to optimize the clearing price and adjust the unit operation strategy and inter-provincial electricity trading strategy, considering the security constraints of regional power grid tie lines. Finally, the RESM composed of power grids in the Yangtze River Delta region of China is simulated as an example. The analysis focuses on the operational state of the power grid after the operation of the RESM, considering its safety benefits, economic benefits, and environmental benefits. The optimization of the RESM can effectively solve the serious regional power grid congestion problem, which is achieved through the superposition and printing of pre-clearing results in various provinces, and the average daily cost of electricity purchasing in the region has been reduced by about CNY 11 million, while the annual cost has been reduced by about CNY 4 billion. In addition, the total carbon emissions have been reduced by 11,000 tons per day and 0.18 kg per kilowatt hour on average, and scenes without power abandonment account for more than 95% of the total scenes.

关键词： bi-level optimization model market clearing provincial electricity spot market regional electricity spot market regional power grid

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A bi-level Capacity optimization Method for Hybrid Energy Storage Systems Combining the IBWO and MVMD Algorithms

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ENERGIES 2025年第7期18卷 1777-1777页

作者： Xing, Qiaoqiao Li, Shidong Qiu, Da Long, Yang Liao, Qinyi Yin, Xiangjin Li, Yunxiang Qian, Kai Hubei Minzu Univ Sch Intelligent Sci & Engn Enshi 445000 Peoples R China Hubei Minzu Univ Key Lab Green Mfg Superlight Elastomer Mat State Ethn Affairs Commiss Enshi 445000 Peoples R China

With the swift evolution of renewable energy technologies, the design and optimization of microgrids have emerged as vital components for fostering energy transition and promoting sustainable development. This study presents a bi-level capacity optimization model for microgrids, integrating wind-solar generation with hybrid electric-hydrogen energy storage systems to simultaneously enhance economic efficiency and system stability. The outer layer minimizes the annual total cost through the application of an Improved Beluga Whale optimization (IBWO) algorithm, which is enhanced by strategies including the reverse elitism strategy, horizontal and vertical crossover operations, and a whirlwind scavenging strategy to improve performance. The inner layer builds on the optimized results from the outer layer, employing a Multivariable Variational Mode Decomposition (MVMD) algorithm to regulate the power output of the energy storage system. By integrating electric-hydrogen hybrid storage technology, the inner layer effectively mitigates power fluctuations. Furthermore, this study designs a modal decomposition-based charging and discharging scheduling strategy to ensures the system's continuous and stable operation. Simulations performed on MATLAB 2018b and CPLEX 12.8 platforms indicate that the proposed dual-layer model decreases annual total expenses by 27.5% compared to a single-layer model while keeping grid-connected power variations within 10% of the installed capacity. This research provides innovative perspectives on microgrid optimization design and offers substantial technical support for ensuring stability and economic efficiency in intricate operational settings.

关键词： microgrid bi-level optimization model hybrid energy storage improved beluga whale optimization algorithm MVMD algorithm

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Operation strategy for a net-zero emissions park microgrid with multi-stakeholders: A multi-objective with bi-level optimization approach

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ENERGY 2025年 321卷

作者： Kang, Jia-Ning Liu, Li Liu, Lancui Wei, Yi-Ming Beijing Inst Technol Ctr Energy & Environm Policy Res Beijing 100081 Peoples R China Beijing Inst Technol Sch Management Beijing 100081 Peoples R China Beijing Lab Syst Engn Carbon Neutral Beijing 100081 Peoples R China Basic Sci Ctr Energy & Climate Change Beijing 100081 Peoples R China Beijing Normal Univ Sch Business Beijing 100875 Peoples R China

Microgrids are essential for achieving stable, carbon-neutral power systems, with park-level projects being key implementations. However, research gaps persist in addressing complex operational scheduling and multistakeholder coordination challenges. This study develops a novel park-level microgrid integrating biomass-togas, carbon removals, and hydrogen storage to achieve net-zero emissions. We establish a bi-level optimization framework that integrates long-term planning and short-term operations, where the upper level minimizes lifecycle costs while the lower level simultaneously optimizes operational profits and risk mitigation. The nonlinear problem is solved using a decomposition-based multi-objective evolutionary algorithm. A hybrid mechanism combining Shapley value principles with Nash bargaining theory is further proposed to equitably allocate benefits based on stakeholders' contributions to profit, risk reduction, and carbon mitigation. Empirical analysis shows carbon removal technologies can enhance system stability and energy utilization. Medium-to-high carbon prices boost operating profits by 131.7%-170.1 % and lower capital costs by 21.8%-29.9 %, while achieving negative emissions. The proposed model improves stakeholder satisfaction by 7.8 % compared to traditional methods. Sensitivity analysis reveals hydrogen prices below 19 & YEN;/kg risk losses, recommending high carbon price deployment for financial risk mitigation. This framework provides a robust approach for designing sustainable, economically viable park-level microgrids, enabling equal multi-stakeholder benefit allocation and risk-aware operational optimization.

关键词： bi-level optimization model Carbon removal unit Net-zero emissions Park microgrid Profit allocation

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A bi-level solution strategy based on distributed proximal policy optimization for transmission and distribution network dispatch with EVs and variable energy

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APPLIED ENERGY 2025年 384卷

作者： Lu, Peng Lan, Hanqing Yuan, Qiwei Jiang, Zhihao Cao, Siqi Ding, Jingyi Wei, Qianrun Fan, Junqiu Cai, Quan Zhang, Ning Ye, Lin Li, Kangping Shahidehpour, Mohammad Siano, Pierluigi China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Guizhou Power Grid Co LTD Guiyang 550002 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China Shanghai Jiao Tong Univ Coll Smart Energy Shanghai 200240 Peoples R China IIT Elect & Comp Engn Dept Chicago IL 60616 USA Univ Salerno Dept Management & Innovat Syst Via Giovanni Paolo II 132 I-84084 Fisciano Italy Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

Integrating large-scale wind power and extensive electric vehicle (EV) loads into the power grid impacts the system's safety and economic operations, posing challenges including frequent changes in grid dispatch instructions, unregulated EV charging and discharging behaviors, and increased network losses. Therefore, a bilevel optimization strategy model employing distributed proximal policy optimization for transmission and distribution network dispatch considering large-scale EVs is established, efficiently managing unit outputs and the system's capacity for charging and discharging, allocating these capabilities to individual nodes in real-time. The upper-level model focuses on minimizing the system's total operating costs, optimizing the operational status of thermal units, and regulating the number of EVs charging and discharging in the transmission network. The lower layer seeks to reduce the distribution network's total network loss costs by optimizing EV charging and discharging power, active/reactive power in branch circuits, and voltage levels at node charging stations. The best solutions for the upper-layer and lower-layer models are solved using the distributed proximal policy optimization (DPPO) method. The bi-level optimization model is tested on a modified IEEE-24 and IEEE-33 system and demonstrated by case studies.

关键词： bi-level optimization model Cluster of EVs Distributed proximal policy optimization algorithm Coordination between transmission and distribution flows

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Capacity optimization of Grid-Connected Solar-Wind-Storage-Electrolytic Aluminum System

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 3413-3423页

作者： Huo, Qunhai Li, Xiaoqian Liu, Qiran Deng, Huawei Wang, Wenyong Xu, Feng Lu, Yi Chinese Acad Sci Inst Elect Engn Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China China Aerosp Sci & Ind Corp Ltd Inst Magnet Levitat & Electromagnet Prop Beijing 100070 Peoples R China State Grid Zhejiang Elect Power Res Inst Hangzhou 310014 Peoples R China Hangzhou E Energy Technol Co Ltd Hangzhou 310014 Peoples R China

Energy-intensive industries consume a considerable amount of energy and emit high levels of carbon dioxide, which places a significant burden on environmental protection. However, there is a possibility that the transformation of these industries into green and low-carbon operations may prove inadequate for addressing global climate change and achieving carbon neutrality goals. In order to address this challenge, this paper focuses on the load electrolytic aluminum production process and constructs a bi-level optimization model. The objective is to optimize the configuration of photovoltaic (PV), wind turbines (WT), and energy storage systems in order to maximize the utilization of renewable energy sources in aluminum production. The outer layer of the bi-level optimization model is designed to achieve maximum economic benefits, while the inner layer is set to identify the minimum energy storage configuration capacity. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is employed for the purpose of optimization analysis. Moreover, the model takes into account the flexible adjustability of electrolytic aluminum, treating it as a power-adjustable load in order to efficiently match the electricity demand of the production process with the generation capacity of renewable energy sources. The results demonstrate that the proposed optimization model not only achieves efficient local consumption of renewable energy but also reduces the investment cost and strengthens the capability of system to cope with the volatility of renewable energy generation by utilizing electrolytic aluminum as an adjustable resource. This has significant practical implications for the utilization of a high proportion of renewable energy in the electrolytic aluminum production process.

关键词： Aluminum industry bi-level optimization model controllable power load non-dominated sorting genetic algorithm (NSGA-II) renewable energy sources renewable energy sources controllable power load non-dominated sorting genetic algorithm (NSGA-II) renewable energy sources

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Optimal Configuration of Electricity-Heat Integrated Energy Storage Supplier and Multi-Microgrid System Scheduling Strategy Considering Demand Response

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ENERGIES 2024年第21期17卷 5436页

作者： Liu, Yuchen Dou, Zhenhai Wang, Zheng Guo, Jiaming Zhao, Jingwei Yin, Wenliang Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Peoples R China State Power Investment Corp Haiyang Offshore Wind Guangzhou 510710 Peoples R China

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively;and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system.

关键词： multi-microgrid system electricity-heat integrated energy storage supplier demand response bi-level optimization model

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Optimizing community post-earthquake emergency medical capability via mobile hospital configuration

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RELIAbiLITY ENGINEERING & SYSTEM SAFETY 2025年 261卷

作者： Zhao, Taiyi Zeng, Cong Chu, Cunjie Tang, Yuchun Wang, Jingquan Northeast Elect Power Univ Sch Civil Engn & Architecture Jilin Peoples R China Shandong Prov Commun Planning & Design Inst Grp Co Jinan Peoples R China Southeast Univ Sch Civil Engn Dept Construct & Real Estate Nanjing Jiangsu Peoples R China Southeast Univ Sch Civil Engn Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing Peoples R China

Emergency medical capability is critical for communities to reduce casualties after earthquakes. In this research, a bi-level design optimization model for the configuration of mobile hospitals is developed to minimize both the post-earthquake transfer time of casualties from their residential areas to the mobile hospitals, and the withinhospital service time including mean queue waiting time and average processing time. The upper level of the model characterizes community officials' decisions regarding possible mobile hospital configuration that consists of three decision variables: the location of mobile hospitals on candidate sites, their functional classes, and the number of attending physicians dispatched to them. The lower level model leverages the mixed user equilibrium traffic assignment to simulate the path selection behaviour of drivers transferring casualties with different injury heterogeneity under different information perceptions. Meanwhile, considering the uncertainty in the number of casualties and the post-earthquake damage state of bridges, a robust configuration model for the stochastic case is also established based on the robust optimization theory. To tackle the optimization model, an interactive solution approach combining the genetic algorithm and the modified mixed equilibrium assignment algorithm is presented. A demonstrative study is conducted on a middle-class city located in an earthquake-prone area.

关键词： Community disaster resilience Mobile hospital Emergency medical service Configuration optimization bi-level optimization model Genetic algorithm

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Joint optimization of Longitudinal and Lateral Locations of Autonomous-Vehicle-Dedicated Lanes on Expressways

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第1期25卷 430-442页

作者： Liu, Congjian Zhang, Cheng Yu, Chunhui Chen, Ke Jiang, Zehao Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Univ Shanghai Sci & Technol Dept Traff Engn Shanghai 200093 Peoples R China Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China

Appropriate deployment of autonomous-vehicle-dedicated lanes (AVDLs) is crucial for optimizing transportation system with mixed human-driven and autonomous vehicles. Existing studies mainly focus on matching the capacity and traffic flow volume to maximize throughput under different AV penetration rate, while lane changing scenarios and network geometry impacts were ignored. Therefore, the lateral locations in optimal deployment on expressway cannot be determined. This study developed an analysis framework to jointly optimize the longitudinal and lateral locations of AVDLs on expressways. The problem is formulated as a bi-level optimization model. In the lower level, a lane-level multiclass equilibrium assignment model is built to predict traffic flow distribution among lanes on both longitudinal and lateral locations regarding AVDL deployment plan. In the upper level, a 0-1 integer programming model is established to obtain the optimal deployment plan of the AVDL with minimal total travel time (TTT). Additionally, a problem-specific heuristic-based adaptive large neighborhood search algorithm is developed to solve the problem. The advantages of the proposed model are validated in North-South Elevated Expressway, Shanghai, China. Results indicate that the proposed framework outperforms existing methods in terms of TTT reduction. Finally, sensitivity analysis demonstrates the robustness of the developed method in reducing the TTT under different headways, demand levels, and OD distributions. The proposed framework enables the optimal deployment of AVDLs regarding both longitudinal and lateral locations in urban expressways containing multiple on-and off-ramps, which supports decision-making for managing expressway with mixed HV and AV traffic flow.

关键词： Roads optimization Transportation Mathematical models Analytical models Adaptation models Robustness Autonomous-vehicle-dedicated lane lateral location bi-level optimization model heterogeneous traffic flow adaptive large neighborhood search algorithm

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Optimal sizing for a wind-photovoltaic-hydrogen hybrid system considering levelized cost of storage and source-load interaction

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第11期48卷 4129-4142页

作者： Li, Junzhou Zhao, Jinbin Chen, Yiwen Mao, Ling Qu, Keqing Li, Fen Shanghai Univ Elect Power Coll Elect Engn Shanghai 200090 Peoples R China

Hydrogen energy storage system (HESS) has excellent potential in high-proportion renewable energy systems due to its high energy density and seasonal storage characteristics. After detailing the volatility of wind speed, irradiance and load, this paper proposes a bi-level optimization model to analyze the economic operation of the wind-photovoltaichydrogen hybrid system (WPH-HS). First, the relationship between the source-load output matching and operating conditions of HESS is studied, two evaluation indicators are described, which can be adjusted by wind-solar complementarity on the source side and demand response on the load side. Second, considering the levelized cost of storage (LCOS), the total annual cost (TAC) calculation method of WPH-HS is presented, and this paper provides a new hybrid optimization technology of chaotic search, particle swarm optimization and non-dominated sorting genetic algorithm2. Finally, the system is simulated with the MATLAB software to determine the optimal sizing of components and minimize the LCOS while ensuring the optimal TAC. The simulation results are elaborated in detail. In particular, the added source-load interaction reduces the TAC and LCOS by 7.3% and 10.3%. When two indicators reach 0.03 and 0.1745, the system is economically viable with the LCOS of 0.276 USD/kWh. The hybrid optimization algorithm can achieve better result in fewer iterations. & COPY;2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hybrid energy system Seasonal hydrogen storage system Renewable energy bi-level optimization model levelized cost of storage

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