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RETRACTED: Selecting and balancing market portfolio using artificial intelligence and fuzzy multiobjective decision-making model (Retracted Article)

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2022年第4期30卷 382-398页

作者： Shih, Wen-Lung Wei, Chiu-Chi Lin, Hsien-Hong Chang, Pin-Hsiang Chung Hua Univ PhD Program Technol Management Hsinchu 300 Taiwan Huaiyin Inst Technol Dept Logist Engn Nanjing Jiangsu Peoples R China Yuan Pei Univ Med Technol Dept Healthcare Management Hsinchu Taiwan

Most enterprises focus on product portfolio management (PPM) and exclude market portfolio management, and individual markets are selected solely based on financial performance which may not be appropriate because other factors may dictate the outcome of the market selection. This study proposes a market portfolio model that considers market share, market growth, market competition, market risk, and market cost to maximize overall profit. A three-stage approach is proposed to identify potential product markets using an AI algorithm. A set of the most promising market is then determined based on the results of Stage 1 using a fuzzy multi-objective mathematical programming model by concurrently maximizing the total market share and market growth, and minimizing the market competition and market risk. A single-objective mathematical programming model is then used to determine a market portfolio to maximize the total profit using the results for Stage 2. The single-objective model is tested using two sets of threshold constraints. The balance of the market portfolio is discussed and the results are compared. The novelty of the article: (1) the first study involves a product market portfolio, (2) the decision of market selection is scientific (using AI instead of personal judgment), holistic (factors include market share, growth, competition, risk, cost, and profit) and optimized (using mathematical optimization), and (3) the market portfolio maximizes total profit. (4) the proposed method produces a portfolio with a higher profit than the TOPSIS method.

关键词： Ansoff matrix market segment market portfolio mathematical programming TOPSIS

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A Simulated Annealing Algorithm for the Vehicle Routing Problem With Parcel Lockers

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IEEE ACCESS 2022年 10卷 20764-20782页

作者： Yu, Vincent F. Susanto, Hadi Jodiawan, Panca Ho, Tsai-Wei Lin, Shih-Wei Huang, Yu-Tsung Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Natl Taiwan Univ Sci & Technol Ctr Cyber Phys Innovat Taipei 106 Taiwan Universal Global Sci Ind Co Ltd Nantou 542 Taiwan Chang Gung Univ Dept Informat Management Taoyuan 333 Taiwan Ming Chi Univ Technol Dept Ind Engn & Management New Taipei 243 Taiwan Linkou Chang Gung Mem Hosp Dept Neurol Taoyuan 333 Taiwan Kerry TJ Logist Co Ltd Taipei 100 Taiwan

Due to the growth in e-commerce, city logistics needs to cope with the rise of customers' demand. Consequently, it adds to the complexity of the last-mile delivery process. Moreover, this process often contributes significantly to the distribution cost of companies. An alternative to alleviate the problem is the utilization of parcel lockers. It allows the delivery to be extended not only for home delivery but also for locker delivery, which brings several advantages in terms of cost-saving. However, considering multiple delivery options means adding new complexity to the delivery system. In the previous works, vehicle routing problem with time windows (VRPTW) has been widely dealt by only considering home delivery. This research proposes a new VRPTW variant by adding locker delivery as one of the delivery options. The developed problem is called the vehicle routing problem with parcel lockers (VRPPL). The goal of VRPPL is to minimize the total traveling cost. In this research, we formulate a new mathematical programming model and develop a simulated annealing (SA) algorithm to deal with VRPPL. A newly generated set of instances was developed from the well-known Solomon's VRPTW instances. The performance of the proposed algorithm is also presented.

关键词： Metaheuristics Costs Vehicle routing mathematical models Logistics Routing mathematical programming Vehicle routing problem simulated annealing time window parcel locker delivery options

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A Data and Model Driven Method for Low-Voltage Distribution Area Topology Identification 9

A Data and Model Driven Method for Low-Voltage Distribution ...

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9th Asia Conference on Power and Electrical Engineering, ACPEE 2024

作者： Han, Fujia Wang, Xiaohui Chen, Yuyao Qiao, Ji Zhu, Qiongfeng Feng, Mo Center of Artificial Intelligence Application China Electric Power Research Institute Beijing China

ISBN: (纸本)9798350309638

Topology identification of low-voltage distribution areas is the important basis for intelligent low-voltage distribution network management. At present, topology relationship identification for distribution areas faces challenges, such as confusion, loss and inaccuracy of electricity user information. Traditional topology identification methods for low-voltage distribution areas has a series of problems, such as low accuracy and low efficiency. Therefore, combining the operating mechanism and measured data of distribution areas, this paper presented a data and model driven topology identification method for low-voltage distribution areas. Firstly, hierarchical clustering is utilized to cluster the voltage sequences of observer meters and electricity users respectively. Then, Pearson correlation coefficient is utilized to measure the correlation among voltage series of distribution area transformers, observer meters and electricity users, in order to realize the phase identification of distribution areas. Finally, based on the phase identification results of distribution areas, the mathematical programming model of the line identification of distribution areas is established through the electricity conservation mechanism, and the optimal solution of the model is obtained by the CPLEX optimization solver to realize the line identification of distribution areas. The real-life experiment results show that the proposed method can accurately, quickly and automatically realize the topology identification of distribution areas, which has high application value and potential in practical applications. © 2024 IEEE.

关键词： mathematical programming

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A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems

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RENEWABLE ENERGY 2022年第0期194卷 822-849页

作者： Alabi, Tobi Michael Aghimien, Emmanuel I. Agbajor, Favour D. Yang, Zaiyue Lu, Lin Adeoye, Adebusola R. Gopaluni, Bhushan Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen Peoples R China Hong Kong Polytech Univ Dept Bldg Environm & Energy Engn Renewable Energy Res Grp RERG Hong Kong Peoples R China Univ British Columbia Dept Chem & Biol Engn Data Analyt & Intelligent Syst DAIS Lab Vancouver BC Canada City Univ Hong Kong Dept Architecture & Civil Engn Bldg Energy Res Grp Hong Kong Peoples R China Durban Univ Technol Dept Construct Management & Quant Surveying Steve Biko Campus Durban South Africa Delano Architects Lagos Architecture Dept Lagos Nigeria

The optimal co-planning of the integrated energy system (IES) and machine learning (ML) application on the multivariable prediction of IES parameters have mostly been carried out separately in the literature. Meanwhile, the synergy of optimization methods and ML techniques can enhance the feasibility of a zero-emission IES, boost realistic planning, and promote accurate day-ahead scheduling. Thus, a comprehensive review of integrated optimization and ML techniques in IES is crucial and hereby presented in this study. Critical issues such as an overview of IES structure, IES modeling approaches and techniques, application of ML in IES research, and the trends of integrating ML and optimization techniques for optimal and feasible planning of IES were presented. Specifically, extant studies on the integrated approach were reviewed under ML hyperparameter tuning using optimization, combined uncertainty estimation and decision making, integrated ML and scenario generation, integrated prediction, and optimal decision-making techniques. Findings from this review show that the IES structure depends on the available technologies, the multi-energy demand patterns, the available renewable resources, and the planner's objective. It was also revealed that despite the popularity of ML models and the benefits of synergizing them with optimization models, the application on IES has not been fully explored. The main conclusion from the review is that an IES framework with the aim of a carbon neutrality target is worthy of development. Also, the application of integrated ML and optimization on IES is still at its infant stage;hence, more research exploration is required in this area.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Multi-energy system Machine learning Deep learning mathematical programming Optimization methods Carbon neutrality

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Exact relaxation of complementary constraints for optimal bidding strategy for electric vehicle aggregators

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IET RENEWABLE POWER GENERATION 2022年第12期16卷 2493-2507页

作者： Chen, Dapeng Jing, Zhaoxia Li, Zhigang Xu, Hedong Ji, Tianyao South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China China Elect Power Planning & Engn Inst Beijing Peoples R China Jinan Univ Inst Finance Guangzhou Peoples R China

This paper concentrates on the optimal bidding strategy of a plug-in electric vehicle aggregator (PEVA) using indirect load control in the day-ahead energy market, which is generally formulated as bilevel programming. However, this bilevel model is nonconvex and intractable due to the complementary constraints imposed to preclude plug-in electric vehicles (PEVs) from simultaneously charging and discharging. To handle this problem, an exact relaxation method is first proposed to remove the complementary constraints of the PEVs' charging and discharging behaviours. To further reduce the complexity of the model, a method is proposed for the exact relaxation of the complementary slackness of Karush-Kuhn-Tucker (KKT) conditions in the reformulated mathematical program with equilibrium constraints. By removing the complementary constraints, the relaxation methods can effectively reduce the complexity of the model and improve the computational performance. The results of the case study demonstrate the exactness and efficiency of the proposed relaxation methods. Specifically, in the test case with 1000 PEVs and 20 scenarios, 220,000 constraints are removed by the two exact relaxation methods. Moreover, using the second exact relaxation method, in the test case with 400 PEVs and 60 scenarios and the case with 1000 PEVs and 60 scenarios, the computational time drops by 30.3% and 23.3%, respectively.

关键词： battery powered vehicles KKT condition PEVA electric vehicle charging power markets plug-in electric vehicle aggregator concave programming equilibrium constraints mathematical programming tendering PEV charging-discharging behaviour Karush-Kuhn-Tucker condition bidding strategy relaxation methods nonconvex model day-ahead energy market bilevel programming complementary constraints mathematical program

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Multi-period design of hybrid microgrid under multi-timescale uncertainties 22

Multi-period design of hybrid microgrid under multi-timescal...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Kim, Sunwoo Lee, Jay H. Korea Adv Inst Sci & Technol Daejeon 291 South Korea

ISBN: (纸本)9781713872344

This study focuses on the deployment planning of hybrid microgrid that uses energy storage and power-to-hydrogen (P2H) systems for combatting the variability and intermittency problem of renewable energy sources (RES). To consider the long-term uncertainties, such as those related to demand growth and installation costs, the deployment problem is formulated as a multi-period stochastic decision problem. Given that deployment decisions are intimately linked with how the system is operated against the shortterm variabilities of the energy generated from the RES, the overall decision problem is multi-scale in nature, and computation for solving the problem can quickly become exorbitant. To address this complexity, this study proposes a combination of reinforcement learning and mathematical programming in a complementary, hierarchical way. The proposed method is demonstrated through a case study, where deployment decisions over multiple decades are considered along with hourly operational decisions within each period under various uncertainty types. The deployment plan obtained using the suggested method shows an 11% improvement in the economic performance compared to the two-stage stochastic programming (2SSP) approach. Copyright (c) 2023 The Authors.

关键词： Multi period design hybrid microgrid multi timescale uncertainties mathematical programming Deep Q Network power to hydrogen

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Design of dynamic active-passive beamforming for reconfigurable intelligent surfaces assisted hybrid VLC/RF communications

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IET COMMUNICATIONS 2022年第13期16卷 1531-1544页

作者： Han, Yufeng Xiao, Yue Zhang, Xiaonan Gao, Yulan Zhu, Qiaonan Dong, Binhong Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun 2006 Xiyuan Ave Chengdu Peoples R China Florida State Univ Dept Comp Sci Tallahassee FL 32306 USA

The hybrid visible light communication (VLC)/radio frequency (RF) communications are investigated with the aid of reconfigurable intelligent surfaces (RISs) in dynamic wireless networks, where the RIS access selection processes of VLC/RF users are updated depending on the channel quality dynamically. Specifically, a dynamic optimization problem due to the mobility of users and time-varying selection strategy is formulated. Under the constraints of the average minimum rate for VLC/RF users and the maximum transmit power constraints for VLC/RF access points (APs), the target is to minimize the average long-term power consumption, by jointly considering the active beamforming at APs and the passive beamforming at RISs. Based on the Lyapunov optimization framework and the drift-plus-penalty (DPP) algorithm, the original optimization problem is transformed into corresponding short-term problems at each frame. Furthermore, the closed form solutions with active-passive beamforming are derived using the fractional programming method based on the Lagrangian dual theory. Finally, numerical results demonstrate the convergence and effectiveness of the proposed optimization algorithm.

关键词： average long-term power consumption VLC/RF access points drift-plus-penalty algorithm time-varying selection strategy wireless channels Lyapunov optimization framework telecommunication power management dynamic wireless networks array signal processing dynamic optimization problem RIS access selection processes dynamic active-passive beamforming original optimization problem free-space optical communication short-term problems reconfigurable intelligent surfaces mathematical programming maximum transmit power constraints average minimum rate fractional programming method active beamforming Lagrangian dual theory

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Improving Heuristic Process Discovery Methods Through Determining the Optimal Split/Join Patterns of Dependency Graphs

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IEEE ACCESS 2022年 10卷 1116-1131页

作者： Tavakoli-Zaniani, Maryam Gholamian, Mohammad Reza Iran Univ Sci & Technol Sch Ind Engn Tehran 16844 Iran

Identifying the split and join patterns of dependency graphs is an essential step in Heuristics Mining process discovery methods. The existing methods determine the split/join patterns (consisting of AND and XOR relations) according to the event log information about the activities involved in the splits and joins. Hence, they neglect the event log information available for the other activities on the paths from split points to join points. On the other hand, the current methods determine the patterns of each split/join separately and do not aim to select the best set of patterns. Therefore, the outputs of the existing methods can be non-optimal. Furthermore, the current methods cannot guarantee that there is a matching And-join for each AND-split, and vice versa. This can make some split/join patterns incapable of being activated. To handle these issues, this paper, for the first time, presents an integer linear programming model which identifies the optimal patterns of splits/joins with regard to all succession information that is available in the event log;simultaneously, it ensures that for each AND-split there is at least one matching AND-join, and vice versa. The objective function of the proposed model is inspired by replay fitness and precision dimensions of process model quality. According to the assessments, the process models obtained by the proposed method are superior to the results of the most prominent methods of determining split/join patterns in terms of replay fitness, precision, simplicity, and matching AND-splits with AND-joins.

关键词： Concurrent computing Pattern matching mathematical programming Linear programming Licenses Integer linear programming Volume measurement Heuristics Miner integer linear programming process discovery process mining mining split join patterns optimization

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Integrated Backup Rolling Stock Allocation and Timetable Rescheduling with Uncertain Time-Variant Passenger Demand Under Disruptive Events

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INFORMS JOURNAL ON COMPUTING 2022年第6期34卷 3234-3258页

作者： Yin, Jiateng Yang, Lixing D'Ariano, Andrea Tang, Tao Gao, Ziyou Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Univ Roma Tre Dept Engn I-00154 Rome Italy

Railway traffic management focuses on regulating train movements and deliv-ering improved service quality to passengers;however, such efforts are subject to many uncertainties in terms of disruptions and passenger demand on a rail transit line. In con-trast to most existing studies, which focus on the rescheduling of passenger timetables in a deterministic framework, this study proposes a two-stage stochastic optimization model for allocating backup rolling stocks (BRS) to storage lines to reschedule the timetable and serve passengers delayed by disruptions. The first stage is an assignment problem to deter-mine the optimal plan for the allocation of BRS to storage lines to achieve a good trade-off between the investment cost for the BRS and the expected travel time of delayed passen-gers across different stochastic scenarios. The second stage is explicitly formulated as a net-work flow model to optimize the timetable of the delayed trains on the tracks and the BRS from the storage lines such that the passenger travel time is minimized under each stochas-tic scenario. To improve the efficiency of convergence, we develop an improved L-shaped method with several accelerating techniques. Among these, we show that the classical inte-ger L-shaped cut can be tightened given the property of the second-stage problem, which can also be generalized to other two-stage integer stochastic programs. Real-world case studies based on historical data from the Beijing metro verify the effectiveness of the pro-posed approach in reducing the travel time for passengers.

关键词： mathematical programming backup rolling stock allocation train rescheduling two-stage stochastic optimization Benders decomposition

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A mathematical model for line-seru conversion and scheduling problem in seru production system

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2022年第3期37卷 1213-1223页

作者： Furugi, Ahad Haliloglu, Melis Ondokuz Mayis Univ Fac Engn Ind Engn Dept TR-55139 Samsun Turkey

In today's competitive environment, businesses need to respond quickly and with high efficiency to the diverse and personal demands of customers in order to increase their competitiveness. In order to increase efficiency and flexibility in production systems, different production systems such as mass production (assembly line) produced by the American Ford company, Toyota Production System (TPS) developed by Toyota in Japan and cellular production system (CM) based on group technology (GT) have been developed. However, productivity is dropped in the assembly line and Toyota production system due to dynamic demands, short life cycle of products and high diversity. Japanese companies such as Sony and Canon have developed a Japanese cellular manufacturing system to meet market demands and to distinguish it from the traditional CM, they have called this production system a seru production system. There have been few studies in the literature on the seru production system. Especially in the Turkish literature, there are only two studies that considered seru production systems. With this aspect, this study is aimed to fill this gap in the literature. In this study, a comprehensive literature review has been conducted. In addition, a mathematical model is proposed for the conversion of the assembly line to the seru production system (line-seru conversion) and scheduling problems that minimize total tardiness. The GAMS package program is used to solve the model and the results are analyzed.

关键词： Seru production system line-seru conversion problem scheduling mathematical programming

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