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Drawdown minimization in asset portfolio selection: MINLP models and efficient cross-entropy algorithm

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第0期

作者： Bayat, M. Hooshmand, F. Mirhassani, S. A. Amirkabir Univ Technol Tehran Polytech Dept Math & Comp Sci Tehran Iran

Portfolio management is an important research topic in finance and optimization. Drawdown as one of the measures in evaluating portfolios indicates the relative difference between the portfolio value in the current moment and its maximum value during a given time interval in the recent past. In this paper, first, the importance of this measure is discussed and then two mixed-integer nonlinear programming (MINLP) models with the objectives of minimizing the expected drawdown and the maximum drawdown under real-world constraints are presented. Due to the NP-hardness of this problem, by utilizing the problem structure, an efficient cross-entropy-based algorithm is presented to solve it. An effective mechanism is suggested to calibrate the algorithm parameters. Computational results confirm the performance of the proposed algorithm from both solution quality and running time in comparison with MINLP solvers.

关键词： portfolio selection problem drawdown real-world constraints cross-entropy algorithm

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Scenario-based stochastic model and efficient cross-entropy algorithm for the risk-budgeting problem

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ANNALS OF OPERATIONS RESEARCH 2024年第2-3期341卷 731-755页

作者： Bayat, M. Hooshmand, F. Mirhassani, S. A. Amirkabir Univ Technol Dept Math & Comp Sci Tehran Polytech Tehran Iran

Risk budgeting is one of the most recent and successful approaches for the portfolio selection problem. Considering mean-standard-deviation as a risk measure, this paper addresses the risk budgeting problem under the uncertainty of the covariance matrix and the mean vector, assuming that a finite set of scenarios is possible. The problem is formulated as a scenario-based stochastic programming model, and its stability is examined over real-world instances. Then, since investing in all available assets in the market is practically impossible, the stochastic model is extended by incorporating the cardinality constraint so that all selected assets have the same risk contribution while maximizing the expected portfolio return. The extended problem is formulated as a bi-level programming model, and an efficient hybrid algorithm based on the cross-entropy is adopted to solve it. To calibrate the algorithm's parameters, an effective mechanism is introduced. Numerical experiments on real-world datasets confirm the efficiency of the proposed models and algorithm.

关键词： Portfolio selection problem Risk budgeting problem Stochastic bi-level model Cardinality constraint cross-entropy algorithm

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Scheduling Study of Stereo Garage Access Problem Based on Hybrid cross-entropy algorithm 4

Scheduling Study of Stereo Garage Access Problem Based on Hy...

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4th International Conference on Robotics, Control and Automation Engineering (RCAE) / 4th International Conference on Advanced Mechanical and Electrical Engineering (AMEE)

作者： Chen, Yangjun Yi, Guohong Li, Jingfang Wuhan Inst Technol Sch Comp Sci & Engn Wuhan Peoples R China Sdic Xinjiang Luobupo Potash Co Ltd Enterprise Management Dept Urumqi Xinjiang Peoples R China

ISBN: (纸本)9781665427302

In this paper, an improved cross-entropy hybrid genetic algorithm (CE-GA) is modeled and proposed for the access efficiency problem under multi-machine collaboration in a new air-entry type stereo garage. The algorithm is further optimized by optimizing the initial population and extracting elite samples to ensure that the solution space satisfies the constraints, using the crossover and variation operators of the genetic algorithm to avoid premature convergence, and introducing adaptive factors for task distribution and conflict frequency. Experimental comparisons with the widely used genetic algorithm and its improved algorithms were conducted, and better results were achieved to improve the garage access efficiency.

关键词： stereo garage genetic algorithm cross-entropy algorithm multi-machine collaborative schedule

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The sorted-waste capacitated location routing problem with queuing time: A cross-entropy and simulated-annealing-based hyper-heuristic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期201卷 1页

作者： Shang, Chunjian Ma, Liang Liu, Yong Sun, Shuo Univ Shanghai Sci & Technol Sch Management Shanghai Peoples R China

Waste sorting is an imperative and significant issue in China, of which sorted-waste collection and transportation are indispensable parts. Despite its vital yet practical significance, few studies research mathematical models or algorithms of waste collection and transportation from the perspective of waste sorting. To address this issue, we extend a novel transportation model for the waste management system, namely, capacitated location routing problem with queuing time (CLRPQT) and design a cross-entropy and simulated-annealing based hyper-heuristic algorithm (CE-SAHH) for it. The main idea of this paper is three-fold: (1) As a particular property of this problem, source nodes cannot but need to be served by more than one vehicle that causes queuing time between a heterogeneous fleet of vehicles, which is novel in terms of the proposed model;(2) For the methodological contribution, a character encoding scheme, new decoding procedure, and local search strategy are designed embedded in the proposed method;(3) An integration of simulated annealing strategy and the cross-entropy-based hyper-heuristic algorithm is developed to overcome the combinatorial optimization problem with a more complex solution of this study. Finally, the results and analysis of three numeric experiments on benchmark datasets, new instances of CLRPQT, and simulation data in Shanghai, China, verify the effectiveness and universality of the proposed model and method.

关键词： Capacitated location routing problem Sorted-waste collection and transportation Hyper-heuristic algorithm cross-entropy algorithm Simulated-annealing scheme

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Model-Free Optimization and Experimental Validation of RIS-Assisted Wireless Communications Under Rich Multipath Fading

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第3期13卷 627-631页

作者： Chen, Tianrui You, Minglei Zhang, Yangyishi Zheng, Gan Gros, Jean Baptiste Lerosey, Geoffroy Nasser, Youssef Burton, Fraser Gradoni, Gabriele Univ Nottingham Dept Elect & Elect Engn Nottingham NG7 2RD England British Telecommun PLC Appl Res Ipswich IP5 3RE England Univ Warwick Sch Engn Coventry CV4 7AL England Greenerwave F-75002 Paris France Univ Surrey Inst Commun Syst Guildford GU2 7XH England

Reconfigurable intelligent surface (RIS) devices have emerged as an effective way to control the propagation channels for enhancing the end-users' performance. However, RIS optimization involves configuring the radio frequency response of a large number of radiating elements, which is challenging in real-world applications due to high computational complexity. In this letter, a model-free cross-entropy (CE) algorithm is proposed to optimize the binary RIS configuration for improving the signal-to-noise ratio (SNR) at the receiver. One key advantage of the proposed method is that it only requires system performance indicators, e.g., the received SNR, without the need for channel models or channel state information. Both simulations and experiments are conducted to evaluate the performance of the proposed CE algorithm. This letter provides an experimental demonstration of the channel hardening effect in a multi-antenna RIS-assisted wireless system under rich multipath fading.

关键词： Optimization Signal to noise ratio Wireless communication Numerical models Antennas Transceivers Receivers Reconfigurable intelligent surface (RIS) phase shifts optimization cross-entropy algorithm channel hardening

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A decision making framework for joint replenishment and delivery scheduling problems under mixed uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Wang, Guang Zhou, Jian Pantelous, Athanasios A. Liu, Yuanyuan Li, Youwei Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China Univ Bahrain Confucius Inst Zallaq Bahrain Monash Univ Monash Business Sch Dept Econometr & Business Stat 20 Chancellors WalkWellington Rd Clayton Vic 3800 Australia Shangdong Univ Finance & Econ Sch Management Sci & Engn Jinan 250014 Peoples R China Univ Hull Hull Univ Business Sch Cottingham Rd Kingston Upon Hull HU6 7RX England 99 Shangda Rd Shanghai Peoples R China

Concerning the essence of risk, a joint replenishment and delivery scheduling problem with fuzzy cost -related parameters and random number of imperfect quality items is developed to make it suitable for the inherent uncertainties of procurement-shipment process. The mathematical modelling-based decision system is formulated as a chance-constrained programming with the idea of embedding decision makers' risk tolerance. Following this notion, the model is translated into an equivalent non-linear counterpart and a neighbourhood heuristic search is designed based on the properties of the cost function. We introduce an integrated cross -entropy algorithm, incorporating the heuristic in the cross-entropy framework, to solve it. The numerical results demonstrate that ICE is quite effective in comparison to state-of-the-art algorithms. Our framework is helpful for decision makers to determine economically acceptable performance objectives in the presence of uncertain issues, and thus to build resilience in supply chain.

关键词： Supply chain resilience Joint replenishment Mixed uncertainty Chance-constrained programming cross-entropy algorithm

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Data-driven efficient reliability evaluation of power systems with wind penetration: an integrated GANs and CE method

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第4期14卷 577-584页

作者： Zhuang, Xinran Ye, Chengjin Ding, Yi Cheng, Lin Song, Yonghua Ye, Shuiquan Tian, Shiming Chen, Rong Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China Tsinghua Univ Coll Elect Engn Beijing Peoples R China Hangzhou Runpaq Technol Co Ltd Hangzhou Peoples R China China Elect Power Res Inst Beijing Peoples R China State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China

The conventional Monte Carlo simulation may not be efficient enough for reliability evaluation of composite power systems. The cross-entropy (CE) algorithm is a promising state-of-the-art fast sampling method, while it has not been well developed in this field due to the implicit probability distributions of penetrated renewable energies. Specifically, the CE sampling requires the distributions of interest to be explicit and parametric, while some preconceived probabilistic distribution functions (PDFs) such as the Weibull distribution of wind speed make the results to deviate from the reality sometimes. In this study, a data-driven efficient approach for reliability evaluation of power systems with wind penetration is proposed utilising generative adversarial networks (GANs) and CE sampling. The distributions of wind speeds in multiple wind farms are estimated by GANs considering their spatial correlation without any prior knowledge. With the trained generative network mapping from the explicit Gaussian noise to the raw wind speed data, the CE sampling is successfully enabled to efficiently sample the system states with implicit PDFs, which are associated with wind speeds and component failures. A real wind speed dataset and the RTS testing system are utilised to verify the proposed integrated method, including the accuracy of distribution estimation and reliability evaluation result, as well as the speed-up efficiency of sampling.

关键词： Monte Carlo methods probability entropy Weibull distribution sampling methods wind power plants power generation reliability neural nets power engineering computing Gaussian noise data-driven efficient reliability evaluation wind penetration Monte Carlo simulation composite power systems cross-entropy algorithm implicit probability distributions renewable energies CE sampling preconceived probabilistic distribution functions Weibull distribution data-driven efficient approach generative adversarial networks multiple wind farms generative network mapping raw wind wind speed dataset RTS testing system distribution estimation integrated GAN method power system reliability wind speed distribution spatial correlation explicit Gaussian noise reliability evaluation

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Robust network design for sustainable-resilient reverse logistics network using big data: A case study of end-of-life vehicles

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2021年 149卷 102279-102279页

作者： Govindan, Kannan Gholizadeh, Hadi Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 201306 Peoples R China Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Southern Denmark Danish Inst Adv Study Dept Technol & Innovat Ctr Sustainable Supply Chain Engn Campusvej 55 Odense M Denmark Univ Laval Dept Genie Mecan Quebec City PQ Canada

With new global regulations on supply chains (SCs), sustainable regulation mechanisms have become subject to controversy. The intention is to create and expand green and sustainable supply chains (SSC) to meet environmental and economic standards and to boost one's position in competitive markets. This study examines the resilient sustainable reverse logistics network (RLN) process for end-of-life vehicles (ELVs) in Iran. We pursue both actual and uncertain situations that possess big data characteristics (3 V's) in information between facilities of the proposed reverse logistics (RL), and we consider recycling technology due to its societal impacts. Due to unpredictable environmental and social factors, the various proposed network facilities may not utilize their full capacity, so we also consider situations in which the network facility capacity is disrupted. Our primary objective is to minimize the total cost of the resilient sustainable RLN. For most parameters, finding the best solution through traditional methods is time-consuming and costly. Hence, to enhance decision-making power, the value of model parameters in each scenario is considered. A cross-entropy (CE) algorithm with basic scenario concepts is used in robust model optimization. The results demonstrate that changing the scenario situation significantly impacts optimal environmental and social costs. In particular, when the situation is "pessimistic," environmental impact costs are at their highest levels. Hence, scenario-based modeling of the network is a good approach to implement under uncertainty conditions. On the other hand, results show that cost savings for organizations are achieved through optimal planning of the centers' capacity to save cost, increase services, and ensure effective government response to cost-effective and instrumental market competition.

关键词： Resilient sustainable reverse logistics network Robust optimization cross-entropy algorithm Big data ELVs

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An efficient algorithm for multi-modal skyport location problem with choice-based demand

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Socio-Economic Planning Sciences 2025年 100卷

作者： Kim, Gwang Jeong, Yoonjea Shin, Youngchul Division of Business Administration Chosun University Gwangju 61452 South Korea Division of Systems Management and Safety Engineering Pukyong National University Busan 48513 South Korea Department of Industrial Engineering Ajou University Suwon-si Gyeonggi-do 16499 South Korea

This study presents the operation of urban air mobility (UAM), an innovative transportation technology aimed at alleviating traffic congestion in cities. A key consideration for the successful introduction and efficient operation of the transportation is to decide on locations of skyports, which are stations for vertical take-off and landing of air mobility. In this study, we cover a skyport location problem incorporating choice probabilities of transportation modes for each user by applying a binary logit model. The choice probabilities are predicted by considering factors such as fare, travel time, and social acceptance. We propose two mathematical formulations associated with the skyport location problem;one is to maximize the proportion of users who choose the UAM service and the other is to minimize the total sum of the travel time. The two models are NP-hard problems, necessitating an effective and efficient solution methodology for the problem. This study presents a methodology based on a cross-entropy algorithm, which is known for efficiently solving combinatorial optimization problems. We also validate the good performances of the solution methodology through numerical experiments. © 2025

关键词： cross-entropy algorithm Discrete choice model Skyport location problem Urban air mobility

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Parameter Design and Performance Analysis of an Improved MOCEO algorithm

Parameter Design and Performance Analysis of an Improved MOC...

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15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhao Duo Huang Chenxi Tang Qichao Zhang Xiaying Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151694

In order to solve the multi-objective optimization problem, this paper proposes a multi-objective cross-entropy optimization (MOCEO) algorithm based on the original single-objective cross-entropy (CE) optimization algorithm. Situations, with a low probability for optimal point, and also, locations with a high probability to fall into local optimum after tested with standard test function ZDT4 and ZDT6 problems. The algorithm is then introduced an improved method called disturbance, including recombination, variance disturbance and vary ing population size. Each operation contains a variable parameter. Appropriate selection of parameters can maximize the optimization ability. A set of optimal parameters is designed and the answers are verified by a comparative study with other meta-heuristic optimization algorithms such as NSCA-II, SPEA2, MOEA/D and PAES in similar conditions. The results indicate that those improvements are effective and the algorithm proposed in this paper is superior to other algorithms. It has the advantages of strong searching ability and high robustness which is applicable to challenging difficulties with unknown search spaces.

关键词： cross-entropy algorithm Evolutionary algorithm Multi-objective Optimization Parameter design

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