Asia Pac. J. Oper. Res.最新文献_第3页

Maximum Entropy Bi-Objective Model and its Evolutionary Algorithm for Portfolio Optimization 投资组合优化的最大熵双目标模型及其进化算法

Asia Pac. J. Oper. Res. Pub Date : 2022-04-11 DOI: 10.1142/s0217595922500142

Chun-an Liu, Qian Lei, H. Jia

{"title":"Maximum Entropy Bi-Objective Model and its Evolutionary Algorithm for Portfolio Optimization","authors":"Chun-an Liu, Qian Lei, H. Jia","doi":"10.1142/s0217595922500142","DOIUrl":"https://doi.org/10.1142/s0217595922500142","url":null,"abstract":"Diversification of investment is a well-established practice for reducing the total risk of investing. Portfolio optimization is an effective way for investors to disperse investment risk and increase portfolio return. Under the assumption of no short selling, a bi-objective minimizing portfolio optimization model, in which the first objective is a semi-absolute deviation mean function used to measure the portfolio risk, and the second objective is a maximum entropy smooth function used to measure the portfolio return, is given in this paper. Also, a maximum entropy multi-objective evolutionary algorithm is designed to solve the bi-objective portfolio optimization model. In order to obtain a sufficient number of uniformly distributed portfolio Pareto optimal solutions located on the true Pareto frontier and fully exploit the useful asset combination modes which can lead the search process toward the frontier direction quickly in the objective space, a subspace multi-parent uniform crossover operator and a subspace decomposition mutation operator are given. Furthermore, a normalization method to deal with the tight constraint and the convergence analysis of the proposed algorithm are also discussed. Finally, the performance of the proposed algorithm is verified by five benchmark investment optimization problems. The performance evaluations and results analyses illustrate that the proposed algorithm is capable of identifying good Pareto solutions and maintaining adequate diversity of the evolution population. Also, the proposed algorithm can obtain faster and better convergence to the true portfolio Pareto frontier compared with the three state-of-the-art multi-objective evolutionary algorithms. The result can also provide optimal portfolio plan and investment strategy for investors to allocate and manage asset effectively.","PeriodicalId":8478,"journal":{"name":"Asia Pac. J. Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73343600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scheduling High Multiplicity Jobs on Parallel Multi-Purpose Machines with Setup Times and Machine Available Times 具有设置时间和机器可用时间的并行多用途机器上的高多重作业调度

Asia Pac. J. Oper. Res. Pub Date : 2022-04-05 DOI: 10.1142/s0217595922500129

Caixia Jing, Wanzhen Huang, Lei Zhang, Heng Zhang

引用次数: 0

An Output-Space Based Branch-and-Bound Algorithm for Sum-of-Linear-Ratios Problem 线性比率和问题的基于输出空间的分支定界算法

Asia Pac. J. Oper. Res. Pub Date : 2022-03-24 DOI: 10.1142/s0217595922500105

Bo Zhang, Yuelin Gao

引用次数: 3

Multiobjective Symmetric Duality in Higher-Order Fractional Variational Programming 高阶分数阶变分规划中的多目标对称对偶性

Asia Pac. J. Oper. Res. Pub Date : 2022-03-10 DOI: 10.1142/s0217595922500087

Arshpreet Kaur, M. K. Sharma, I. Ahmad

引用次数: 0

Online Batch Scheduling of Incompatible Job Families with Variable Lookahead Interval 可变前瞻间隔不兼容作业族的在线批调度

Asia Pac. J. Oper. Res. Pub Date : 2022-03-08 DOI: 10.1142/s0217595922400127

Wenhua Li, Libo Wang, Hang Yuan

引用次数: 0

Critical Interactive Risks in Project Portfolios from the Life Cycle Perspective 从生命周期角度看项目组合中的关键交互风险

Asia Pac. J. Oper. Res. Pub Date : 2022-03-08 DOI: 10.1142/s0217595922500075

L. Bai, Jiale Liu, Ning Huang, Kanyin Zheng, Tingting Hao

{"title":"Critical Interactive Risks in Project Portfolios from the Life Cycle Perspective","authors":"L. Bai, Jiale Liu, Ning Huang, Kanyin Zheng, Tingting Hao","doi":"10.1142/s0217595922500075","DOIUrl":"https://doi.org/10.1142/s0217595922500075","url":null,"abstract":"The need for enterprises to manage project portfolio risks over the life cycle has become increasingly prominent. It is essential to evaluate and manage them to achieve project portfolios and organizations’ success. Unlike project risk, project portfolio risk is more complex and uncertain due to risk interactions. Risk management is unsatisfactory in project portfolios due to the lack of awareness of risk interactions and the life cycle. The purpose of this paper is to identify the critical risks of project portfolios over the life cycle considering risk interactions. We primarily verified 20 identified risks through a questionnaire survey and an expert interview method and evaluated the interactions among them using the Delphi method. Furthermore, risk interactions were analyzed using the social network analysis (SNA) methodology to determine the important risks. Finally, a comprehensive evaluation of important risks was carried out to identify critical risks according to the evaluation principles. The results identified six critical portfolio risks, two key risk contagion paths and revealed risk characteristics of different life cycle phases. This research considerably contributes to the body of knowledge pertaining to project portfolio management that will enable organizations that implement project portfolios and similar multi projects to emphasize critical risks.","PeriodicalId":8478,"journal":{"name":"Asia Pac. J. Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79071876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Location Problems with Cutoff 有切断的位置问题

Asia Pac. J. Oper. Res. Pub Date : 2022-03-02 DOI: 10.1142/s0217595922500452

Raoul Müller, A. Schöbel, Dominic Schuhmacher

引用次数: 0

Scalarization and Optimality Conditions of E-Globally Proper Efficient Solution for Set-Valued Equilibrium Problems 集值平衡问题e -全局固有有效解的标化及最优性条件

Asia Pac. J. Oper. Res. Pub Date : 2022-02-28 DOI: 10.1142/s0217595922500099

Zhiang Zhou, Min Kuang

引用次数: 0

Parallel-Machine Scheduling with Step-Deteriorating Jobs to Minimize the Total (Weighted) Completion Time 具有逐步退化作业的并行机器调度以最小化总(加权)完成时间

Asia Pac. J. Oper. Res. Pub Date : 2022-02-14 DOI: 10.1142/s0217595922400115

Cuixia Miao, Fanyu Kong, Juan Zou, Ran Ma, Yujia Huo

引用次数: 0

Effective Heuristic Techniques for Combined Robust Clustering Problem 组合鲁棒聚类问题的有效启发式技术

Asia Pac. J. Oper. Res. Pub Date : 2022-02-07 DOI: 10.1142/s0217595922400097

Yunhe Xu, Chenchen Wu, Ling Gai, Lu Han

{"title":"Effective Heuristic Techniques for Combined Robust Clustering Problem","authors":"Yunhe Xu, Chenchen Wu, Ling Gai, Lu Han","doi":"10.1142/s0217595922400097","DOIUrl":"https://doi.org/10.1142/s0217595922400097","url":null,"abstract":"Clustering is one of the most important problems in the fields of data mining, machine learning, and biological population division, etc. Moreover, robust variant for [Formula: see text]-means problem, which includes [Formula: see text]-means with penalties and [Formula: see text]-means with outliers, is also an active research branch. Most of these problems are NP-hard even the most classical problem, [Formula: see text]-means problem. For the NP-hard problems, the heuristic algorithm is a powerful method. When the quality of the output can be guaranteed, the algorithm is called an approximation algorithm. In this paper, combining two types of robust settings, we consider [Formula: see text]-means problem with penalties and outliers ([Formula: see text]-MPO). In the [Formula: see text]-MPO, we are given an [Formula: see text]-point set [Formula: see text], a penalty cost [Formula: see text] for each [Formula: see text], an integer [Formula: see text], and an integer [Formula: see text]. The target is to find a center subset [Formula: see text] with [Formula: see text], a penalty subset [Formula: see text] and an outlier subset [Formula: see text] with [Formula: see text], such that the sum of the total costs, including the connection cost and the penalty cost, is minimized. We offer an approximation algorithm using a heuristic local search scheme. Based on a single-swap manipulation, we obtain [Formula: see text]-approximation algorithm.","PeriodicalId":8478,"journal":{"name":"Asia Pac. J. Oper. Res.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80423388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0