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An uncertain single machine scheduling problem with periodic maintenance

KNOWLEDGE-BASED SYSTEMS 2018年 144卷 32-41页

作者： Shen, Jiayu Zhu, Kai Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Jiangsu Peoples R China Jiangsu Univ Technol Sch Automobile & Traff Engn Changzhou 213001 Jiangsu Peoples R China

This paper studies a single machine scheduling problem with periodic maintenance, in which processing time and repair time are nondeterministic. In order to deal with nondeterministic phenomena, uncertainty theory is introduced to minimize the makespan under an uncertain environment. Three uncertain programming models are proposed, which can be converted into deterministic forms based on the uncertainty inverse distribution. List scheduling (LS) and longest processing time (LPT) algorithms are employed to solve the problem. It is proved that the two algorithms have the same worst cast ratio under different confidence levels and the LPT algorithm has a better performance bound. A hybrid intelligent algorithm for the problem is designed and some numerical experiments demonstrate the effectiveness of the proposed models and algorithm. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Single machine Periodic maintenance Longest processing time algorithm Worst-case ratio hybrid intelligent algorithm

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A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第2期222卷 341-349页

作者： Zhang, Wei-Guo Liu, Yong-Jun Xu, Wei-Jun S China Univ Technol Sch Business Adm Guangzhou 510641 Peoples R China

This paper deals with a multi-period portfolio selection problem with fuzzy returns. A possibilistic mean-semivariance-entropy model for multi-period portfolio selection is presented by taking into account four criteria viz., return, risk, transaction cost and diversification degree of portfolio. In the proposed model, the return level is quantified by the possibilistic mean value of return, the risk level is characterized by the lower possibilistic semivariance of return, and the diversification degree of portfolio is measured by the originally presented possibilistic entropy. Furthermore, a hybrid intelligent algorithm is designed to obtain the optimal portfolio strategy. Finally, the comparison analysis between the possibilistic entropy model and the proportion entropy model is provided by two numerical examples to illustrate the efficiency of the proposed approaches and the designed algorithm. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Finance Multi-period portfolio selection Possibilistic semivariance Possibilistic entropy hybrid intelligent algorithm

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Reliability based assignment in stochastic-flow freight network

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APPLIED MATHEMATICS AND COMPUTATION 2009年第1期211卷 85-94页

作者： Xu, Wangtu He, Shiwei Song, Rui Li, Juan Beijing Jiaotong Univ Sch Traff & Transportat Dept Transportat Management Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Dept Traff Engn Beijing 100044 Peoples R China

Based on the reliability of transportation time, a transportation assignment model of stochastic-flow freight network is designed in this paper. This transportation assignment model is built by mean of stochastic chance-constraint programming and solved with a hybrid intelligent algorithm (HIA) which integrates genetic algorithm (GA), stochastic simulation (SS) and neural network (NN). GA is employed to report the optimal solution as well as the optimal objective function values of the proposed model. SS is used to simulate the value of uncertain system reliability function. The uncertain function approximated via NN is embedded into GA to check the feasibility and to compute the fitness of the chromosomes. These conclusions have been drawn after a test of numerical case using the proposed formulations. System reliability, total system cost and flow on each path would finally reach at their own convergence points. Increase of the system reliability causes increase of the total time cost. The system reliability and the total time cost converge at a possible Nash Equilibrium point. (C) 2009 Elsevier Inc. All rights reserved.

关键词： System reliability Stochastic-flow network Transportation assignment Chance-constraint programming hybrid intelligent algorithm

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SIMULATED ANNEALING AND GENETIC algorithm BASED METHOD FOR A BI-LEVEL SERU LOADING PROBLEM WITH WORKER ASSIGNMENT IN SERU PRODUCTION SYSTEMS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2021年第2期17卷 779-803页

作者： Luo, Lan Zhang, Zhe Yin, Yong Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Peoples R China Doshisha Univ Grad Sch Business Kamigyo Ku Kyoto 6028580 Japan

Seru production is one of the latest manufacturing modes arising from Japanese production practice. Sera can achieve efficiency, flexibility, and responsiveness simultaneously. To accommodate the current business environment with volatile demands and fierce competitions, seru has attracted more and more attention both from researchers and practitioners. A new planning management system, just-in-time organization system (JIT-OS), is used to manage and control a seru production system. The JIT-OS contains two decisions: seru formation and sera loading. By seru formation, a sera system with one or multiple appropriate serus is configured;by seru loading, customer ordered products are allocated to serus to implement production plans. In the process of sera formation, workers have to be assigned to serus. In this paper, a sera loading problem with worker assignment is constructed as a bi-level programming model, and the worker assignment on the upper level is to minimize total idle time while the lower level is to minimize the makespan by finding out optimal product allocation. A product lot can be splitted and allocated to different serus. The problem of this paper is shown to be NP-hard. Therefore, a simulated annealing and genetic algorithm (SA-GA) is developed. The SA is for the upper level programming and the GA is for the lower level programming. The practicality and effectiveness of the model and algorithm are verified by two numerical examples, and the results show that the SA-GA algorithm has good scalability.

关键词： Seru loading bi-level programming lot-splitting worker assignment hybrid intelligent algorithm

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Optimizing influence diffusion in a social network with fuzzy costs for targeting nodes

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2017年第5期8卷 819-826页

作者： Ni, Yaodong Shi, Qiaoni Wei, Zhiyuan Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China

Over the last decade, the problem of optimizing influence diffusion in a social network has drawn much attention. In this paper, we study the problem of minimizing the complete influence time in a social network where the cost for targeting each individual is with fuzzy uncertainty. By adopting three different decision criteria in the area of uncertain programming, we propose three decision models to characterize the problem we study. In view of the complexity of the problem, we design a hybrid intelligence algorithm to solve models, where fuzzy simulation technologies are integrated with a modified greedy algorithm. Finally, numerical experiments are preformed to show the effectiveness of the models and algorithm we propose.

关键词： Social network Fuzzy variable Influence diffusion hybrid intelligent algorithm

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A Novel PSOS-CGSA Method for State Estimation in Unbalanced DG-Integrated Distribution Systems

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IEEE ACCESS 2020年 8卷 113219-113229页

作者： Ullah, Zia Elkadeem, Mohamed R. Wang, Shaorong Radosavljevic, Jordan Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Tanta Univ Fac Engn Dept Elect Power & Machines Engn Tanta 31521 Egypt Univ Pristina Kosovska Mitrovica Fac Tech Sci Kosovska Mitrovica 38220 Serbia

This paper proposes a novel optimization method, namely, hybrid PSOS-CGSA for state estimation in three-phase unbalanced DG-integrated distribution systems. The distribution system state estimation (DSSE) is formulated as a nonlinear optimization problem with constraints where loads and DG outputs are considered as the control variables, while real-time measurements are treated as dependent variables. The proposed DSSE model estimates the loads and DG outputs at each bus by minimizing the difference between the measure and calculated values of the variables monitored in real-time. A novel hybrid algorithm of particle swarm optimization with sigmoid-based acceleration coefficients and chaotic gravitational search algorithm (PSOS-CGSA) is proposed and applied for the DG-integrated DSSE. The feasibility of the proposed approach is verified on the IEEE 13-bus test system, the IEEE 37-bus test system, and the IEEE 123-bus test system. These simulations show that the proposed DSSE model provides reliable and accurate state estimation of DG-integrated distribution systems with a very limited number of real-time measurements at the source substation. The results obtained by proposed hybrid PSOS-CGSA are evaluated by comparing with other methods under the same test conditions, and the obtained results demonstrate the merits of the proposed scheme.

关键词： Distribution networks Real-time systems State estimation Load modeling Load flow Transmission line measurements Voltage measurement hybrid intelligent algorithm metaheuristic optimization state estimation unbalanced distribution system

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A new model and algorithm for uncertain random parallel machine scheduling problem

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SOFT COMPUTING 2019年第15期23卷 6555-6566页

作者： Ma, Weimin Liu, Yang Zhang, Xingfang Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China Liaocheng Univ Sch Math Sci Liaocheng 252059 Shandong Peoples R China

The paper presents a new model for uniform parallel machine scheduling problem with uncertainty and randomness simultaneously for processing times of jobs based on chance theory. The objective of the model is to minimize expected completion time. The constraint of the model is that uncertain random completion time of scheduling is less than or equal to expected completion time. The model is transformed to a crisp non-deterministic polynomial hard mathematical programming model based on chance theory. Firstly, simulation techniques of the objective function and the left chance constraint are proposed. Then, two heuristic methods to solve the crisp model are presented. Finally, they are integrated into two hybrid intelligent algorithms for searching the quasi-optimal schedule. Besides, the effectiveness of the model and its hybrid intelligent algorithms are verified by a numerical example generated randomly.

关键词： Parallel machine scheduling Uncertainty theory Chance theory Uncertain random programming hybrid intelligent algorithm

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Model and algorithm of fuzzy joint replenishment problem under credibility measure on fuzzy goal

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KNOWLEDGE-BASED SYSTEMS 2013年 39卷 57-66页

作者： Wang, Lin Fu, Qing-Liang Lee, Chi-Guhn Zeng, Yu-Rong Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada Hubei Univ Econ Sch Informat Management Wuhan 430205 Hubei Peoples R China

The joint replenishment problem (JRP) has received considerable attention and all of the work on JRP is under explicit environment. In fact, the decision makers often have to face vague operational conditions. In this paper, a novel JRP model with fuzzy minor replenishment cost and fuzzy inventory holding cost is developed. More concisely, this model is a fuzzy dependent-chance programming (DCP) model. Subsequently, the technique of the traditional fuzzy simulation (FS) approach and differential evolution algorithm (DE) are integrated to design a hybrid intelligent algorithm named FSDE-I to solve this practical fuzzy JRP. Thirdly, another intelligent algorithm named FSDE-II using an improved FS approach is proposed to estimate the credibility more precisely. Finally, FSDE-I and FSDE-II are illustrated with numerical examples and the results show the effectiveness of FSDE-II. Crown Copyright (c) 2012 Published by Elsevier B.V. All rights reserved.

关键词： Joint replenishment Dependent-chance programming Differential evolution algorithm Fuzzy simulation hybrid intelligent algorithm

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Expected value operator of random fuzzy variable and random fuzzy expected value models

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2003年第2期11卷 195-215页

作者： Liu, YK Liu, BD Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China

Random fuzzy variable is a mapping from a possibility space to a collection of random variables. This paper first presents a new definition of the expected value operator of a random fuzzy variable, and proves the linearity of the operator. Then, a random fuzzy simulation approach, which combines fuzzy simulation and random simulation, is designed to estimate the expected value of a random fuzzy variable. Based on the new expected value operator, three types of random fuzzy expected value models axe presented to model decision systems where fuzziness and randomness appear simultaneously. In addition, random fuzzy simulation, neural networks and genetic algorithm are integrated to produce a hybrid intelligent algorithm for solving those random fuzzy expected valued models. Finally, three numerical examples are provided to illustrate the feasibility and the effectiveness of the proposed algorithm.

关键词： fuzzy variable random fuzzy variable random fuzzy expected value model hybrid intelligent algorithm

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Bi-level programming model and hybrid genetic algorithm for flow interception problem with customer choice

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第11-12期57卷 1985-1994页

作者： Yang, Jun Zhang, Min He, Bo Yang, Chao Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China

This paper investigates how to optimize the facility location strategy such as to maximize the intercepted customer flow, while accounting for "flow-by" customers' path choice behaviors and their travel cost limitation. A bi-level programming static model is constructed for this problem. An heuristic based on a greedy search is designed to solve it. Consequently, we proposed a chance constrained bi-level model with stochastic flow and fuzzy trip cost threshold level. For solving this uncertain model more efficiently, we integrate the simplex method, genetic algorithm, stochastic simulation and fuzzy simulation to design a hybrid intelligent algorithm. Some examples are generated randomly to illustrate the performance and the effectiveness of the proposed algorithms. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： hybrid intelligent algorithm Location Path choice Simulation

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