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Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model

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COMPUTERS & INDUSTRIAL ENGINEERING 2015年 88卷 444-457页

作者： Dai, Zhuo Zheng, Xiaoting Jiu Jiang Univ Sch Elect Commerce Jiu Jiang City 332005 Jiangxi Peoples R China

The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integrates Monte Carlo simulation embedded hybrid genetic algorithm, fuzzy programming and chance-constrained programming jointly deal with the issue. A fuzzy programming and chance-constrained programming approach take up the uncertainty issue. Monte Carlo simulation embedded hybrid genetic algorithm is employed to determine the configuration of CLSC network. Parameters of GA are chosen to balance two aims. One aim is that the best value is global optimum, that is, maximum profit. The other aim is that the computational time is as short as possible. Non-parametric test confirms the advantage of hybrid GA. Then, the validity of Monte Carlo simulation embedded hybrid genetic algorithm is verified. The impacts of uncertainty in disposed rates, demands, and capacities on the overall profit of CLSC network are studied through sensitivity analysis. The proposed model is effective in designing CLSC network under uncertain environment. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Close-loop supply chain Network design Uncertainty Monte Carlo simulation embedded hybrid genetic algorithm fuzzy programming Chance-constrained programming

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Planning Water Resources Allocation Under Multiple Uncertainties Through a Generalized fuzzy Two-Stage Stochastic programming Method

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IEEE TRANSACTIONS ON fuzzy SYSTEMS 2015年第5期23卷 1488-1504页

作者： Fan, Yurui Huang, Guohe Huang, Kai Baetz, Brian W. Univ Regina Fac Engn Regina SK S4S 0A2 Canada Univ Regina Inst Energy Environm & Sustainabil Res UR NCEPU Regina SK S4S 0A2 Canada North China Elect Power Univ Inst Energy Environm & Sustainabil Res UR NCEPU Beijing 102206 Peoples R China McMaster Univ Dept Civil Engn Hamilton ON L8S 4L8 Canada

In this study, a generalized fuzzy two-stage stochastic programming (GFTSP) method is developed for planning water resources management systems under uncertainty. The developed GFTSP method can deal with uncertainties expressed as probability distributions, fuzzy sets, as well as fuzzy random variables. With the aid of a robust stepwise interactive algorithm, solutions for GFTSP can be generated by solving a set of deterministic submodels. Furthermore, the possibility information (expressed as fuzzy membership functions) can be reflected in the solutions for the objective function value and decision variables. The developed GFTSP approach is also applied to a water resources management and planning problem to demonstrate its applicability. Solutions of decision variables and objective function value are expressed as fuzzy membership functions, reflecting the fluctuating ranges of decision alternatives under different plausibilities. And thus, the water alternatives can be directly derived from the obtained fuzzy membership functions when the preferred a value is predefined by decision makers.

关键词： Decision making dual-uncertainty fuzzy programming planning water resources

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Charging Station Selection Optimization Based on Electric and Traffic Information

Charging Station Selection Optimization Based on Electric an...

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2017 IEEE Power & Energy Society General Meeting

作者： Zhou, Zhe Guo, Qinglai Sun, Hongbin Zhang, Kai Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Beijing Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing Peoples R China

ISBN: (纸本)9781538622124

With the increase of electric vehicles (EVs) and charging stations (CSs), how to maximize the benefits of all parties and maintain the efficiency and security of both power system and traffic network is an important research topic. To address these issues, we propose a game-theoretic price-setting framework to select the optimal CSs based on the total cost of EVs. To combine electric information, Security Index (SI) is innovatively introduced in the demand function, which impacts the price after game. SI is also used in distance calculation to revise the spatial distance to electrical distance. Besides, based on geographic information, route planning is achieved by revised Dijkstra algorithm. The simulation results demonstrate the effectiveness of the framework in balancing the load distribution compared with no SI-revised method.

关键词： Electric Vehicles Security Index Non-cooperative oligopoly game fuzzy programming

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Analysis of interval programming in different environments and its application to fixed-charge transportation problem

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DISCRETE MATHEMATICS ALGORITHMS AND APPLICATIONS 2017年第3期9卷

作者： Midya, Sudipta Roy, Sankar Kumar Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Midnapore 721102 W Bengal India

In this paper, we analyze the interval programming using interval and rough interval (RI). Interval programming is one of the tools to tackle uncertainty in mathematical programming. In the real world situations, we often encounter the cases where the data cannot be determined with certainty. So, the value of the data is assessed using an interval. Here, we consider that the parameters of the Fixed-Charge Transportation Problem (FCTP) are imprecise. In our proposed problem, uncertainties are considered in terms of interval and RI. We present a study of the FCTP using the interval programming. An example of the FCTP is included to show the effectiveness and usefulness of our proposed study. Finally, concluding remarks and future scopes of our paper are discussed.

关键词： Interval programming fuzzy programming rough interval fixed-charge transportation problem

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A fuzzy Model of Scenario Planning Based on the Credibility Theory and fuzzy programming

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International Conference on Mechatronics and Materials Processing (ICMMP 2011)

作者： Shi, Zhong Guo Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610054 Peoples R China

ISBN: (纸本)9783037852385

Uncertainty, complexity and paradigm shift are three challenges that are inherent in emerging technologies. Scenarios provide background for decision-making by clarifying possible paths of emerging technologies. Based on the analysis of three basic stages of empirical scenario construction, i.e. analysis phase, subsystem analysis phase and syntheses phase, credibility theory and fuzzy programming are introduced to dissolve scenario planning problems. This paper adopt such research road as (1) measurement of factors' credibility, (2) analysis of pairwise factors' compatibility, (3) eliminate incompatible scenarios through pruning rules, and (4) choice of scenarios through fuzzy programming. And then, the choice of scenarios can be conducted by expected value model. In the end, the further research fields are also discussed.

关键词： Scenario planning Credibility theory fuzzy programming

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A carbon footprint-based closed-loop supply chain model under uncertainty with risk analysis: A case study

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2016年第0期48卷 425-450页

作者： Mohajeri, Amir Fallah, Mohammad Islamic Azad Univ Cent Tehran Branch Dept Ind Engn Tehran Iran Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran

The management of products' end-of-life and the recovery of used products has gained significant importance in recent years. In this paper, we address the carbon footprint-based problem that arises in a closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed of or be remanufactured to be resold as new ones. Given this environment, an optimization model for a closed-loop supply chain (CLSC) in which carbon emission is expressed in terms of environmental constraints, i.e., carbon emission constraints, is developed. These constraints aim to limit the carbon emission per unit of product supplied with different transportation modes. Here, we design a closed-loop network where capacity limits, single-item management and uncertainty on product demands and returns are considered. First, fuzzy mathematical programming is introduced for uncertain modeling. Then, the statistical approach to the possibility to synthesize fuzzy information is utilized. Therefore, using a defined possibilistic mean and variance, we transform the proposed fuzzy mathematical model into a crisp form to facilitate efficient computation and analysis. Finally, the risk caused by violating the estimated resource constraints is analyzed so that decision makers (DMs) can trade off between the expected cost savings and the expected risk. We utilize data from a company located in Iran. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Green supply chain management Transport mode selection Risk analysis Preference model Interval programming fuzzy programming

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The empty wagons adjustment algorithm of Chinese heavy-haul railway

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CHAOS SOLITONS & FRACTALS 2016年 89卷 91-99页

作者： Zhang, Jinchuan Yang, Hao Wei, Yuguang Shang, Pan Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

The paper studied the problem of empty wagons adjustment of Chinese heavy-haul railway. Firstly, based on the existing study of the empty wagons adjustment of heavy-haul railway in the world, Chinese heavy-haul railway was analyzed, especially the mode of transportation organization and characteristics of empty wagons adjustment. Secondly, the optimization model was set up to solve the empty wagons adjustment of heavy-haul railway and the model took the minimum idling period as the function goal. Finally, through application and solution of one case, validity and practicability of model and algorithm had been proved. So, the model could offer decision support to transport enterprises on adjusting empty wagons. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Empty wagons adjustment Dynamic planning Heavy-haul railway Idling period Transportation organization fuzzy programming

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Interval-fuzzy municipal-scale energy model for identification of optimal strategies for energy management A case study of Tianjin, China

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RENEWABLE ENERGY 2016年第Feb.期86卷 1161-1177页

作者： Chen, C. Li, Y. P. Huang, G. H. Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China North China Elect Power Univ Sino Canada Resources & Environm Res Acad MOE Key Lab Reg Energy Syst Optimizat Beijing 102206 Peoples R China Univ Regina Fac Engn & Appl Sci Environm Syst Engn Program Regina SK S4S 0A2 Canada

An interval-fuzzy municipal-scale energy model (IF-MEM) is developed for energy systems planning. IF-MEM can effectively tackle uncertainties presented in terms of fuzzy sets and discrete intervals. IF-MEM is applied to a real case of energy systems planning in the City of Tianjin, which is the economic and industrial center of northern China. IF-MEM allows in-depth analyses for tradeoffs among economic objective, satisfaction degree, and constraint-violation risk. The results indicate that reasonable solutions have been generated. The results suggest that: (i) coal would be the dominant sources in city's energy systems due to the increasing energy demand;however, its consumption ratio would decrease over the planning horizon due to requirements of environment protection and sustainable development;(ii) clean (e.g., natural gas, liquefied petroleum gas) and renewable energies (e.g., wind power, solar power) would be vigorously encouraged during the planning periods;(iii) coal-fired technology (i.e., coal-fired power and heat) would be the major contributor for the city's atmosphere pollution;(iv) the pollutant emissions would be decreased over the planning horizon due to the restriction of fossil fuels and the development of renewable energy. Promoting the utilization of renewable energy sources is crucial for supporting pollutant emission reduction and solving energy shortage crisis. (C) 2015 Published by Elsevier Ltd.

关键词： Energy system fuzzy programming Modeling Planning Municipal-scale Renewable energy

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Multiobjective Stochastic Multivariate Stratified Sampling in Presence of Nonresponse

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2016年第8期45卷 2810-2826页

作者： Haseen, Sanam Ali, Irfan Bari, Abdul Aligarh Muslim Univ Dept Stat & Operat Res Aligarh Uttar Pradesh India

The case of nonresponse in multivariate stratified sampling survey was first introduced by Hansen and Hurwitz in 1946 considering the sampling variances and costs to be deterministic. However, in real life situations sampling variance and cost are often random (stochastic) and have probability distributions. In this article, we have formulated the multivariate stratified sampling in the presence of nonresponse with random sampling variances and costs as a multiobjective stochastic programming problem. Here, the sampling variance and costs are considered random and converted into a deterministic NLPP by using chance constraint and modified E-model. A solution procedure using three different approaches are adopted viz. goal programming, fuzzy programming, and D-1 distance method to obtain the compromise allocation for the formulated problem. An empirical study has also been provided to illustrate the computational details.

关键词： D-1 distance method fuzzy programming Goal programming Modified E-model Multivariate sampling survey Nonresponse Stochastic modeling

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An advanced optimization technique for considering reliability issues and unit commitment problems simultaneously

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第3期24卷 1061-U4019页

作者： Heidari, Ahmad Alizadeh Pahlavani, Mohammad Reza Chamran Univ Ahwaz Ahvaz Iran Malek Ashtar Univ Technol Tehran Iran

This paper presents an advanced optimization technique to solve unit commitment problems and reliability issues simultaneously for thermal generating units. To solve unit commitment, generalized Benders decomposition along with a genetic algorithm are proposed to include minimum up/down time constraints, and for reliability issues consideration, a fuzzy stochastic-based technique is presented. To implement the problem into an optimization program, MATLAB software and CPLEX and KNITRO solvers are applied. To verify the proposed technique and algorithm, two case studies, the IEEE 14- and 118-bus systems, are implemented for optimal generation scheduling and reliability issues. Finally, a comparison with other solution techniques is given.

关键词： Benders decomposition fuzzy programming genetic algorithm optimization technique reliability issues unit commitment

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