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Thermo-economic analysis and optimization selection of ORC system configurations for low temperature binary-cycle geothermal plant

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APPLIED THERMAL ENGINEERING 2017年 125卷 153-164页

作者： Liu, Xiaomin Wei, Ming Yang, Luona Wang, Xing Xi An Jiao Tong Univ Sch Energy & Power Engn Dept Fluid Machinery & Engn 28 West Xianning Rd Xian 710049 Shaanxi Peoples R China Chinese Acad Sci Inst Engn Thermophys Energy Storage R&D Ctr Beijing 100190 Peoples R China

multi-objective programming is used to analyze the performance of different organic Rankine cycle (ORC) plant layouts with different working fluids for low temperature binary-cycle geothermal plant. The studied results show that for the considered ORC plant layouts, the optimal overall performance indices increase with the geothermal temperature increasing. For a specific working fluid, the optimal ORC system for overall performance always remains unchanged despite the increase in the geothermal temperature. The optimal schemes of ORC systems vary with the performance indices of ORC system. The optimal scheme for comprehensive performance index is simple cycle with R123 when the geothermal temperature increases from 80 degrees C to 95 degrees C. The optimal scheme for thermal efficiency is regenerative cycle with R123, while the optimal scheme for capital cost is superheated cycle with R123. For the work output and exergy efficiency, the optimal scheme is superheated cycle with R152a when the geothermal temperature varies from 80 degrees C to 85 degrees C, while the scheme of superheated cycle with R134a is better for work output and exergy efficiency when the geothermal temperature is greater than 90 degrees C. This study provides useful references for the researchers selecting the optimal configuration of ORC system for low temperature binary-cycle geothermal plant. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Organic Rankine cycle multi-objective programming Working fluid Geothermal plant

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A multi-stage and multi-supplier inventory model allowing different order quantities

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APPLIED MATHEMATICAL MODELLING 2017年 52卷 613-625页

作者： Chang, Shu-Chin Chang, Ching-Ter Chung Yuan Christian Univ Dept Accounting Taoyuan Taiwan Chang Gung Univ Dept Informat Management Taoyuan Taiwan Chang Gung Mem Hosp Dept Thorac Med Taoyuan Taiwan Ming Chi Univ Technol Dept Ind Engn & Management New Taipei Taiwan

This paper addresses a multi-stage inventory model that allows different order quantities among the selected suppliers to obtain the optimal solutions. To achieve the objective of the study, the single-objective and multi-objective methods are adopted for suitable real world applications. With respect to a single-objective method, this paper aims to minimize the total ordering costs, holding costs, and purchasing costs, subject to the price, quality, and capacity. With respect to a multi-objective method, it focuses on cost minimization, as well as quality and capacity maximization. The proposed model not only considers the allocation of different order quantities among the selected suppliers, but also incorporates the multi-stage inventory problem. Furthermore, a numerical example is provided to illustrate the usefulness of the proposed model and a comparative understanding of various methods. In addition, a simulation test is performed to effectively validate the proposed model which outperforms the previous works. Finally, a sensitivity analysis carried out to investigate the impact of supply chain cost. (C) 2017 Elsevier Inc. All rights reserved.

关键词： multi-objective programming multi-stage inventory model Supply chain system

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Evaluation of multi-objective optimization approaches for solving green supply chain design problems

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2017年 68卷 168-184页

作者： Kadzinski, Milosz Tervonen, Tommi Tomczyk, Michaf K. Dekker, Rommert Poznan Univ Tech Inst Comp Sci Piotrowo 2 PL-60965 Poznan Poland Evidera Ltd London England Erasmus Univ Erasmus Sch Econ Econometr Inst NL-3000 DR Rotterdam Netherlands

This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO2 and fine dust (also known as PM - Particulate Matters) emissions. We approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies, and their interactive counterparts that incorporate Decision Maker's (DM's) indirect preferences into the search process. Within this case study, the CO2 emissions could be lowered significantly by accepting a marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of the Pareto front, but produced significantly worse solutions than the exact optimization methods. The interactive methods outperformed their a posteriori counterparts, and could discover solutions corresponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and starting generation of the elicitation, the number of pairwise comparisons needed by the interactive evolutionary methods to construct a satisfactory solution could be decreased. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Supply chain management Green logistics multi-objective programming Indirect preference information Evolutionary algorithms Interactive evolutionary multi-objective optimization

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multi-objective EVOLUTIONARY APPROACH FOR SUPPLY CHAIN NETWORK DESIGN PROBLEM WITHIN ONLINE CUSTOMER CONSIDERATION

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RAIRO-OPERATIONS RESEARCH 2017年第1期51卷 135-155页

作者： Liao, Shu-Hsien Hsieh, Chia-Lin Ho, Wei-Chung Tamkang Univ Dept Management Sci & Decis Making 151 Yingjuan Rd New Taipei 251 Taiwan Aletheia Univ Dept Finance & Actuarial Sci 26 Chenli St New Taipei 251 Taiwan

Supply chain network design is one of the most important strategic decisions that need to be optimized for long-term efficiency. Critical decisions include facility location, inventory, and transportation issues. This study proposes that with a dual-channel supply chain network design model, the traditional location-inventory problem should be extended to consider the vast amount of online customers at the strategic level, since the problem usually involves multiple and conflicting objectives. Therefore, a multi-objective dual-channel supply chain network model involving three conflicting objectives is initially proposed to allow a comprehensive trade-off evaluation. In addition to the typical costs associated with facility operation and transportation, we explicitly consider the pivotal online customer service rate between the distribution centers (DCs) and their assigned customers. This study proposes a heuristic solution scheme to resolve this multi-objective programming problem, by integrating genetic algorithms, a clustering analysis, a Non-dominated Sorting Genetic Algorithm II (NSGA-II), and a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Several experiments are simulated to demonstrate the possibility and efficacy of the proposed approach. A scenario analysis is conducted to understand the model's performance.

关键词： Supply chain network design location inventory problem dual channel multi-objective programming evolutionary computation

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Resilience Optimization for Complex Engineered Systems Based on the multi-Dimensional Resilience Concept

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IEEE ACCESS 2017年 5卷 19352-19362页

作者： Ren, Fuchun Zhao, Tingdi Jiao, Jian Hu, Yuqing Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Most traditional engineered systems are designed with a passive and fixed reliability capability and just required to achieve a possibly low level of failure occurrence. However, as the complexity at spatial-temporal scales and integrations increases, modern complex engineered systems (CESs) are facing new challenges of inherent risk and bottleneck for a successful and safe operation through the system life cycle when potential expected or unexpected disruptive events happen. As a prototype for ensuring the successful operation of inherently risky systems, resilience has demonstrated itself to be a promising concept to address the above-mentioned challenges. A standard multi-dimensional resilience triangle model is first presented based on the concept of the three-phase system resilience cycle, which can provide a theoretical foundation for indicating the utility objectives of resilience design. Then, the resilience design problem for CESs is proposed as a multi-objective optimization model, in which the three objectives are to maximize the survival probability, to maximize the reactive timeliness and to minimize the total budgeted cost. Furthermore, the proposed multi-objective optimization programming is solved based on the efficient multi-objective evolutionary algorithm NSGA-II. Finally, the effectiveness of the proposed models and solving procedure is illustrated with an engineered electro-hydrostatic aircraft control actuator resilience design problem, a comparative analysis on the case study is also carried out with respect to previous works. This work can provide an effective tradeoff foundation to improve the resilience of CESs.

关键词： Resilience complex engineered systems multi-dimensional model multi-objective programming NSGA-II

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A class of generalized invex functions and vector variational-like inequalities

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JOURNAL OF INEQUALITIES AND APPLICATIONS 2017年第1期2017卷 70页

作者： Li, Ru Yu, Guolin Beifang Univ Nationalities Inst Appl Math Ningxia 750021 Yinchuan Peoples R China

In this paper, a class of generalized invex functions, called (a,.,.)-invex functions, is introduced, and some examples are presented to illustrate their existence. Then we consider the relationships of solutions between two types of vector variational-like inequalities and multi-objective programming problem. Finally, the existence results for the discussed variational-like inequalities are proposed by using the KKM-Fan theorem.

关键词： vector variational-like inequality multi-objective programming invex function KKM-Fan theorem

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A new method for the selection of distribution centre locations

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IMA JOURNAL OF MANAGEMENT MATHEMATICS 2017年第3期28卷 421-436页

作者： Zhou, Lei Zhang, Guoshan Liu, Wanquan Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China Hebei Univ Sci & Technol Sch Econ & Management Shijiazhuang 050018 Peoples R China Curtin Univ Dept Comp Perth WA 6102 Australia

This paper investigates the distribution centre location problem with inaccurate information, and a general model based on a rough feasible region is established. By means of synthesizing the believable degree of the rough feasible region and objective functions, a solution model termed as rough multi-objective synthesis effect (RMOSE) model is developed;this constitutes a series of crisp multi-objective programming models that reflect different decision consciousness for each decision maker. The optimal solutions of the RMOSE model can be obtained by using the genetic algorithm, and it is demonstrated that the solution of the RMOSE model in proper parameters is same as that of existing model with fuzzy model information. So the proposed RMOSE model is actually an extension of a crisp multi-objective programming model. Two cases of experiments for the distribution centre location problems show that the proposed method can be directly applied to real world practices and it is better than existing methods with fuzzy model information.

关键词： distribution centre location multi-objective programming rough set genetic algorithm synthesis effect

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multi-objective optimization for a closed-loop network design problem using an improved genetic algorithm

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APPLIED MATHEMATICAL MODELLING 2017年 45卷 14-30页

作者： Shi, Jianmai Liu, Zhong Tang, Luohao Xiong, Jian Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

This paper develops a multi-objective Mixed Integer programming model for a closed-loop network design problem. In addition to the overall costs, the model optimizes overall carbon emissions and the responsiveness of the network. An improved genetic algorithm based on the framework of NSGA II is developed to solve the problem and obtain Pareto-optimal solutions. An example with 95 cities in China is presented to illustrate the approach. Through randomly generated examples with different sizes;the computational performance of the proposed algorithm is also compared with former genetic algorithms in the literature employing the weight-sum technique as a fitness evaluation strategy. Computational results indicate that the proposed algorithm can obtain superior Pareto-optimal solutions. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Closed loop supply chain Carbon emission multi-objective programming Facility location Evaluation algorithm

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Mathematical Model of Financial Investment Risk

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Journal of Mathematical Finance 2018年 8卷 127-136页

作者： Deyu Yin Rizhao Polytechnic Rizhao China

This paper establishes the income and risk model in financial investment based on multi-objective programming theory, aiming to analyze the relationship between risk and return in financial investment and discuss the relationship between the risk the investor shall bear and decentralization degree of investment project. MATLAB software is used to analyze the investor’s optimized return under fixed risk level and the minimized risk with defined benefit. In addition, it chooses the optimal portfolio under such risk level with respect to the bearing capacity of different risks. This paper performs sensitivity analysis of risk in income model using LINGO software, and puts forward the optimal portfolio for the investor without special preference. Calculations show that the model established is satisfactory in determining the optimal portfolio.

关键词： multi-objective programming Risk Measurement Income Level Sensitivity Analysis Optimal Portfolio

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Trade-off between cost and variance for a multi-objective compromise allocation in stratified random sampling

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第6期46卷 2655-2666页

作者： Shad, Muhamamd Yousaf Husain, Ijaz Quaid I Azam Univ Dept Stat Islamabad Pakistan

In the present paper, a multi-objective goal optimization mechanism is developed by trading off between cost and variance. Both are adversaries to each other while allocating a sample size even in stratified sampling design. Discussion section shows how these adversaries put their influence on optimal selection. This is a dual optimization procedure in which variance or mean square error is optimized in the first step and then considering some compromise on variance, cost is optimized. The process is applied to both individual and multi-objective programming models.

关键词： Compromise allocation lexicographic goal programming multi-objective programming multivariate stratified sampling non linear cost function

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