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Production, investment and financial plan for a "natural gas+" integrated energy enterprise: An assessment using system dynamics and multi-objective optimization model

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ENERGY REPORTS 2025年 13卷 1859-1874页

作者： Xiong, Ye Wang, Yifan Zhang, Yue Chen, Jieke Wu, Mengxi PetroChina Southwest Oil & Gasfield Co Res Inst Nat Gas Econ Chengdu Sichuan Peoples R China Chengdu Environm Protect Agcy Chengdu Sichuan Peoples R China Univ Leeds Business Sch Leeds England

This study explores the critical challenge of balancing energy supply, investment efficiency, and profitability during the transition of natural gas enterprises into "natural gas+ " integrated energy systems. In a dynamic and uncertain environment, these systems face competing objectives: the carbon neutrality, resource optimization, and financial sustainability. To address these complexities, this study proposes a System Dynamics-multi- objective programming (SD-MOP) model, grounded in the "Business-Production-Investment-Financial-Target" (BPIFT) framework, to integrate production, investment, and financial planning. The model incorporates SD to capture dynamic feedback among production, investment, and financial processes;MOP to resolve conflicting goals;Monte Carlo simulations to assess uncertainties across 200 scenarios;and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate Pareto-optimal solutions. Results identify key variables, such as the Reserve Replacement Ratio (RRR) and Single Well Production (SWP), as critical for maintaining system stability. Scenario analyses demonstrate that the model balances competing objectives, achieving a 14% improvement in energy supply and a 19% increase in profitability, while highlighting the trade-offs between maximizing energy supply and profitability under manageable investment levels. Overall, these findings establish the model as a practical decision-making tool for integrated energy enterprises, aligning enterprise strategies with sustainability goals.

关键词： Natural Gas+ Integrated Energy Enterprise System dynamics multi-objective programming Comprehensive planning Carbon neutrality Optimization Energy transition

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Active-Passive Beamforming Optimization for RIS-Aided mmWave Joint Localization and Communication System

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 2941-2957页

作者： Qiu, Hengji Gong, Shiqi Liu, Heng Zhao, Xin Xing, Chengwen Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Samsung Res China Beijing SRC B Beijing 100028 Peoples R China

With the remarkable development of the location-aware communications, joint localization and communication (L&C) has become one of the strongest competitors for the 6G key enabling technologies. Meanwhile, the reconfigurable intelligent surface (RIS) has emerged as an important enhancing technology for both communication and localization. In this paper, we investigate a RIS-aided joint L&C system, where the localization and data transmission functions are integrated in a time-division pattern. Specifically, we aim to jointly optimize the covariance matrices of the localization signal and the communication signal, the RIS beamforming matrix and the time division ratio to simultaneously improve the localization and communication performance. An alternating optimization (AO) algorithm is proposed via jointly leveraging the successive convex approximation technique, the semidefinite relaxation method and the projected gradient descent with momentum algorithm, based on which we strike a compelling trade-off between the localization and communication performance. Considering the high computational cost in the large-scale array scenario, we also develop a low-complexity algorithm free of iterations. To analyze the optimality of our proposed AO algorithm, we further consider the special single-user line-of-sight (LoS) scenario and validate that our AO algorithm could achieve the beam alignment, implying its effectiveness. Finally, numerical simulations are carried out to demonstrate the superior performance of our proposed algorithm.

关键词： Location awareness Optimization Array signal processing Millimeter wave communication Communication systems Resource management Performance evaluation OFDM Covariance matrices Wireless communication Joint localization and communication reconfigurable intelligent surface multi-objective programming successive convex approximation

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Biform game consensus analysis of group decision making with unconnected social network

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第1期324卷 259-275页

作者： Tang, Jie Li, Zi-Jun Meng, Fan-Yong Gong, Zai-Wu Pedrycz, Witold Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Cent South Univ Sch Business Changsha 410083 Hunan Peoples R China North China Univ Water Resources & Elect Power Sch Management & Econ Zhengzhou 450000 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

In today's network era, people's decisions are susceptibly influenced by others, especially the ones they trust. This study confines to studying social network group decision making (SNGDM). Due to the mutual influence of consensus level and consensus adjustment among decision makers (DMs), this study utilizes biform game theory to propose an innovative consensus mechanism for facilitating group decision making with unconnected social networks. Specifically, in the context of a DM social network with multiple trust relationship-based connected components, we construct a multi-objective programming model to determine the consensus adjustment. Within each connected component, we employ the digraph game theory to study DMs' consensus adjustments, leveraging the directional and asymmetrical characteristics of trust-relationships. We then analyze the consensus adjustments of feasible DM coalitions using built optimization models and define the di-Myerson value. Additionally, we construct several axiomatic systems to show the rationality of consensus allocation results. We identify partial trust-relationships that increase the consensus adjustments of DMs as irrational, and design an algorithm to address them, thereby reducing the cost of consensus. Finally, we present a case study that showcases the real-world application of our new theoretical results. This is the first bi-form game consensus mechanism based on trust relationship for SNGDM.

关键词： Group decisions and negotiations Consensus analysis Biform game multi-objective programming Di-myerson value

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On the efficiency test in multi-objective linear fractional programming problems by Lotfi et al. 2010

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APPLIED MATHEMATICAL MODELLING 2013年第10-11期37卷 7086-7093页

作者： Stanojevic, Bogdana Stanojevic, Milan Serbian Acad Arts & Sci Math Inst Belgrade 11001 Serbia Univ Belgrade Fac Org Sci Belgrade 11000 Serbia

The solution concept in multi-objective programming is represented by a program which reaches "the best compromise". Many solving methods find a good compromise feasible solution and then check whether the solution is efficient or not. In this paper, using the efficiency test introduced by Lotfi et al. (2010) [12], we propose two procedures for deriving weakly and strongly efficient solutions in multi-objective linear fractional programming problems (MOLFPP) starting from any feasible solution, and present their possible applications in multiple criteria decision-making process. Then, we discuss a shortcoming of some fuzzy approaches to solving MOLFPP and modify them in order to guarantee the efficiency of the optimal solution. (C) 2013 Elsevier Inc. All rights reserved.

关键词： multi-objective programming Linear fractional programming Efficient solution Fuzzy approach

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Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation

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INTERNATIONAL JOURNAL OF GENERAL SYSTEMS 2016年第3期45卷 286-310页

作者： Du, Jiaoman Yu, Lean Li, Xiang Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China

Hazardous materials transportation is an important and hot issue of public safety. Based on the shortest path model, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. First, we present the risk model, travel time model and fuel consumption model. Furthermore, we formulate a chance-constrained programming model within the framework of credibility theory, in which the lengths of arcs in the transportation network are assumed to be fuzzy variables. A hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed for finding a satisfactory solution. Finally, some numerical examples are given to demonstrate the efficiency of the proposed model and algorithm.

关键词： Hazardous materials transportation multi-objective programming fuzzy chance-constrained programming hybrid intelligent algorithm

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The interval copula-measure Me based multi-objective multi-stage stochastic chance-constrained programming for seasonal water resources allocation under uncertainty

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2021年第7期35卷 1463-1480页

作者： Wang, Youzhi Guo, Ping China Agr Univ Ctr Agr Water Res China Beijing 100083 Peoples R China Minist Agr & Rural Affairs Wuwei Expt Stn Efficient Water Use Agr Wuwei 733000 Peoples R China

A copula-measure Me based interval multi-objective multi-stage stochastic chance-constrained programming (CMIMOMSP) model is proposed for water consumption optimization. It can conduct water allocation amid multiple users and multiple stages, and deal with the uncertainties presented as interval numbers, random fuzzy interval numbers, and stochastic variables. It improves upon multi-stage stochastic chance-constrained programming by introducing the multi-objective programming, and it can tradeoff the relationships amid economic benefit, full usage of water resources, and economic loss. It enhances the accuracy of copula function and conditional distribution function through proposing the interval functions. Besides, it can deal with the impact of the decision attitudes of managers on water allocation by formulating the function equation between water demand and the optimistic-pessimistic factor. The CMIMOMSP model is applied to a case study of the Heihe River Basin to verify its application. The results indicate that: (1) the optimistic-pessimistic factors have different degrees of positive influences on water allocation for industrial, domestic and ecological sectors;(2) the joint violated probability and optimistic-pessimistic factor have various range of impacts on agricultural water allocation;(3) tthe objective function values have different variation tendencies with the rise of joint violated probabilities and optimistic-pessimistic factors. Its robustness is enhanced by comparing it with the three single-objective programming models. The CMIMOMSP model can provide various water allocation schemes for managers with different risk attitudes in semi-arid and arid districts.

关键词： Interval copula function Interval conditional distribution multi-objective programming multi-stage stochastic programming Measure Me Uncertainty

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Integrated multi-objective stochastic fuzzy programming and AHP method for agricultural water and land optimization allocation under multiple uncertainties

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JOURNAL OF CLEANER PRODUCTION 2019年 210卷 12-24页

作者： Ren, Chongfeng Li, Zhehao Zhang, Hongbo Minist Educ Key Lab Subsurface Hydrol & Ecol Effect Arid Reg Xian Shaanxi Peoples R China Changan Univ Sch Environm Sci & Engn YantaSt 126 Xian 710054 Shaanxi Peoples R China

Optimization allocation of agricultural water and land is a very complex system involves multiple objectives. Moreover, weights of each objective were signed by subjective judgement of decision makers is unreasonable. Furthermore, uncertainties are inevitable in the optimization allocation of irrigation water and land. In order to solve the above problems, this paper developed an improved multi-objective stochastic fuzzy programming method. The developed model was then applied to a case study in Wuwei City, Gansu Province, China. Maximum net benefit, maximum agricultural water productivity, and minimum irrigation area were regarded as planning objectives. A series of optimal irrigation and planting structure schemes were obtained under multiple uncertainties. From the results, water resources shortage in Wuwei city is very severely and it could not satisfy Wuwei's water demand even if the risk probability P-i reaches 0.25. Moreover, water and irrigation area would vary in different crops. Such changes would mainly take place in potato, vegetable and cucurbit, which have the same characteristics with higher yield, lower cost or lower irrigation quota than other crops. Furthermore, the decision makers could make reasonable decisions on the optimal use of irrigation water and land resources under multiple objective and uncertainties based on the obtained results. (C) 2018 Published by Elsevier Ltd.

关键词： Irrigation water optimal allocation multi-objective programming Analytic hierarchy process method Chance-constrained programming Fuzzy sets multiple uncertainties

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An approach to find redundant objective function(s) and redundant constraint(s) in multi-objective nonlinear stochastic fractional programming problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第2期201卷 390-398页

作者： Charles, V. Udhayakumar, A. Uthariaraj, V. Rhymend Pontificia Univ Catolica Peru CTR Catolica Santiago De Surco Peru Hindustan Coll Engn Dept Comp Applicat Madras 603103 Tamil Nadu India Anna Univ Ramanujan Comp Ctr Madras 600025 Tamil Nadu India

Structural redundancies in mathematical programming models are nothing uncommon and nonlinear programming problems are no exception. Over the past few decades numerous papers have been written on redundancy. Redundancy in constraints and variables are usually studied in a class of mathematical programming problems. However, main emphasis has so far been given only to linear programming problems. In this paper, an algorithm that identifies redundant objective function(s) and redundant constraint(s) simultaneously in multi-objective nonlinear stochastic fractional programming problems is provided. A solution procedure is also illustrated with numerical examples. The proposed algorithm reduces the number of nonlinear fractional objective functions and constraints in cases where redundancy exists. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Stochastic programming Fractional programming multi-objective programming Redundancy

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A simple augmented ε-constraint method for multi-objective mathematical integer programming problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第1期234卷 15-24页

作者： Zhang, Weihua Reimann, Marc Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Graz Univ Inst Prod & Operat Management A-8010 Graz Austria

A simple augmented epsilon-constraint (SAUGMECON) method is put forward to generate all non-dominated solutions of multi-objective integer programming (MOIP) problems. The SAUGMECON method is a variant of the augmented epsilon-constraint (AUGMECON) method proposed in 2009 and improved in 2013 by Mavrotas et al. However, with the SAUGMECON method, all non-dominated solutions can be found much more efficiently thanks to our innovations to algorithm acceleration. These innovative acceleration mechanisms include: (1) an extension to the acceleration algorithm with early exit and (2) an addition of an acceleration algorithm with bouncing steps. The same numerical example in Lokman and Koksalan (2012) is used to illustrate workings of the method. Then comparisons of computational performance among the method proposed by (Ozlen and Azizoglu (2009), Ozlen et al. (2012), the method developed by Lokman and Koksalan (2012) and the SAUGMECON method are made by solving randomly generated general MOIP problem instances as well as special MOIP problem instances such as the MOKP and MOSP problem instances presented in Table 4 in Lolcman and Koksalan (2012). The experimental results show that the SAUGMECON method performs the best among these methods. More importantly, the advantage of the SAUGMECON method over the method proposed by Lokman and Koksalan (2012) turns out to be increasingly more prominent as the number of objectives increases. (C) 2013 Elsevier B.V. All rights reserved.

关键词： multi-objective programming epsilon-Constraint method AUGMECON method SAUGMECON method

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A novel algorithm for generating Pareto frontier of bi-level multi-objective rough nonlinear programming problem

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AIN SHAMS ENGINEERING JOURNAL 2021年第2期12卷 2125-2133页

作者： Elsisy, M. A. El Sayed, M. A. Abo-Elnaga, Y. Benha Univ Benha Fac Engn Dept Basic Engn Sci Elqalyoubia Egypt Higher Technol Inst Dept Basic Sci Tenth Of Ramadan City Egypt Menoufia Higher Inst Engn & Technol Dept Basic Engn Sci Menoufia Egypt

This paper discusses a new algorithm for generating the Pareto frontier for bi-level multi-objective rough nonlinear programming problem (BL-MRNPP). In this algorithm, the uncertainty exists in constraints which are modeled as a rough set. Initially, BL-MRNPP is transformed into four deterministic models. The weighted method and the Karush-Kuhn-Tucker optimality condition are combined to obtain the Pareto front of each model. The nature of the problem solutions is characterized according to newly proposed definitions. The location of efficient solutions depending on the lower/upper approximation set is discussed. The aim of the proposed solution procedure for the BL-MRNPP is to avoid solving four problems. A numerical example is solved to indicate the applicability of the proposed algorithm. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： multi-objective programming Bi-level programming Rough set KKT optimality

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