Journal articles: 'Multi-objective maximization'

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Nguyen, Duy Van. "Global maximization of UTA functions in multi-objective optimization." European Journal of Operational Research 228, no. 2 (July 2013): 397–404. http://dx.doi.org/10.1016/j.ejor.2012.06.022.

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Feng, Chao, and Chao Qian. "Multi-Objective Submodular Maximization by Regret Ratio Minimization with Theoretical Guarantee." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12302–10. http://dx.doi.org/10.1609/aaai.v35i14.17460.

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Submodular maximization has attracted much attention due to its wide application and attractive property. Previous works mainly considered one single objective function, while there can be multiple ones in practice. As the objectives are usually conflicting, there exists a set of Pareto optimal solutions, attaining different optimal trade-offs among multiple objectives. In this paper, we consider the problem of minimizing the regret ratio in multi-objective submodular maximization, which is to find at most k solutions to approximate the whole Pareto set as well as possible. We propose a new algorithm RRMS by sampling representative weight vectors and solving the corresponding weighted sums of objective functions using some given \alpha-approximation algorithm for single-objective submodular maximization. We prove that the regret ratio of the output of RRMS is upper bounded by 1-\alpha+O(\sqrt{d-1}\cdot(\frac{d}{k-d})^{\frac{1}{d-1}}), where d is the number of objectives. This is the first theoretical guarantee for the situation with more than two objectives. When d=2, it reaches the (1-\alpha+O(1/k))-guarantee of the only existing algorithm Polytope. Empirical results on the applications of multi-objective weighted maximum coverage and Max-Cut show the superior performance of RRMS over Polytope.

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Osiadacz, Andrzej J., and Niccolo Isoli. "Multi-Objective Optimization of Gas Pipeline Networks." Energies 13, no. 19 (October 2, 2020): 5141. http://dx.doi.org/10.3390/en13195141.

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The main goal of this paper is to prove that bi-objective optimization of high-pressure gas networks ensures grater system efficiency than scalar optimization. The proposed algorithm searches for a trade-off between minimization of the running costs of compressors and maximization of gas networks capacity (security of gas supply to customers). The bi-criteria algorithm was developed using a gradient projection method to solve the nonlinear constrained optimization problem, and a hierarchical vector optimization method. To prove the correctness of the algorithm, three existing networks have been solved. A comparison between the scalar optimization and bi-criteria optimization results confirmed the advantages of the bi-criteria optimization approach.

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Qiu, Jianfeng, Minghui Liu, Lei Zhang, Wei Li, and Fan Cheng. "A multi-level knee point based multi-objective evolutionary algorithm for AUC maximization." Memetic Computing 11, no. 3 (February 9, 2019): 285–96. http://dx.doi.org/10.1007/s12293-019-00280-7.

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Alshareef, Haya, and Mashael Maashi. "Application of Multi-Objective Hyper-Heuristics to Solve the Multi-Objective Software Module Clustering Problem." Applied Sciences 12, no. 11 (June 2, 2022): 5649. http://dx.doi.org/10.3390/app12115649.

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Software maintenance is an important step in the software lifecycle. Software module clustering is a HHMO_CF_GDA optimization problem involving several targets that require minimization of module coupling and maximization of software cohesion. Moreover, multi-objective software module clustering involves assembling a specific group of modules according to specific cluster criteria. Software module clustering classifies software modules into different clusters to enhance the software maintenance process. A structure with low coupling and high cohesion is considered an excellent software module structure. In this study, we apply a multi-objective hyper-heuristic method to solve the multi-objective module clustering problem with three objectives: (i) minimize coupling, (ii) maximize cohesion, and (iii) ensure high modularization quality. We conducted several experiments to obtain optimal and near-optimal solutions for the multi-objective module clustering optimization problem. The experimental results demonstrated that the HHMO_CF_GDA method outperformed the individual multi-objective evolutionary algorithms in solving the multi-objective software module clustering optimization problem. The resulting software, in which HHMO_CF_GDA was applied, was more optimized and achieved lower coupling with higher cohesion and better modularization quality. Moreover, the structure of the software was more robust and easier to maintain because of its software modularity.

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Hashir, Syed Muhammad, Sabyasachi Gupta, Gavin Megson, Ehsan Aryafar, and Joseph Camp. "Rate Maximization in a UAV Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization." Electronics 11, no. 3 (January 28, 2022): 401. http://dx.doi.org/10.3390/electronics11030401.

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In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems.

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Ene, Seval, and Nursel Öztürk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (April 12, 2017): 15. http://dx.doi.org/10.18844/gjbem.v7i1.1391.

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In supply chain management, economical objectives have traditionally guided decisions of the supply chains. However, with increased global environmental and social concerns, in recent years, green aspects have been incorporated in supply chain decisions. These expansions lead to new research areas about green or sustainable supply chain management that includes applying various green practices in order to reduce negative impact on the environment or providing sustainable development. The purpose of this study is to develop a multi-objective optimization model for determining network design of the green supply chains. In multi-objective frame of the proposed model, total profit maximization and environmental impact minimization objectives are considered in order to obtain best network configuration for economic and environmental performance of the green chain. The proposed model is validated with numerical experiments. Obtained results showed that the model can be used as a strategic decision tool in problems with multi and conflicting objectives. Keywords: Green supply chain management, multi-objective modelling, network optimization;

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Ene, Seval, and Nursel Ozturk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (January 15, 2018): 15–24. http://dx.doi.org/10.18844/gjbem.v7i1.1875.

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In supply chain management, economical objectives have traditionally guided decisions of the supply chains. However, with increased global environmental and social concerns, in recent years, green aspects have been incorporated in supply chain decisions. These expansions lead to new research areas about green or sustainable supply chain management that includes applying various green practices in order to reduce negative impact on the environment or providing sustainable development. The purpose of this study is to develop a multi-objective optimization model for determining network design of the green supply chains. In multi-objective frame of the proposed model, total profit maximization and environmental impact minimization objectives are considered in order to obtain best network configuration for economic and environmental performance of the green chain. The proposed model is validated with numerical experiments. Obtained results showed that the model can be used as a strategic decision tool in problems with multi and conflicting objectives. Keywords: green supply chain management, multi-objective modelling, network optimization.

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Küçükoğlu, Ilker, and Nursel Öztürk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (October 20, 2017): 15. http://dx.doi.org/10.18844/gjbem.v7i1.2561.

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Abstract In supply chain management, economical objectives have traditionally guided decisions of the supply chains. However, with increased global environmental and social concerns, in recent years, green aspects have been incorporated in supply chain decisions. These expansions lead to new research areas about green or sustainable supply chain management that includes applying various green practices in order to reduce negative impact on the environment or providing sustainable development. The purpose of this study is to develop a multi-objective optimization model for determining network design of the green supply chains. In multi-objective frame of the proposed model, total profit maximization and environmental impact minimization objectives are considered in order to obtain best network configuration for economic and environmental performance of the green chain. The proposed model is validated with numerical experiments. Obtained results showed that the model can be used as a strategic decision tool in problems with multi and conflicting objectives. Keywords: green supply chain management, multi-objective modelling, network optimization.

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Adeyeye, Ademola David, and Festus Adekunle Oyawale. "Lexicographic Multi-Objective Optimization Approach for Welding Flux System Design." European Journal of Engineering Science and Technology 4, no. 1 (June 18, 2022): 1–14. http://dx.doi.org/10.33422/ejest.v4i1.593.

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Multiple response optimization of welding flux performance has been found to be cost effective and useful for the achievement of the best balance among conflicting welding flux quality attributes. Many multi-criteria optimization methods (MCOM) have been applied in flux formulation situations where flux quality attributes are of comparable importance. However, information on applications of MCOM to flux design situations where quality attributes are in hierarchical order of importance is scarce in the open literature. In this study, a Lexicographic Multi-objective Optimization (LMO) model was proposed for handling flux design situations in which the attributes are in hierarchical order of importance. The model was applied using data from literature. Two priority levels were used: acicular ferrite (AF) maximization was assigned first priority while the maximization of polygonal ferrite (PF) content and weld-metal impact toughness (WIT) were assigned second priority subject to oxygen content constraint of 250 – 350ppm. The respective solutions for AF, PF, WIT and oxygen content were 51.19%, 21.80%, 23.70J at -20oC and 315ppm. The corresponding flux formulation was CaO (25.90) MgO (15.00) CaF2 (31.10) and Al2O3 (8.00%). Various priority structures were used to explore trade-off options and to generate three more pareto efficient solutions from which the flux formulator can select the most preferred one. The proposed model has filled the existing gap in the literature being a pioneering work in the application of lexicographic multi-objective optimization method in welding flux design.

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Salkuti, Surender Reddy. "Multi-Objective based Optimal Energy and Reactive Power Dispatch in Deregulated Electricity Markets." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3427. http://dx.doi.org/10.11591/ijece.v8i5.pp3427-3435.

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This paper presents a day-ahead (DA) multi-objective based joint energy and reactive power dispatch in the deregulated electricity markets. The traditional social welfare in the centralized electricity markets comprises of customers benefit function and the cost function of active power generation. In this paper, the traditional social welfare is modified to incorporate the cost of both active and reactive power generation. Here, the voltage dependent load modeling is used. This paper brings out the unsuitability of traditional single objective functions, e.g., social welfare maximization (SWM), loss minimization (LM) due to the reduction of amount of load served. Therefore, a multi-objective based optimization is required. This paper proposes four objectives, i.e., SWM, load served maximization (LSM), LM and voltage stability enhancement index (VSEI); and these objectives can be combined as per the operating condition. The simulation studies are performed on IEEE 30 bus test system by considering the both traditional constant load modeling and the proposed voltage dependent load modeling.

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Sun, Liucheng, Chenwei Weng, Chengfu Huo, Weijun Ren, Guochuan Zhang, and Xin Li. "Traffic Shaping in E-Commercial Search Engine: Multi-Objective Online Welfare Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 574–81. http://dx.doi.org/10.1609/aaai.v35i1.16136.

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The e-commercial search engine is the primary gateway for customers to find desired products and engage in online shopping. Besides displaying items to optimize for a single objective (i.e., relevance), ranking items needs to satisfy some other business requirements in practice. Recently, traffic shaping was introduced to incorporate multiple objectives in a constrained optimization framework. However, many practical business requirements can not explicitly represented by linear constraints as in the existing work, and this may limit the scalablity of their framework. This paper presents a unified framework from the aspect of multi-objective welfare maximization where we regard all business requirements as objectives to optimize. Our framework can naturally incorporate a wide range of application-driven requirements. In addition to formulating the problem, we design an online traffic splitting algorithm that allows us to flexibly adjust the priorities of different objectives, and it has rigorous theoretical guarantees over the adversarial scenario. We also run experiments on both synthetic and real-world datasets to validate our algorithms.

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OKELLO, Moses Oyaro. "Time Governed Multi-Objective Optimization." Eurasia Proceedings of Science Technology Engineering and Mathematics 16 (December 31, 2021): 167–81. http://dx.doi.org/10.55549/epstem.1068585.

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Multi-objective optimization (MOO) is an optimization involving minimization or maximization of several objective functions more than the conventional one objective optimization, which is useful in many fields. Many of the current methodologies addresses challenges and solutions that attempt to solve simultaneously several Objectives with multiple constraints subjoined to each. Often MOO are generally subjected to linear inequality, equality and or bounded constraint that prevent all objectives from being optimized at once. This paper reviews some recent articles in area of MOO and presents deep analysis of Random and Uniform Entry-Exit time of objectives. It further breaks down process into sub-process and then provide some new concepts for solving problems in MOO, which comes due to periodical objectives that do not stay for the entire duration of process lifetime, unlike permanent objectives which are optimized once for the entire process duration. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives are given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified based on their properties, and ranked using a given criteria and in addition estimated for an optimal weight point (pareto optimal point) if it certifies a coveted optimal weight point. Then finally predicted to find how far it deviates from the estimated optimal weight point. A Sample Mathematical Tri-Objectives and Real-world Optimization was analyzed using partial method, ranking and classification method, the result showed that an objective can be added or removed without affecting previous or existing optimal solutions. Therefore, suitable for handling time governed MOO. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis and examples presented, the concept is worthy of igniting further research and application.

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Lam, Weng Siew, Weng Hoe Lam, and Saiful Hafizah Jaaman. "Portfolio Optimization with a Mean–Absolute Deviation–Entropy Multi-Objective Model." Entropy 23, no. 10 (September 28, 2021): 1266. http://dx.doi.org/10.3390/e23101266.

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Investors wish to obtain the best trade-off between the return and risk. In portfolio optimization, the mean-absolute deviation model has been used to achieve the target rate of return and minimize the risk. However, the maximization of entropy is not considered in the mean-absolute deviation model according to past studies. In fact, higher entropy values give higher portfolio diversifications, which can reduce portfolio risk. Therefore, this paper aims to propose a multi-objective optimization model, namely a mean-absolute deviation-entropy model for portfolio optimization by incorporating the maximization of entropy. In addition, the proposed model incorporates the optimal value of each objective function using a goal-programming approach. The objective functions of the proposed model are to maximize the mean return, minimize the absolute deviation and maximize the entropy of the portfolio. The proposed model is illustrated using returns of stocks of the Dow Jones Industrial Average that are listed in the New York Stock Exchange. This study will be of significant impact to investors because the results show that the proposed model outperforms the mean-absolute deviation model and the naive diversification strategy by giving higher a performance ratio. Furthermore, the proposed model generates higher portfolio mean returns than the MAD model and the naive diversification strategy. Investors will be able to generate a well-diversified portfolio in order to minimize unsystematic risk with the proposed model.

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Bakshi, Shalley, Surbhi Sharma, and Rajesh Khanna. "A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network." Elektronika ir Elektrotechnika 28, no. 3 (June 28, 2022): 78–89. http://dx.doi.org/10.5755/j02.eie.31245.

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In this article, a novel technique is proposed, namely rank-based multi-objective antlion optimization (RMOALO), and applied to optimize the performance of the energy harvesting cognitive radio network (EHCRN). The original selection method in multi-objective antlion optimizer (MOALO) is suitably changed to improve the algorithm, thus reaching the optimal solution for the problem. The proposed technique shows considerable performance improvement over the method used in the multi-objective antlion optimizer (MOALO). The performance of the proposed RMOALO is demonstrated on five benchmark mathematical functions and compared to multi-objective particle swarm optimization (MOPSO), multi-objective moth flame optimization (MOMFO), MOALO-Tournament, and MOALO-Roulette. The simulation results show an improved convergence of RMOALO and find the optimal solution to the throughput maximization problem. We show that RMOALO provides 16.33 % improved average throughput with the optimal value of sensing duration for the varying amount of harvested energy compared to MOPSO, MOMFO, MOALO-Roulette, and MOALO-Tournament.

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Surender Reddy, S. "Multi-objective optimization considering cost, emission and loss objectives using PSO and fuzzy approach." International Journal of Engineering & Technology 7, no. 3 (July 20, 2018): 1552. http://dx.doi.org/10.14419/ijet.v7i3.11203.

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A novel approach to solve multi-objective optimization (MOO) problem which aims at minimizing fuel cost, emission release and real power loss of the system simultaneously has been proposed in this paper. Conventional minimum cost operation cannot be the only basis for generation dispatch; emission release minimization and loss minimization must also be taken care of. Power system must be operated in such a way that both active and reactive powers are optimized simultaneously. Reactive powers should be optimized to provide better volt-age profile as well as to reduce system losses. In this paper, the proposed multi-objective optimal power flow (MO-OPF) problem is solved using particle swarm optimization (PSO) and Fuzzy satisfaction maximization approach. In this paper, it is assumed that the decision maker has imprecise or fuzzy goals of satisfying all the objectives, and the proposed problem is thus formulated as a fuzzy satisfaction maximization problem which is basically a min-max problem. It is an efficient technique to obtain trade-off solution for the proposed optimization problem. The MO-OPF problem is tested on IEEE 30 bus, 6 generator system. The obtained results are found to be effective for the MO-OPF problem.

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Wang, Ting, and Li Feng Li. "Decision Based on Fuzzy Multi-Objective Programming Method of High-Tech Investment Portfolio." Key Engineering Materials 474-476 (April 2011): 1961–65. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1961.

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The expected rate of earnings and risk of high-tech projects are very fuzzy, and investors hope to get the expected rate of earnings maximization and risk minimization. Therefore, this paper establishes the model of fuzzy multi-objective programming method to select an optimal portfolio scheme. On the one hand, the objectives risk can be scattered, on the other hand investors can get ideal earnings. The example shows that this method to solve problems of portfolio investment decision is feasible and effective.

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Rathee, Manisha, and T. V. Vijay Kumar. "DNA Fragment Assembly Using Multi-Objective Genetic Algorithms." International Journal of Applied Evolutionary Computation 5, no. 3 (July 2014): 84–108. http://dx.doi.org/10.4018/ijaec.2014070105.

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DNA Fragment Assembly Problem (FAP) is concerned with the reconstruction of the target DNA, using the several hundreds (or thousands) of sequenced fragments, by identifying the right order and orientation of each fragment in the layout. Several algorithms have been proposed for solving FAP. Most of these have solely dwelt on the single objective of maximizing the sum of the overlaps between adjacent fragments in order to optimize the fragment layout. This paper aims to formulate this FAP as a bi-objective optimization problem, with the two objectives being the maximization of the overlap between the adjacent fragments and the minimization of the overlap between the distant fragments. Moreover, since there is greater desirability for having lesser number of contigs, FAP becomes a tri-objective optimization problem where the minimization of the number of contigs becomes the additional objective. These problems were solved using the multi-objective genetic algorithm NSGA-II. The experimental results show that the NSGA-II-based Bi-Objective Fragment Assembly Algorithm (BOFAA) and the Tri-Objective Fragment Assembly Algorithm (TOFAA) are able to produce better quality layouts than those generated by the GA-based Single Objective Fragment Assembly Algorithm (SOFAA). Further, the layouts produced by TOFAA are also comparatively better than those produced using BOFAA.

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Wang, Cheng Jun, and Shu Ting Zhao. "Based on Multi-Objective Theory Researching on Location Problem of Heavy Metals Enterprises." Advanced Materials Research 864-867 (December 2013): 1078–82. http://dx.doi.org/10.4028/www.scientific.net/amr.864-867.1078.

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Industrialization bring a great deal of environmental burden in our country, heavy metal pollution problems have become increasingly prominent. In order to prevent and control the Heavy metals pollution effectively, this paper discusses the location problem of heavy metals enterprises. Through the discussion of the factors of heavy metals company location, based on the health protection distance, establish multi-objective model by achieving three goals: minimizing the risks of surrounding neighborhoods; fair maximization; Cost minimization. The optimal heavy metal enterprises location has certain practical significance in the control of heavy metal pollution events.

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Jafaryeganeh, Hamidreza, Manuel Ventura, and Carlos Guedes Soares. "Multi-Objective Optimization of Internal Compartment Layout of Oil Tankers." Journal of Ship Production and Design 35, no. 4 (November 1, 2019): 374–85. http://dx.doi.org/10.5957/jspd.09180034.

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This work deals with the design of the internal layout of a shuttle tanker formulated as a multi-objective optimization problem, balancing cargo capacity and minimizing still water bending moment with safety requirements, in particular survivability after damage. A parametric model is used to specify the internal layout of a tanker ship considering a fixed hull shape and regulatory framework. The design variables include positions of watertight members in the internal layout, such as watertight bulkhead position, double-bottom height, and wing tanks width. Merit functions are the minimization of oil outflow parameter, maximization of cargo capacity, and minimization of the longitudinal bending moment, which are, respectively, represented for reduction of environmental pollution due to damaged oil tankers, improvement of economic benefits, and safety during operation. The multi-objective genetic algorithm is used for approaching the Pareto frontiers, and the choices between the optimal designs are discussed while introducing a utility function.

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Lijesh, KP, Mrityunjay Doddamani, SI Bekinal, and SM Muzakkir. "Multi-objective optimization of stacked radial passive magnetic bearing." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 232, no. 9 (September 25, 2017): 1140–59. http://dx.doi.org/10.1177/1350650117733374.

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Modeling, design, and optimization for performances of passive magnetic bearings (PMBs) are indispensable, as they deliver lubrication free, friction less, zero wear, and maintenance-free operations. However, single-layer PMBs has lower load-carrying capacity and stiffness necessitating development of stacked structure PMBs for maximum load and stiffness. Present work is focused on multi-objective optimization of radial PMBs to achieve maximum load-carrying capacity and stiffness in a given volume. Three-dimensional Coulombian equations are utilized for estimating load and stiffness of stacked radial PMBs. Constraints, constants, and bounds for the optimization are extracted from the available literature. Optimization is performed for force and stiffness maximization in the obtained bounds with three PMB configurations, namely (i) mono-layer, (ii) conventional (back to back), and (iii) rotational magnetized direction. The optimum dimensions required for achieving maximum load without compromising stiffness for all three configurations is investigated. For designers ease, equations to estimate the optimized values of load, stiffness, and stacked PMB variables in terms of single-layer PMB are proposed. Finally, the effectiveness of the proposed method is demonstrated by considering the PMB dimensions from the available literature.

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Yang, Guangyu, and Ming Li. "Research of Service Industry's Customer Satisfaction Maximization Problem based on Multi-objective Programming Model." International Journal of u- and e-Service, Science and Technology 7, no. 5 (October 31, 2014): 95–104. http://dx.doi.org/10.14257/ijunesst.2014.7.5.09.

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Attea, Bara’a A., Enan A. Khalil, Suat Özdemir, and Oktay Yıldız. "A Multi-objective Disjoint Set Covers for Reliable Lifetime Maximization of Wireless Sensor Networks." Wireless Personal Communications 81, no. 2 (November 12, 2014): 819–38. http://dx.doi.org/10.1007/s11277-014-2159-3.

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Basto-Fernandes, Vitor, Iryna Yevseyeva, José R. Méndez, Jiaqi Zhao, Florentino Fdez-Riverola, and Michael T.M. Emmerich. "A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification." Applied Soft Computing 48 (November 2016): 111–23. http://dx.doi.org/10.1016/j.asoc.2016.06.043.

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Chowdhury, Archana, Pratyusha Rakshit, and Amit Konar. "Prediction of protein–protein interaction network using a multi-objective optimization approach." Journal of Bioinformatics and Computational Biology 14, no. 03 (June 2016): 1650008. http://dx.doi.org/10.1142/s0219720016500086.

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Protein–Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.

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Bozorg-Haddad, Omid, Irene Garousi-Nejad, and Hugo A. Loáiciga. "Extended multi-objective firefly algorithm for hydropower energy generation." Journal of Hydroinformatics 19, no. 5 (June 9, 2017): 734–51. http://dx.doi.org/10.2166/hydro.2017.114.

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Classical methods have severe limitations (such as being trapped in local optima, and the curse of dimensionality) to solve optimization problems. Evolutionary or meta-heuristic algorithms are currently favored as the tools of choice for tackling such complex non-linear reservoir operations. This paper evaluates the performance of an extended multi-objective developed firefly algorithm (MODFA). The MODFA script code was developed using the MATLAB programming language and was applied in MATLAB to optimize hydropower generation by a three-reservoir system in Iran. The two objectives used in the present study are the maximization of the reliability of hydropower generation and the minimization of the vulnerability to generation deficits of the three-reservoir system. Optimal Paretos (OPs) obtained with the MODFA are compared with those obtained with the multi-objective genetic algorithm (MOGA) and the multi-objective firefly algorithm (MOFA) for different levels of performance thresholds (50%, 75%, and 100%). The case study results demonstrate that the MODFA is superior to the MOGA and MOFA for calculating proper OPs with distinct solutions and a wide distribution of solutions. This study's results show that the MODFA solves multi-objective multi-reservoir operation system with the purpose of hydropower generation that are highly nonlinear that classical methods cannot solve.

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Grzyb, Sławomir, and Przemysław Orłowski. "Multi-Objective Control Optimization for Congestion Avoidance in Computer Networks." Modelling, Measurement and Control A 93, no. 1-4 (December 31, 2020): 39–44. http://dx.doi.org/10.18280/mmc_a.931-406.

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Effective congestion control is an issue strongly impacting basic features demanded from modern network environment as reliability, high and stable throughput, and low delays. These characteristics define the quality of communication channels. Optimizing network nodes configuration for only one of mentioned features, can exacerbate other parameters. This paper focuses on avoiding and alleviating network congestions using multi-objective optimization for gain setting of used controllers. Unlike in other presented approaches, in this case the non-stationary, discrete, dynamical model is discussed. The significant advantage of this approach is in the better reflection of the real environment conditions, where the transmission delay is floating. As the further development of the control strategy, the controller with the memory of previous steps have been deployed. Such control strategy mitigates the unfavorable impact of extended delays. Both proposed control strategies tune the presented model of communication channel to alleviate the results of sudden, unexpected network state changes. It is obtained by maximization of available bandwidth usage combined with minimization of buffer utilization. This supports avoiding undesirable congestion effects like packet dropping, retransmissions, high delay, and low network throughput.

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Pandey, M. K., M. K. Tiwari, and M. J. Zuo. "Interactive enhanced particle swarm optimization: A multi-objective reliability application." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 221, no. 3 (September 1, 2007): 177–91. http://dx.doi.org/10.1243/1748006xjrr51.

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In reliability optimization problems, it is desirable to address different conflicting objectives. This generally includes maximization of system reliability and minimization of cost, weight, and volume. The proposed algorithm of a metaheuristic nature is designed to address multi-objective problems. In the presented algorithm, interaction with a decision maker guides the search towards the preferred solution. A comparison between an existing solution and the newly generated solution substantiates the desirability or fitness of the latter. Further, the utility function expresses the preference information of the decision maker while searching for the best solution. During the development of the algorithm, a new variant of particle swarm optimization (PSO) is proposed and named as ‘enhanced particle swarm optimization’ (EPSO). EPSO considers the difference between the particle's best position and the global best position for efficient search and convergence. The developed algorithm is applied to the reliability optimization problem of a multistage mixed system with four different value functions that are used to simulate the designer's opinion in the solution evaluation process. Results indicate that the algorithm effectively captures the decision maker's preferences for different structures. Superior results in multi-objective reliability problem-solving prove the algorithm's superiority over other approaches.

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Lotfi, Reza, Yahia Zare Mehrjerdi, and Nooshin Mardani. "A Multi-Objective and Multi-Product Advertising Billboard Location Model with Attraction Factor Mathematical Modeling and Solutions." International Journal of Applied Logistics 7, no. 1 (January 2017): 64–86. http://dx.doi.org/10.4018/ijal.2017010104.

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Location of advertising is one of the most important factors of marketing strategy, as finding the best location to install advertising billboards can have a major impact on profitability of the entire marketing process. This paper provides a billboard location model, which can determine the optimal locations for installing such billboards. The multi-objective and multi-product model developed for this purpose has two objective functions: optimizing the sales profit minus the costs of designing and installing the billboards, and attracting most visitors through maximization of an attraction factor. The designing cost is assumed to be associated with the attraction factor. This model finds the best location of billboards based on constraint such as number of visits and sales volume. Finally, a set of small and large-scale numerical examples are solved by implementing the solution method in GAMS\Cplex solver software. To solve the large-scale variants of the problem, the genetic algorithm.

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Sedghamiz, Abbas, Manouchehr Heidarpour, Mohammad Reza Nikoo, and Saeed Eslamian. "A Game Theory Approach for Conjunctive Use Optimization Model Based on Virtual Water Concept." Civil Engineering Journal 4, no. 6 (July 4, 2018): 1315. http://dx.doi.org/10.28991/cej-0309175.

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In this study to allocate the agricultural and environmental water, considering virtual water concept, a multi-objective optimization model based on NSGA-II is developed. The objectives consist of equity maximization, agricultural benefit maximization for each region, maximization of green water utilization and finally minimization of environmental shortage. Then a cooperative game (Grand Coalition) model is presented by forming all possible coalitions. By the game model including Nucleolus, Proportional Nucleolus, Normal Nucleolus and Shapley methods, the benefit is reallocated based on all Pareto optimal solutions obtained from multi-objective optimization model. Then using two famous fallback bargaining methods, Unanimity and q-Approval, preferable alternative (solution) for each of the cooperative games is determined. Finally, based on the obtained benefit for each selected alternatives, the two most beneficial alternatives are chosen. The proposed methodology applied for water allocation of Minoo-Dasht, Azad-Shahr and Gonbad-Kavoos cities in Golestan province, Iran for a 3-year period as a case study. Also, eight crops including Wheat, Alfalfa, Barley, Bean, Rice, Corn, Soya, and Cotton are selected based on local experts’ recommendations. The models’ results indicated no significant difference between the grand coalition model and the multi-objective optimization model in terms of the average cultivation area (a relative change of 2.1%), while lower agricultural water allocation occurred for the grand coalition model (about 10.35 percent average) compared with the multi-objective optimization model. It is also observed that more agricultural benefit gained by the grand coalition model (32 percent average). Finally, it is found that Wheat and Corn hold the most rates of import and export, respectively, and Rice was the crop which has the least shortage of production to supply food demand.

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Wang, Xin, Jing Xu, Ke Kong, Lei Yan, and Fang Wu. "The Multi-Objective Optimization Allocation Model of Water Resources in Xiaokai River Irrigation Area." Advanced Materials Research 1092-1093 (March 2015): 1289–94. http://dx.doi.org/10.4028/www.scientific.net/amr.1092-1093.1289.

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For the three big problems of water resources supply and demand contradiction, protection of groundwater environment and sediment over long distances in Xiaokai river irrigation area, the model of water utilization benefit maximization, groundwater level optimal control and the goal of sediment transport effect optimization model are established, and coupled into a multi-objective optimization model. The model is solved by using The delaminating sequence method, obtained the rational allocation plan of water resources in water years, and analyzing the rationality of the plan. The results show that, the scheme comprehensively considers the economic and environmental issues and has great reference value to promote sustainable development of irrigation area.

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Gutierrez-Franco, Edgar, Andres Polo, Nicolas Clavijo-Buritica, and Luis Rabelo. "Multi-Objective Optimization to Support the Design of a Sustainable Supply Chain for the Generation of Biofuels from Forest Waste." Sustainability 13, no. 14 (July 12, 2021): 7774. http://dx.doi.org/10.3390/su13147774.

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The production and supply chain management of biofuels from organic waste as raw material has been identified as a promising strategy in the field of renewable energies and circular economy initiatives. This industry involves complex tasks such as strategic land use, feedstock purchasing, production plant location, production capacity strategy, and material flows, which can be solved by mathematical modeling. The study proposed a multi-objective mixed-integer linear programming model to design a sustainable supply chain of biofuels with forest residues from its triple function: economic, environmental, and social. The trade-offs between the proposed objectives were determined with computational results. The proposed objectives were profit maximization, CO2 minimization, and employment generation maximization. Thus, the proposed model serves as a tool for decision-making, allowing the projection of a long-term structure of the biofuel supply chains and contribute to the United Nations Sustainable Development Goals.

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Hossain, Shah Mohazzem, and Abdul Hasib Chowdhury. "Multi-objective optimal placement of distributed generations for dynamic loads." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 2303. http://dx.doi.org/10.11591/ijece.v9i4.pp2303-2313.

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Large amount of active power losses and low voltage profile are the two major issues concerning the integration of distributed generations with existing power system networks. High R/X ratio and long distance of radial network further aggravates the issues. Optimal placement of distributed generators can address these issues significantly by alleviating active power losses and ameliorating voltage profile in a cost effective manner. In this research, multi-objective optimal placement problem is decomposed into minimization of total active power losses, maximization of bus voltage profile enhancement and minimization of total generation cost of a power system network for static and dynamic load characteristics. Optimum utilization factor for installed generators and available loads is scaled by the analysis of yearly load-demand curve of a network. The developed algorithm of N-bus system is implemented in IEEE-14 bus standard test system to demonstrate the efficacy of the proposed method in different loading conditions.

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KARA, Nurdan, and Hale KÖÇKEN. "New Solution Approaches for Multi-Objective Solid Transportation Problem Using Some Aggregation Operators." Journal of the Institute of Science and Technology 12, no. 3 (September 1, 2022): 1776–89. http://dx.doi.org/10.21597/jist.1107648.

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A solid transportation problem emerges when the decision variables are represented by three items: the source, the destination, and the mode of transport. In applications, the STP generally requires considering multiple objectives such as cost minimization, time minimization, security level maximization, etc. In this way, a multi-objective solid transportation problem arises. This paper deals with the solution of the problem and analyzes the effect of several important fuzzy aggregation operators on the solution of the problem. In this context, the most commonly used aggregation operators are investigated for this problem. To explain the solution approach, a numerical example from the literature is given and a Pareto-optimal solution set is provided to offer the decision-maker. Furthermore, graphical comparisons and sensitivity analysis are presented with the solution obtained.

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Gopu, Arunkumar, and NeelaNarayanan Venkataraman. "Virtual Machine Placement Using Multi-Objective Bat Algorithm With Decomposition in Distributed Cloud." International Journal of Applied Metaheuristic Computing 12, no. 4 (October 2021): 62–77. http://dx.doi.org/10.4018/ijamc.2021100104.

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Virtual machine placement in cloud computing considering multiple objectives is one of the significant issues in modern virtualized datacenters. Many businesses and organizations are outsourcing their computational workload to the cloud datacenters, which increases datacenter energy consumption and emission of CO2. In particular, allocating a virtual machine to a physical server in the community cloud model is even challenging due to its dynamic nature. Unlike public clouds, cloud servers are not always available in the same location. In this paper, a bio-inspired bat algorithm using decomposition (MOBA/D) is proposed to reduce three different objectives namely minimization of power consumption, minimization of network latency, and maximization of economical revenue. The performance of the proposed algorithm is compared with other multi-objective algorithms in terms of feasible solutions and execution time.

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Singaravel, B., T. Selvaraj, and S. Vinodh. "MULTI-OBJECTIVE OPTIMIZATION OF TURNING PARAMETERS USING THE COMBINED MOORA AND ENTROPY METHOD." Transactions of the Canadian Society for Mechanical Engineering 40, no. 1 (March 2016): 101–11. http://dx.doi.org/10.1139/tcsme-2016-0008.

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Selection of optimum machining parameters in machining operations leads to good functional attributes for the machined components and increased productivity. In this work, machining parameters and nose radius are optimized in turning of EN25 steel with coated carbide tool by the application of combined Multi-Objective Optimization by Ratio Analysis (MOORA) and entropy measurement method. The selected machining parameters are cutting speed, feed rate, depth of cut and nose radius for minimization of surface roughness, micro-hardness and maximization of Material Removal Rate (MRR). Entropy concept has been used to assign the weight criteria of each objective being considered. The optimum combination of machining parameters and nose radius are obtained using normalized assessment values. The results obtained in the analysis are validated and the results based on turning process responses can be effectively improved.

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Khalili, Ata, Mohammad Robat Mili, Mehdi Rasti, Saeedeh Parsaeefard, and Derrick Wing Kwan Ng. "Antenna Selection Strategy for Energy Efficiency Maximization in Uplink OFDMA Networks: A Multi-Objective Approach." IEEE Transactions on Wireless Communications 19, no. 1 (January 2020): 595–609. http://dx.doi.org/10.1109/twc.2019.2946832.

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Rangel, Elivelton O., Daniel G. Costa, and Angelo Loula. "On redundant coverage maximization in wireless visual sensor networks: Evolutionary algorithms for multi-objective optimization." Applied Soft Computing 82 (September 2019): 105578. http://dx.doi.org/10.1016/j.asoc.2019.105578.

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VIVEKANANDAN, N., and K. VISWANATHAN. "Optimization of multi-objective cropping pattern using linear and goal programming approaches." MAUSAM 58, no. 3 (November 26, 2021): 323–34. http://dx.doi.org/10.54302/mausam.v58i3.1326.

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Irrigation planning and scheduling are essential components of water management in irrigated agriculture. For this purpose, optimal allocation of land and water is required for optimization of cropping pattern under a set of limitations. In this paper, an attempt was made to optimize the cropping pattern for Barna irrigation project using Linear and Goal Programming (LP and GP) approaches. Three different objectives such as maximization of net return, protein and calorie values were considered for optimization of cropping pattern. The factors like amount of net return, values of protein and calorie, and quantum of water utilized for irrigation by LP and GP were considered for selection of best approach for optimization of cropping pattern for the project. The paper presents the methodology adopted in optimizing the cropping pattern using LP and GP approaches and the results obtained from the study. GP approach was found to be best for optimization of cropping pattern for the project.

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Wu, Zhiqiao, and Jiafu Tang. "Designing and Reporting on Computational Experiments of Multi-objective Component Selection Algorithm." International Journal of Information Technology & Decision Making 14, no. 02 (March 2015): 375–94. http://dx.doi.org/10.1142/s0219622015500066.

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One important issue of component-based software development is the minimization of the development cost and the maximization of the system reliability while satisfying functional requirements. There are numerous publications on this issue based on metaheuristic techniques, but there are two deficiencies: too tough to evaluate the performance of algorithms and fix parameters in the real-world application. To address this problem, a three phased algorithm is proposed by the Wu et al. [International Journal of Information Technology & Decision Making5 (2011) 811–841]. This paper describes computational experience in solving the problems using the metaheuristics and the proposed algorithm. The results indicate the efficiency of the proposed algorithms in terms of overall nondominated vector generation, well converged set of solutions, and diversity of solutions. Computational results and simulation analysis further assist a decision maker to fix optimal parameters of metaheuristics including the number of iteration, crossover rate, and mutation rate, and explore hints in using metaheuristics for the problem.

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Li, Chunquan. "A fuzzy multi-objective linear programming with interval-typed triangular fuzzy numbers." Open Mathematics 17, no. 1 (June 27, 2019): 607–26. http://dx.doi.org/10.1515/math-2019-0048.

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Abstract A multi-objective linear programming problem (ITF-MOLP) is presented in this paper, in which coefficients of both the objective functions and constraints are interval-typed triangular fuzzy numbers. An algorithm of the ITF-MOLP is provided by introducing the cut set of interval-typed triangular fuzzy numbers and the dominance possibility criterion. In particular, for a given level, the ITF-MOLP is converted to the maximization of the sum of membership degrees of each objective in ITF-MOLP, whose membership degrees are established based on the deviation from optimal solutions of individual objectives, and the constraints are transformed to normal inequalities by utilizing the dominance possibility criterion when compared with two interval-typed triangular fuzzy numbers. Then the equivalent linear programming model is obtained which could be solved by Matlab toolbox. Finally several examples are provided to illuminate the proposed method by comparing with the existing methods and sensitive analysis demonstrates the stability of the optimal solution.

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Boindala, Sriman Pankaj, and Avi Ostfeld. "Robust Multi-Objective Design Optimization of Water Distribution System under Uncertainty." Water 14, no. 14 (July 12, 2022): 2199. http://dx.doi.org/10.3390/w14142199.

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The multi-objective design optimization of water distribution systems (WDS) is to find the Pareto front of optimal designs of WDS for two or more conflicting design objectives. The most popular conflicting objectives considered for the design of WDS are minimization of cost and maximization of resilience index which are considered for the current study. Robust multi-objective optimization is to find the optimal set of the Pareto front considering demand is uncertain. The robustness is controlled by a single parameter that defines the size of the uncertainty set it can vary. The study explores ellipsoidal uncertainty set with different sizes and co-variance matrices. A combined simulation–optimization framework with a combination of self-adaptive multi-objective cuckoo search (SAMOCSA) and the fmincon optimization algorithm is proposed to solve the robust multi-objective design problem. The proposed algorithm is applied to medium and large WDS. The main contribution of this paper is to study the effect of demand uncertainty and the correlation on the WDS designs in a multi-objective framework. The study shows that the inclusion of correlation into the multi-objective design framework can significantly affect the optimal designs.

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Arulmozhi, P., M. Chandrasekaran, and S. Padmanabhan. "Multi Objective Optimization for Spur Gear Design Using Sheep Flocks Heredity Model Algorithm." Applied Mechanics and Materials 591 (July 2014): 68–71. http://dx.doi.org/10.4028/www.scientific.net/amm.591.68.

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In a perspective of stable industrial development to manufacture added consistent and economical industrial product, gears are ever more focus to requirements in terms of power capability, efficiency and compactness etc. In order to increase the performance factors of gears such as transmission capacity, efficiency, gear life, etc. is a difficult criteria for a design engineers as these are all progress in a conflicting behavior. This paper deals with the multi-objective optimization of spur gear drive design with two contradictory objective functions such as maximization of power transmission and minimization of volume of the gear drive. These objectives are approached by an optimization technique based on a Sheep Flocks Heredity Model Algorithm (SFHM) with design constraints like stress, center distance etc. A spur gear problem is solved with traditional trial method and results are compared with proposed algorithm.

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Xiao, Yu, and Xiaoxiang Hu. "Waveform Design for Multi-Target Detection Based on Two-Stage Information Criterion." Entropy 24, no. 8 (August 3, 2022): 1075. http://dx.doi.org/10.3390/e24081075.

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Parameter estimation accuracy and average sample number (ASN) reduction are important to improving target detection performance in sequential hypothesis tests. Multiple-input multiple-output (MIMO) radar can balance between parameter estimation accuracy and ASN reduction through waveform diversity. In this study, we propose a waveform design method based on a two-stage information criterion to improve multi-target detection performance. In the first stage, the waveform is designed to estimate the target parameters based on the criterion of single-hypothesis mutual information (MI) maximization under the constraint of the signal-to-noise ratio (SNR). In the second stage, the objective function is designed based on the criterion of MI minimization and Kullback–Leibler divergence (KLD) maximization between multi-hypothesis posterior probabilities, and the waveform is chosen from the waveform library of the first-stage parameter estimation. Furthermore, an adaptive waveform design algorithm framework for multi-target detection is proposed. The simulation results reveal that the waveform design based on the two-stage information criterion can rapidly detect the target direction. In addition, the waveform design based on the criterion of dual-hypothesis MI minimization can improve the parameter estimation performance, whereas the design based on the criterion of dual-hypothesis KLD maximization can improve the target detection performance.

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CHEN, LIANG-HSUAN, and CHENG-HSIUNG CHIANG. "MULTI-OBJECTIVE OPTIMIZATION IN RELIABILITY SYSTEM USING GENETIC ALGORITHM AND NEURAL NETWORK." Asia-Pacific Journal of Operational Research 25, no. 05 (October 2008): 649–72. http://dx.doi.org/10.1142/s0217595908001936.

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To optimize the design of reliability systems, an analyst is frequently faced with the demand of achieving several targets (i.e., maximization of system reliability, minimizations of cost, volume, and weight), some of which may be in conflict with each other. This paper presents a novel hybrid approach, combining a multi-objective genetic algorithm and a neural network, for multi-objective optimization of a reliability system, namely GANNRS (Genetic Algorithm and Neural Network for Reliability System optimization). The multi-objective genetic algorithm's evolutionary strategy is based on the modified neighborhood design, and is presented to find the Pareto optimal solutions so as to provide a variety of compromise solutions to the decision makers. The purpose of the neural network is to generate a good initial population in order to speed up the searching by genetic algorithm. For demonstrating the feasibility of the proposed approach, four multi-objective optimization problems of reliability system are used, and the outcomes are compared with those from other methods. The evidence shows that the proposed GANNRS is more efficient in computation, and the results from the objectives are appealing.

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Hafyan, R. H., W. D. Prasetyo, L. Bhullar, Z. A. Putra, M. R. Bilad, M. D. H. Wirzal, and N. A. H. M. Nordin. "Multi-objective Optimization of Succinic Acid Production from Empty Fruit Bunch." ASEAN Journal of Chemical Engineering 19, no. 1 (October 24, 2019): 1. http://dx.doi.org/10.22146/ajche.50870.

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Empty Fruit Bunch (EFB) produced in plantation mill activities in Malaysia creates a major disposal problem. On the other hand, sustainability issues have driven industries to overcome the depletion of fossil fuels and reduction of greenhouse gases emissions. Therefore, as a renewable source, EFB can be an attractive option to address the above problems by converting it into fuels and chemicals. Succinic acid, one of 12 chemical building blocks identified by DOE to be used in synthesis of high-value materials, can be produced from biochemical conversion of the EFB. The present study evaluates succinic acid production process using EFB as the raw material from the perspective of three pillars of sustainability, namely economic, environment, and safety. Flowsheet modeling and techno-economic analysis methods are applied, followed by a multi-objective optimization using genetic algorithm method that simultaneously accounts for maximization of Net Present Value (NPV) and minimization of both Global Warming Potential (GWP) and Toxicity Damage Index (TDI). The pareto frontier reveals a trade-off among all objectives that the maximum NPV is 1,619 MMSD at the maximum EFB of 71,900 kg/hour. Meanwhile, the minimum GWP (12.4 kg CO2-eq/kg succinic acid) and TDI (4.5) are acquired at the minimum EFB of 50,000 kg/hour.

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Rajagopal, S., and R. Ganguli. "Conceptual design of UAV using Kriging based multi-objective genetic algorithm." Aeronautical Journal 112, no. 1137 (November 2008): 653–62. http://dx.doi.org/10.1017/s0001924000002621.

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Abstract This paper highlights unmanned aerial vehicle (UAV) conceptual design using the multi-objective genetic algorithm (MOGA). The design problem is formulated as a multidisciplinary design optimisation (MDO) problem by coupling aerodynamic and structural analysis. The UAV considered in this paper is a low speed, long endurance aircraft. The optimisation problem uses endurance maximization and wing weight minimisation as dual objective functions. In this multi-objective optimisation, aspect ratio, wing loading, taper ratio, thickness-to-chord ratio, loiter velocity and loiter altitude are considered as design variables with stall speed, maximum speed and rate of climb as constraints. The MDO system integrates the aircraft design code, RDS and an empirical relation for objective function evaluation. In this study, the optimisation problem is solved in two approaches. In the first approach, the RDS code is directly integrated in the optimisation loop. In the second approach, Kriging model is employed. The second approach is fast and efficient as the meta-model reduces the time of computation. A relatively new multi-objective evolutionary algorithm named NSGA-II (non-dominated sorting genetic algorithm) is used to capture the full Pareto front for the dual objective problem. As a result of optimisation using multi-objective genetic algorithm, several non-dominated solutions indicating number of useful Pareto optimal designs is identified.

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Imran, Muhammad, Muhammad Salman Habib, Amjad Hussain, Naveed Ahmed, and Abdulrahman M. Al-Ahmari. "Inventory Routing Problem in Supply Chain of Perishable Products under Cost Uncertainty." Mathematics 8, no. 3 (March 9, 2020): 382. http://dx.doi.org/10.3390/math8030382.

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This paper presents a multi-objective, multi-period inventory routing problem in the supply chain of perishable products under uncertain costs. In addition to traditional objectives of cost and greenhouse gas (GHG) emission minimization, a novel objective of priority index maximization has been introduced in the model. The priority index quantifies the qualitative social aspects, such as coordination, trust, behavior, and long-term relationships among the stakeholders. In a multi-echelon supply chain, the performance of distributor/retailer is affected by the performance of supplier/distributor. The priority index measures the relative performance index of each player within the supply chain. The maximization of priority index ensures the achievement of social sustainability in the supply chain. Moreover, to model cost uncertainty, a time series integrated regression fuzzy method is developed. This research comprises of three phases. In the first phase, a mixed-integer multi-objective mathematical model while considering the cost uncertainty has been formulated. In order to determine the parameters for priority index objective function, a two-phase fuzzy inference process is used and the rest of the objectives (cost and GHG) have been modeled mathematically. The second phase involves the development of solution methodology. In this phase, to solve the mathematical model, a modified interactive multi-objective fuzzy programming has been employed that incorporates experts’ preferences for objective satisfaction based on their experiences. Finally, in the third phase, a case study of the supply chain of surgical instruments is presented as an example. The results of the case provide optimal flow of products from suppliers to hospitals and the optimal sequence of the visits of different vehicle types that minimize total cost, GHG emissions, and maximizes the priority index.

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Mirajkar, A. B., and P. L. Patel. "Development of sustainable irrigation planning with multi-objective fuzzy linear programming for Ukai–Kakrapar irrigation project, Gujarat, India." Canadian Journal of Civil Engineering 40, no. 7 (July 2013): 663–73. http://dx.doi.org/10.1139/cjce-2013-0090.

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Multi-objective fuzzy linear programming (MOFLP) approach is applied with four conflicting objectives, viz maximization of net benefits, employment generation, minimization of cost of cultivation and maximization of revenue generation from municipal and industrial supplies (M and I), on a water resources project (Ukai), Gujarat, India. The results from the model are reported for the most critical year (90% exceedance probability), critical year (85% exceedance probability), normal year (75% exceedance probability), and wet year (60% exceedance probability) inflow conditions. The degree of satisfaction of the proposed MOFLP model, considering all objectives together, for wet year, normal year, critical year and most critical year are found to be 0.527, 0.515, 0.50, and 0.46 respectively; and corresponding net irrigation benefits for different inflow conditions are computed as 10 611.91 Million Rs, 10 476.67 Million Rs, 8 311.0044 Million Rs, and 6 900.051 Million Rs, respectively. The proposed MOFLP model indicated that probable inflow corresponding to 75% dependability level is marginally sufficient to meet the requirement of the study area, and water availability becomes deficit in the command area for 85% dependability inflow condition. The optimized crop areas from the model, complying with the requirement of existing flood rules, and satisfying relevant conflicting objectives would help the decision makers in sustainable management of water resources in Ukai command area.

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Abou El-Ela, Adel A., Ragab A. El-Sehiemy, Abdullah M. Shaheen, Walaa A. Wahbi, and Mohamed T. Mouwafi. "A multi-objective equilibrium optimization for optimal allocation of batteries in distribution systems with lifetime maximization." Journal of Energy Storage 55 (November 2022): 105795. http://dx.doi.org/10.1016/j.est.2022.105795.

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Journal articles on the topic 'Multi-objective maximization'

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