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Tiered Cloud Storage via Two-Stage, Latency-Aware Bidding

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2019年第1期16卷 176-191页

作者： Zhang, Yang Ghosh, Arnob Aggarwal, Vancet Lan, Tian Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA

In cloud storage, the digital data is stored in logical storage pools, backed by heterogeneous physical storage media and computing infrastructure that are managed by a cloud service provider (CSP). One of the key advantages of cloud storage is its elastic pricing mechanism, in which the users need only pay for the resources/services they actually use, e.g., depending on the storage capacity consumed, the number of file accesses per month, and the negotiated service level agreement. To balance the tradeoff between service performance and cost, CSPs often employ different storage tiers, for instance, cold storage and hot storage. Storing data in hot storage incurs high storage cost yet delivers low access latency, whereas cold storage is able to inexpensively store massive amounts of data and thus provides lower cost with higher latency. In this paper, we address a major challenge confronting the CSPs utilizing such tiered storage architecture-how to maximize their overall profit over a variety of storage tiers that offer distinct characteristics, as well as file placement and access request scheduling policies. To this end, we propose a scheme where the CSP offers a twostage auction process for: 1) requesting storage capacity and 2) requesting accesses with latency requirements. Our two-stage bidding scheme provides a hybrid storage and access optimization framework with the objective of maximizing the CSP's total net profit over four dimensions: file acceptance decision, placement of accepted files, file access decision and access request scheduling policy. The proposed optimization is a mixed-integer nonlinear program that is hard to solve. We propose an efficient heuristic to relax the integer optimization and to solve the resulting nonlinear stochastic programs. The algorithm is evaluated under different scenarios and with different storage system parameters, and insightful numerical results are reported by comparing the proposed approach with other profit-maxim

关键词： Cloud storage latency dependent pricing two-stage bidding mixed-integer nonlinear programming

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IAs based approach for reliability redundancy allocation problems

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APPLIED MATHEMATICS AND COMPUTATION 2006年第2期182卷 1556-1567页

作者： Chen, Ta-Cheng Natl Formosa Univ Dept Informat Management Yuanlin 632 Taiwan

nonlinearly mixed-integer reliability design problems are investigated in this paper where both the number of redundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximize the reliability of system. The reliability design problems have been studied in the literature for decades, usually using mathematical programming or heuristic/metaheuristic optimization approaches. The difficulties confronted for both methodologies are to maintain feasibility with respect to three nonlinear constraints, namely, cost, weight and volume related constraints. A penalty guided artificial immune algorithm is presented for solving such mixed-integer reliability design problems. It can search over promising feasible and infeasible regions to find the feasible optimal/near optimal solution effectively and efficiently. Numerical examples indicate that the proposed approach performs well for the reliability-redundant allocation design problems considered in this paper. As reported, solutions obtained by the proposed approach are as well as or better than the previously best-known solutions. (c) 2006 Elsevier Inc. All rights reserved.

关键词： reliability design immune algorithm mixed-integer nonlinear programming

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Optimising production and distribution operations in large water supply networks: A piecewise linear optimisation approach

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2013年第23-24期51卷 7170-7189页

作者： Verleye, Derek Aghezzaf, El-Houssaine Univ Ghent Dept Ind Management Fac Engn B-9000 Ghent Belgium

Optimal planning of water production and distribution operations in large pressurised water supply networks is one of the most challenging problems in production planning. Its underlying optimisation model is typically formulated as a mixed-integer nonlinear programme (MINLP) involving a large number of nonconvex hydraulic constraints. This makes the optimisation task in an actual water supply network particularly complex. After a brief overview of the existing literature on water supply optimisation, we highlight the major similarities and differences between the water production and distribution operations planning problem and the common production planning problems, and discuss its specific challenges. We then present a complete and sufficiently accurate optimisation model to generate feasible operating plans for water production and distribution in a typical large-scale network. Next, we propose a heuristic method, based on piecewise linearisation, to determine close to optimal solutions in reasonable computational time. Results obtained for some instances generated from an actual drinking water network operated by De Watergroep in Flanders, Belgium, are reported and discussed. Finally, some concluding remarks on the computational difficulties to solve practical water supply optimisation instances in large-scale networks are presented.

关键词： large-scale production and supply networks mixed-integer nonlinear programming cost and energy efficiency piecewise linear approximation

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Solving continuous and discrete nonlinear programs with BARON

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2024年 1-39页

作者： Zhang, Yi Sahinidis, Nikolaos V. Optimizat Firm LLC Shanghai Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Chem & Biomol Engn Atlanta GA 30332 USA

Under development since the early 1990s, BARON has become a highly robust and efficient computational system for solving nonconvex continuous and discrete optimization problems to global optimality. This work discusses key features that were introduced to BARON in the past decade, including hybrid relaxations, linear and nonlinear presolve methods, enhanced convexification methods, heuristics, and various robustness enhancements. A systematic computational comparison on benchmark libraries is presented among various state-of-the-art local and global codes for nonlinear and mixed-integer nonlinear programs. The results demonstrate the benefits from the newly added algorithmic facilities, and the leading performance of BARON over both local and global solvers.

关键词： Global optimization mixed-integer nonlinear programming nonlinear programming Computation BARON

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An integrated multi-objective optimization, evaluation, and decision-making method for ship energy system

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APPLIED ENERGY 2024年 373卷

作者： Wu, Nianyuan Zhang, Fang Zhang, Fuzheng Jiang, Chenxing Lin, Jian Xie, Shan Jing, Rui Zhao, Yingru Xiamen Univ Coll Energy Xiamen 361005 Peoples R China State Power Investment Corp State Nucl Elect Power Planning Design & Res Inst Beijing Peoples R China

With the rapid development of global shipping industry, the energy conservation and emission reduction issue of ships has received increasing attention from the international community. A series of regulations released by the International Maritime Organization (IMO) have greatly stimulated the related research. However, the mechanism of optimization design and evaluation of marine ship energy system at the whole ship level based on the comprehensive consideration of various performance of ships is still unclear. In the present study, an integrated modeling method of multi-objective optimization and evaluation for ship energy system are proposed to optimize the design of ship energy system. This paper constructs a technical selection, capacity allocation, and operational optimization model for ship energy systems using mixed-integer nonlinear programming (MINLP) method. Based on determining relevant constraints and multiple objective functions, the data envelopment analysis (DEA) method is introduced to evaluate and make decisions on the multi-objective optimization results, and compared with traditional decision-making method. The economic optimization results show that the net present cost of ship energy system can be reduced by 20%, while the NOx, SOx and CO2 emissions of the system are reduced by 41%, 20% and 32%, respectively. Moreover, the energy efficiency of the optimized ship energy system is improved by 9% compared to the conventional ship energy system. In summary, the proposed method can effectively identify the advantages and disadvantages of different solutions, therefore provide more design options of sub-optimal schemes for decision makers.

关键词： Ship energy system mixed-integer nonlinear programming Multi-objective optimization Data envelopment analysis

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Using short-term resource scheduling for assessing effectiveness of CCS within electricity generation subsector

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AICHE JOURNAL 2015年第12期61卷 4210-4234页

作者： Alie, Colin Elkamel, Ali Croiset, Eric Douglas, Peter L. Univ Waterloo Dept Chem Engn Waterloo ON N2L 3G1 Canada

A new methodology for assessing the effectiveness of carbon capture and storage (CCS) that does explicitly consider the detailed operation of the target electricity system is proposed. The electricity system simulation consists of three phases, each one using a modified version of an economic dispatch problem that seeks to maximize the producers' and consumers' surplus while satisfying the technical constraints of the system. The economic dispatch is formulated as a dynamic mixed-integer nonlinear programming problem and implemented in general algebraic modelling system (GAMS). The generating unit with CCS is designed and simulated using Aspen Plus (R). In the first case study, the operation of the IEEE RTS '96 (Institute of Electrical and Electronics Engineers One-Area Reliability Test System1996) is simulated with greenhouse gas (GHG) regulation implemented in the form of CO2 permits that generators need to acquire for every unit of CO2 that it is emitted. In the second case study, CCS is added at one of the buses and the operation of the modified IEEE RTS '96 is again simulated with and without GHG regulation. The results suggest that the detailed operation of the target electricity system should be considered in future assessments of CCS and a general procedure for undertaking this for any GHG mitigation option is proposed. Future work will use the novel methodology for assessing the effectiveness of generating units with flexible CO2 capture. (c) 2015 American Institute of Chemical Engineers

关键词： process simulation energy environmental engineering optimization postcombustion CO2 capture carbon capture and storage greenhouse gas mitigation mixed-integer nonlinear programming short-term generation scheduling

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Reliability optimization and redundancy allocation for fire extinguisher drone using hybrid PSO-GWO

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SOFT COMPUTING 2023年第20期27卷 14819-14833页

作者： Bhandari, Ashok Singh Kumar, Akshay Ram, Mangey Shri Guru Ram Rai Univ Sch Basic & Appl Sci Dept Math Dehra Dun India Graph Era Hill Univ Dept Math Dehra Dun Uttarakhand India Graph Era Univ Dept Math Comp Sci & Engn Dehra Dun Uttarakhand India

Reliability-redundancy allocation problem (RRAP) plays a vital role in reliability improvement and designing of systems which depend on the arrangement of components, reliability of the components, and redundancy allocation for the components. Higher reliability is the primary requisite for essential systems such as fire extinguisher drones (FEDs) which are very valuable for firefighters in tackling emergencies in non-reachable areas. In this work, a FED is considered with the aim of system designing for maximum reliability while considering the limited availability of resources such as volume, cost, and weight of the system. A total of five possible arrangements of the redundant components are investigated, and a mixed-integer nonlinear programming problem is solved for system reliability optimization. For optimization purposes, a recently developed metaheuristic hybrid particle swarm grey wolf optimizer (HPSGWO) is implemented. The HPSGWO is a powerful fusion of PSO's exploitation property and GWO's exploration property. Solving RRAP by using HPSGWO provides 99% reliability of the proposed FED under the limited availability of resources. To validate the superiority of the HPSGWO, a comparative study is explained.

关键词： Reliability-redundancy allocation problem Fire extinguisher drone Optimization mixed-integer nonlinear programming Hybrid PSO-GWO

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Global optimization of multiphase flow networks using spline surrogate models

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COMPUTERS & CHEMICAL ENGINEERING 2016年 84卷 237-254页

作者： Grimstad, Bjarne Foss, Bjarne Heddle, Richard Woodman, Malcolm Norwegian Univ Sci & Technol Dept Engn Cybernet NO-7491 Trondheim Norway BP Explorat Operating Co Ltd Sunbury On Thames TW16 7BP Middx England

A general modelling framework for optimization of multiphase flow networks with discrete decision variables is presented. The framework is expressed with the graph and special attention is given to the convexity properties of the mathematical programming formulation that follows. nonlinear pressure and temperature relations are modelled using multivariate splines, resulting in a mixed-integer nonlinear programming (MINLP) formulation with spline constraints. A global solution method is devised by combining the framework with a spline-compatible MINLP solver, recently presented in the literature. The solver is able to globally solve the nonconvex optimization problems. The new solution method is benchmarked with several local optimization methods on a set of three realistic subsea production optimization cases provided by the oil company BP. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： nonlinear flow networks Petroleum production optimization mixed-integer nonlinear programming Branch-and-bound Splines

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Multi-criteria optimisation approach to increase the delivered power in radial distribution networks

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第16期9卷 2565-2574页

作者： Canizes, Bruno Soares, Joao Vale, Zita Lobo, Cristina Polytech Porto ISEP IPP GECAD Knowledge Engn & Decis Support Res Ctr P-4200072 Oporto Portugal

This study proposes a new methodology to increase the power delivered to any load point in a radial distribution network, through the identification of new investments in order to improve the repair time. This research work is innovative and consists in proposing a full optimisation model based on mixed-integer non-linear programming considering the Pareto front technique. The goal is to achieve a reduction in repair times of the distribution networks components, while minimising the costs of that reduction as well as non-supplied energy costs. The optimisation model considers the distribution network technical constraints, the substation transformer taps, and it is able to choose the capacitor banks size. A case study based on a 33-bus distribution network is presented in order to illustrate in detail the application of the proposed methodology.

关键词： distribution networks investment integer programming nonlinear programming Pareto optimisation cost reduction substations transformers power capacitors multicriteria optimisation approach power delivery radial distribution network investment optimisation model mixed-integer nonlinear programming Pareto front technique costs minimisation nonsupplied energy cost substation transformer tap capacitor bank 33-bus distribution network

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An income over feed cost nutritional grouping strategy

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JOURNAL OF DAIRY SCIENCE 2019年第5期102卷 4682-4693页

作者： Wu, Y. Liang, D. Shaver, R. D. Cabrera, V. E. Univ Wisconsin Madison Dept Dairy Sci Madison WI 53705 USA

This study introduces a new nutritional grouping method, OptiGroup, which maximizes milk income over feed cost (IOFC) using a mixed-integer nonlinear programming optimization algorithm. Analyses compared the OptiGroup with the cluster method, the current state-of-the-art nutritional grouping technique. Analyses were performed using cow-level data from 7 Wisconsin dairy farms. Consistently, the OptiGroup and the cluster were constrained to group cows simultaneously into 2 (low and high nutrient requirements) and 3 (low, medium, and high nutrient requirements) same-size groups. Each diet satisfied the net energy (NEL) and crude protein (CP) requirements of approximately 83% of the cows in each group by using lead factors based on nutrient density. A control treatment (1-group scenario) was used as a baseline for comparisons. The IOFC, dietary nutrient densities (NEL and CP), and dry matter intake with both methods were computed and compared. The percentage of cows grouped differently and the percentages of primiparous cows and late-lactation (> 200 d in milk) cows in each group were also analyzed. Results were as follows: (1) average extra IOFC of $8/cow per yr (2-group) and $12/cow per yr (3-group) by switching from cluster to OptiGroup method;(2) difference between dietary nutrient densities of the groups were reduced under OptiGroup method compared with cluster (i.e., NEL differences in 2 groups were 0.20 Mcal/kg for the cluster vs. 0.11 Mcal/kg for OptiGroup);(3) dry matter intake decreased with increasing group numbers within a grouping method, and decreased from cluster to OptiGroup method with constant group numbers;(4) percentage of primiparous cows was greater in the low group of cluster and in the high group of OptiGroup;and (5) proportion of late-lactation cows tended to be greater in the low group in both grouping strategies. Results indicated that the OptiGroup performed economically better than the cluster because of nutrient savings, even with high feed

关键词： cluster diet formulation precision feeding mixed-integer nonlinear programming

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