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Simulation-based model for the optimization of machining parameters in a metal-cutting operation

SIMULATION MODELLING PRACTICE AND THEORY 2018年 84卷 204-221页

作者： Chavez-Garcia, Hernan Castillo-Villar, Krystel K. Univ Texas San Antonio Dept Mech Engn Texas Sustainable Energy Res Inst San Antonio TX 78249 USA

To achieve a certain measurable performance in cutting machines, the machine parameters need to be optimized. Several constraints determine the possible values that these parameters can take. Although parameters are usually assumed to be deterministic, in practice, it is common to find variations on the characteristics of the products or the processes. Modeling machining parameters as stochastic factors provides a more realistic representation of cutting operations. Moreover, multiple operational objectives are of interest, in many real situations, these multiple objectives are conflicting. Consequently, the problem of setting the parameters becomes a trade-offsituation. This paper presents a novel Simulation-based multi-objective optimization (SimMOpt) solution procedure. The procedure is divided into two phases: (1) finding initial solutions and, (2) using a simulated annealing-based method for finding neighboring solutions. In the first phase, non-linear goal programming is used for finding high quality initial solutions. The second phase incorporates Pareto Archive Evolution Strategy (PAES) and hypotheses testing for searching near-optimal solutions for a set of stochastic parameters (i.e., cutting speed, feed rate, and depth of cut) in metal cutting operations. Three objectives are optimized (i.e., operation time, operation cost, and quality of the product). The results from implementing this procedure are analyzed and compared to a baseline methodology based on the multi-objective Simulated Annealing (MOSA) algorithm. The analysis demonstrates that the proposed method outperforms the Genetic Algorithm (GA), which was the benchmark algorithm, in terms of the solution quality of all the objectives. More importantly, the solutions from using the SimMOpt procedure outperform those obtained from using an enhanced MOSA-based approach (i.e., 4.71% improvement in the hypervolume approximation). (C) 2018 Elsevier B.V. All rights reserved.

关键词： stochastic multi-objective optimization Machining parameters Simulated annealing Manufacturing

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Optimal Planning of Integrated Energy Systems for Offshore Oil Extraction and Processing Platforms

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ENERGIES 2019年第4期12卷 756-756页

作者： Zhang, Anan Zhang, Hong Qadrdan, Meysam Yang, Wei Jin, Xiaolong Wu, Jianzhong Southwest Petr Univ Sch Elect & Informat Engn Chengdu 610500 Sichuan Peoples R China Cardiff Univ Sch Engn Cardiff CF24 3AA S Glam Wales

With the introduction of new technologies, such as waste heat recovery units (WHRU), associated gas utilization, the energy flow coupling relationship is further deepened within the energy system of the offshore oil and gas production platform. Besides, the energy system is closely linked with the oil and gas production system, and a closed-loop relationship between energy flow and material flow can be revealed. Uncertainties of energy supply and production process may lead to system-wide fluctuations, which threaten the stable operation of the platform. Therefore, an optimal planning model of integrated energy system for offshore oil and gas production platform is proposed in this paper. Firstly, a generalized energy and material flow model is proposed, three matrixes are defined based on laws of thermodynamics, including energy matrix, process matrix and feedback matrix. Secondly, the energy-material conversion relationship between the energy system and production system of a typical offshore oil and gas platform is quantitatively described, together with the coupling between the input and output of the two systems. Thirdly, considering the energy-material balance constraints and the uncertainties of production system, a multi-objective stochastic planning model for the offshore integrated energy system is established, which takes economics and environmental protection into consideration. A Monte Carlo simulation-based NSGA-II algorithm is proposed to solve the model. Finally, the validity and feasibility of the proposed methodology are demonstrated through an offshore oil and gas platform in Bohai, China. Compared with the traditional planning method, the total cost and CO2 emissions of the proposed method are reduced by 18.9% and 17.3%, respectively.

关键词： energy system production system uncertainty generalized energy and material flow model stochastic multi-objective optimization

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Simulation-based multi-objective model for supply chains with disruptions in transportation

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2017年第0期43卷 39-49页

作者： Chavez, Hernan Castillo-Villar, Krystel K. Herrera, Luis Bustos, Agustin Univ Texas San Antonio Dept Mech Engn San Antonio TX 78249 USA Tecnol Monterrey Dept Ind & Syst Engn Mexico City 01389 DF Mexico Inst Mexicano Transporte Integrated Transport Res Ctr Sanfandila 76700 Queretaro Mexico

Unpredictable disruptions (e.g., accidents, traffic conditions, among others) in supply chains (SCs) motivate the development of decision tools that allow designing resilient routing strategies. The transportation problem, for which a model is proposed in this paper, consists of minimizing the stochastic transportation time and the deterministic freight rate. This paper extends a stochastic multi-objective minimum cost flow (SMMCF) model by proposing a novel simulation-based multi-objective optimization (SimMOpt) solution procedure. A real case study, consisting of the road transportation of perishable agricultural products from Mexico to the United States, is presented and solved using the proposed SMMCF-Continuous/SimMOpt solution framework. In this case study, time variability is caused by the inspection of products at the U.S.-Mexico border ports of entry. The results demonstrate that this framework is effective and overcomes the limitations of the multi-objective stochastic minimum cost flow problem (which becomes intractable for large-scale instances). (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Minimum cost flow Simulated annealing Simulation optimization stochastic multi-objective optimization Resilient supply chains

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Integration of logistics outsourcing decisions in a green supply chain design: A stochastic multi-objective multi-period multi-product programming model

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2016年 182卷 165-184页

作者： Ameknassi, Lhoussaine Ait-Kadi, Daoud Rezg, Nidhal Univ Laval Dept Mech Engn CIRRELT 2325 Rue Univ Quebec City PQ G1V 0A6 Canada Lorraine Univ LGPM F-57045 Metz 01 France

This paper develops a programming model, which combines logistics outsourcing decisions with some strategic Supply Chains' planning issues, such as the Security of supplies, the customer Segmentation, and the Extended Producer Responsibility. The purpose is to minimize both the expected logistics cost and the Green House Gas (GHG) emissions of the Supply Chain (SC) network, in the context of business environment uncertainty. First, we define a general structure of the closed-loop SC. Second, we provide constructive models to roughly estimate the insourcing and outsourcing logistics costs, and their corresponding GHG emissions. Third, we establish a stochastic plan based on a scenarios approach to capture the uncertainty od demand, capacity of facilities, quantity and quality of returns of used products, and the transportation, warehousing, and reprocessing costs. Fourth, we suggest a programming model, and an algorithm based on the Epsilon-constraint method to solve it. The result is a set of optimal non-dominant green SC configurations, which provide the decision' makers with optimal levels of logistics outsourcing integration within a decarbonized Supply Chain, before any further low-carbon investment. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Supply Chain Integration Third-Party Logistics Logistics costs Green House Gas emissions stochastic multi-objective optimization

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stochastic optimization of hybrid renewable energy systems using sampling average method

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2015年 52卷 1668-1679页

作者： Sharafi, Masoud ElMekkawy, Tarek Y. Univ Manitoba Dept Mech Engn Winnipeg MB R3T 5V6 Canada Qatar Univ Dept Mech & Ind Engn Doha Qatar

The stochastic attribute of renewable energy sources and the variability of energy load is a preeminent barrier to design hybrid renewable energy systems. In this paper, a new methodology is advanced to incorporate the uncertainties associated with RE resources and load in sizing an HRES in the application of buildings with low to high renewable energy ratio (RER). Dynamic multi-objective particle swarm optimization (DMOPSO) algorithm, simulation module, and sampling average technique are used to approximate a Pareto front (PF) for an HRES design through a multi-objective optimization framework. The main aim of design is to simultaneously minimize total net present cost (NPC), maximize renewable energy ratio, and minimize fuel emission while satisfy a desirable level of loss of load probability (LLP). The existing randomness in wind speed, solar irradiation, ambient temperature, and energy load is considered using synthetically data generation and sampling average method. The performance of the model has been examined in a building located in Canada as the case study, in which RER of the building is increased by using renewable energy technologies. The generated PF by the stochastic approach is compared to a deterministic PF using well-known performance metrics. Finally, a sensitivity analysis is carried out where the economic characteristics of the model are varied. (c) 2015 Elsevier Ltd. All rights reserved.

关键词： stochastic multi-objective optimization Hybrid renewable energy system Near zero energy buildings

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stochastic Day-Ahead Generation Scheduling With Pumped-Storage Stations and Wind Power Integrated

Stochastic Day-Ahead Generation Scheduling With Pumped-Stora...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Zheng, J. H. Quan, X. Y. Jing, Z. X. Wu, Q. H. South China Univ Technol Sch Elect Engn Guangzhou 510641 Guangdong Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)9781509019700

With more and more uncertain wind power generation integrated in power systems, it is significant to enhance the resilience of generation scheduling to avoid imbalance charges. This paper proposes a stochastic day-ahead generation scheduling (SDAGS) with pumped-storage (PS) stations and wind power (WP) integrated in power systems to tackle the variability of wind power for the purpose of reliability and economy of system operation. Considering the uncertainties of load and wind power generation, Latin hypercube sampling with Cholesky decomposition (LIIS-CD) is utilized to generate several scenarios. multi-objective group search optimizer with adaptive covariance and Levy flights (MGSO-ACL) is applied to optimize the SDAGS over 24-hour period, aiming at reaching a compromise between the minimization of expectation and variance of total cost of the SDAGS. Furthermore, a decision making method based on evidential reasoning (ER) approach is utilized to determine a final optimal solution considering expected carbon dioxide emission and expected polluted gas emission. Simulation studies are conducted on two different power systems with PS stations and WP integrated to verify the efficiency of the SDAGS.

关键词： Generation scheduling wind power penetration pumped-storage stations stochastic multi-objective optimization decision making

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Coordination of combined heat and power-thermal-wind-photovoltaic units in economic load dispatch using chance-constrained and jointly distributed random variables methods

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ENERGY 2015年第C期79卷 50-67页

作者： Azizipanah-Abarghooee, Rasoul Niknam, Taher Bina, Mohammad Amin Zare, Mohsen Shiraz Univ Technol Dept Elect & Elect Engn Shiraz 71555313 Iran Iran Fars Shiraz Fars Elect Power Distributed Co Shiraz Iran

CHP (Combined heat and power) generation or cogeneration has been considered worldwide as the major alternative to traditional systems in terms of significant energy saving and environmental conservation. Furthermore, the wind power generators and photovoltaic units have vastly speared over the power systems due to their free inputs. However, there are many challenges for power system operators because of uncertain characteristics of renewable units and load demands. Therefore, a new multi-objective stochastic framework based on chance constrained programming is developed to handle combined heat and power economic load dispatch considering the stochastic characteristics of wind and photovoltaic power outputs, customer's electrical and heat load demands. The proposed technique makes use of a jointly distributed random variables method to calculate chance of meeting the electrical and heat load requirement using the target decision variables while maintaining the electrical energy cost below a scheduled value. The framework benefits from a new method named hybrid modified cuckoo search algorithm and differential evolution to extract the Pareto optimal surface for minimum cost and maximum probability of meeting the target cost and applies them as the objective functions. Applying to 6 and 40 unit test systems, the ability of the suggested framework is confirmed. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Combined heat and power economic dispatch Chance constrained programming Cuckoo search algorithm Jointly distributed random variables method stochastic multi-objective optimization Photovoltaic and wind power

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multi-objective optimal design of a solar absorption cooling and heating system under life-cycle uncertainties

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2015年 11卷 92-105页

作者： Xu, Donghao Qu, Ming Hang, Yin Zhao, Fu Purdue Univ W Lafayette IN 47907 USA Areva Solar Inc Palo Alto CA 94303 USA

SACH systems, as a type of renewable energy systems, meet building cooling and heating demand through solar energy and therefore can reduce energy consumption and, in turn, mitigate the environmental impacts associated with building operation. Many researchers have evaluated and optimized the design of SACH systems under deterministic schemes;however, deterministic approach is subject to uncertain parameters in the evaluation or objective functions such as energy prices in the future and the results subsequently may not hold true. To enhance the design robustness and validate the use of deterministic approaches, a stochastic multi-objective optimization (SMOO) model is developed that incorporates these uncertainties. In this paper, a SACH system is optimized with respects to life-cycle energy, economic, and environment performance. The uncertain parameters considered include the life span of SACH systems and the fluctuations of energy markets and financial conditions. A case study follows to investigate the relationship among uncertainty and different designs. The comparison between deterministic and stochastic optimization shows that deterministic optimization can be used by designers who are moderate or not risk-averse at all. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Solar absorption cooling and heating system stochastic multi-objective optimization Life-cycle uncertainty Design guideline

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REAL CODED GENETIC ALGORITHM FOR stochastic HYDROTHERMAL GENERATION SCHEDULING

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Journal of Systems Science and Systems Engineering 2011年第1期20卷 87-109页

作者： Jarnail S.DHILLON J.S.DHILLON D.P.KOTHARI G.Z.S.College of Engineering and Technology Sant Longowal Institute of Engineering and Technology Vindhya Institute of Technology and Science

The intent of this paper is to schedule short-term hydrothermal system probabilistically considering stochastic operating cost curves for thermal power generation units and uncertainties in load demand and reservoir water inflows. Therefore, the stochastic multi-objective hydrothermal generation scheduling problem is formulated with explicit recognition of uncertainties in the system production cost coefficients and system load, which are treated as random variable. Fuzzy methodology has been exploited for solving a decision making problem involving multiplicity of objectives and selection criterion for best compromised solution. A real-coded genetic algorithm with arithmetic-average-bound-blend crossover and wavelet mutation operator is applied to solve short-term variable-head hydrothermal scheduling problem. Initial feasible solution has been obtained by implementing the random heuristic search. The search is performed within the operating generation limits. Equality constraints that satisfy the demand during each time interval are considered by introducing a slack thermal generating unit for each time interval. Whereas the equality constraint which satisfies the consumption of available water to its full extent for the whole scheduling period is considered by introducing slack hydro generating unit for a particular time interval. Operating limit violation by slack hydro and slack thermal generating unit is taken care using exterior penalty method. The effectiveness of the proposed method is demonstrated on two sample systems.

关键词： stochastic multi-objective optimization real-coded genetic algorithm fuzzy set economicload dispatch

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Security Constrained stochastic multi-objective Optimal Power Dispatch

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2007年第1期8卷 1-20页

作者： Bath, Sarbjeet Kaur Dhillon, Jaspreet Singh Kothari, D. P. GZS Coll Engn & Technol Bathinda Punjab India St Longowal Inst Engn & Technol Sangrur Punjab India IIT New Delhi India

A stochastic multi-objective line security constrained problem is formulated to minimize non-commensurable objectives viz. operating cost, polluting gaseous emission and variance of active power generation and reactive power generation, with explicit recognition of statistical uncertainties in the thermal power generation cost coefficients, gaseous emission coefficients, power demands and hence power generations and bus voltages, which are considered random variables. Specific technique is put forth to convert the stochastic models into their respective deterministic equivalents. Fuzzy set theory has been exploited to evaluate the different objectives that are quantified by defining their membership functions. Security of transmission lines with respect to expected active power flow is considered in the form of fuzzy objective function. The solution set of such formulated problems is non-inferior due to contradictions among the objectives undertaken. The weighting method is used to simulate the trade-off relationship between the conflicting objectives in the non-inferior domain. Generally, the weights are either simulated or searched in the non-inferior domain. In the paper Evolutionary search technique is implemented to search the 'preferred' weightage pattern in the non-inferior domain, which corresponds to the 'best' compromised solution. Among the non-inferior solutions, the system operator selects the 'preferred' optimal operating point that provides maximum satisfaction level of the most under achieved objective in terms of membership function and is termed as fitness function. The validity of the proposed method has been demonstrated on an IEEE system comprising of 11-nodes, 17-lines and 5-generators.

关键词： decision maker (DM) evolutionary optimization fuzzy goals membership function stochastic multi-objective optimization weight pattern

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