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An interactive approach for designing a robust disaster relief logistics network with perishable commodities

COMPUTERS & INDUSTRIAL ENGINEERING 2016年 94卷 201-215页

作者： Rezaei-Malek, Mohammad Tavakkoli-Moghaddam, Reza Zahiri, Behzad Bozorgi-Amiri, Ali Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Arts & Metiers ParisTech LCFC Metz France SUNY Buffalo Sch Management Dept Operat Management & Strategy Buffalo NY 14260 USA

Pre-positioning of relief commodities in appropriate locations and quantities at a pre-disaster phase assists a disaster relief logistics network in order to efficiently distribute the commodities to demand points;however, a disaster may occur after a long time, and generally perishable commodities (e.g., medical commodities and packed milk) have a fixed lifetime for use. Hence, this paper aims at developing a new integrated model in order to determine the optimum location-allocation and distribution plan, along with the best ordering policy for renewing the stocked perishable commodities at the pre-disaster phase. The uncertain nature of the problem leads to the utilization of a scenario-based robust stochastic approach. The proposed model simultaneously seeks to minimize: (1) the average of the weighted response times and (2) the total operational cost at the pre-disaster phase and the penalty costs of unmet demand and unused commodities at a post-disaster phase. The reservation level Tchebycheff procedure (RLTP) as an interactive approach is also applied to cope with the presented bi-objective model. The significance of the presented model and the efficiency of the RLTP method are then tested via a real case study in Iran. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Disaster relief logistics Location-allocation Ordering policy robust multi-objective optimization Interactive solution approach

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robust multi-objective maintenance planning of deteriorating bridges against uncertainty in performance model

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ADVANCES IN ENGINEERING SOFTWARE 2013年 65卷 32-42页

作者： Ok, Seung-Yong Lee, Sun-Young Park, Wonsuk Hankyong Natl Univ Dept Civil Safety & Environm Engn Anseong 456749 South Korea Machinery Met & Construct Exam Bur Convergence Technol Exam Div 1 Taejon 302701 South Korea Seoul Natl Univ Korea Bridge Design & Engn Res Ctr Seoul 151742 South Korea

This study proposes a new robust multi-objective maintenance planning approach of the deteriorating bridges against uncertainty in performance degradation model. The main focus is to guarantee the performance requirements of the bridge by the scheduled maintenance interventions even in the presence of uncertainty in time-dependent performance degradation model. The uncertainties are modeled as the perturbation of the system parameters. These are simulated by a sampling method, and incorporated into the GA-based multi-objective optimization framework which produces a set of optimal preventive maintenance scenarios. In order to focus the searching on the most preferable region, the performance models of the bridge components are all integrated into single overall performance measure by using the preference-based objective-space reduction method. Numerical example of a typical prestressed concrete girder bridge is provided to demonstrate the new robust maintenance scheduling approach. For comparison purpose, non-robust multi-objective maintenance planning without considering uncertainty of the bridge performance is also provided. It is verified that the proposed approach can produce successfully-performing maintenance scenarios under the perturbation of bridge condition grades while maintaining well-balanced maintenance strategy both in terms of bridge performance and maintenance cost. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Maintenance planning robust multi-objective optimization Deteriorating bridges Uncertainty Performance model Genetic algorithm

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Shifted robust multi-objective test problems

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STRUCTURAL AND multiDISCIPLINARY optimization 2015年第1期52卷 217-226页

作者： Mirjalili, Seyedali Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia Queensland Inst Business & Technol Brisbane Qld 4122 Australia

In 2013 Gaspar-Cunha et al. proposed a set of novel robust multi-objective benchmark functions to increase the difficulty of the current test problems and effectively mimic the characteristics of real search spaces. Despite the merits of the proposed benchmark problems, it is observed that the robust Pareto optimal fronts are located on the boundaries of the search space, which may result in the infeasibility of solutions obtained in case of perturbations along the negative side of the second parameter. This paper modifies the proposed test functions by Gaspar-Cunha et al. to mimic real problems better and allow the parameters to be fluctuated by any degree of perturbations. In fact, the robust fronts are shifted to the centre of the search space, so that any degree of uncertainties can be considered. The paper considers theoretical and experimental analysis of both set of test functions as well.

关键词： robust multi-objective optimization robustness Test functions multi-objective optimization

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A multi-objective formulation for the closed-loop plastic supply chain under uncertainty

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OPERATIONAL RESEARCH 2022年第5期22卷 4725-4768页

作者： Ebrahimi, Seyed Babak Bagheri, Ehsan KN Toosi Univ Technol Fac Ind Engn Financial Engn Grp Pardis St Molasadra AveVanak SqPOB 15875-4416 Tehran 193951999 Iran

Supply risk is one of the undeniable risks in a supply chain that can affect the supply chain process and lead to disruptions in customer satisfaction or distributor reliability. This research employs a closed-loop supply chain network for the plastic bottle industry, followed by formulating a multi-objective mathematical model by considering several assumptions. The model seeks to optimize total costs, supply risk, and customers' satisfaction (distributor's reliability). The revised multi-choice goal programming approach is also applied to solve the model and verify it through a case study. Moreover, the best-worst method as a robust multi-criteria decision-making tool is used to find the values of the parameters of supply risk. Afterward, the sensitivity analysis checks the proposed framework's robustness and shows its reaction under various conditions. Our results indicate the effectiveness of the proposed framework and offer insights that can be used to improve an organization's status.

关键词： robust multi-objective optimization Plastic industry Supply risk Reliability Revised multi-choice goal programming Best-worst method

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Interval robust multi-objective Evolutionary Algorithm

Interval Robust Multi-Objective Evolutionary Algorithm

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IEEE Congress on Evolutionary Computation

作者： Soares, G. L. Guimaraes, F. G. Maia, C. A. Vasconcelos, J. A. Jaulin, L. Pontificia Univ Catolica PUC Minas Belo Horizonte MG Brazil Univ Fed Ouro Preto Dept Comp Sci Ouro Preto Brazil Univ Fed Minas Gerais Dept Elect Engn Belo Horizonte MG Brazil ENSIETA Ecole Natl Super Ingn Brest France

ISBN: (纸本)9781424429585

Uncertainties are commonly present in optimization systems, and when they are considered in the design stage, the problem usually is called a robust optimization problem. robust optimization problems can be treated as noisy optimization problems, as worst case minimization problems, or by considering the mean and standard deviation values of the objective and constraint functions. The worst case scenario is preferred when the effects of the uncertainties on the nominal solution are critical to the application under consideration. Based on this worst case scenario, we developed the [I]RMOEA (Interval robust multi-objective Evolutionary Algorithm), a hybrid method that combines interval analysis techniques to deal with the uncertainties in a deterministic way and a multi-objective evolutionary algorithm. We introduce [I]RMOEA and illustrate it on three robust test functions based on the ZDT problems. The results show that [I]RMOEA is an adequate way of tackling robust optimization problems with evolutionary techniques taking advantage of the interval analysis framework.

关键词： genetic algorithms evolutionary algorithms robust multi-objective optimization robust Pareto front robust test functions interval analysis

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multi-objective DESIGN OPTIMISATION OF A 3D-RAIL STAMPING PROCESS USING A robust multi-objective OPTIMISATION PLATFORM (RMOP)

MULTI-OBJECTIVE DESIGN OPTIMISATION OF A 3D-RAIL STAMPING PR...

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12th International Conference on Computational Plasticity (COMPLAS)

作者： Lee, Dongseop Coma, Marti Espinoza, Hector Fruitos, Oscar Pons-Prats, Jordi UPC Int Ctr Numer Methods Engn CIMNE Castelldefels 08860 Spain Int Ctr Numer Methods Engn CIMNE Barcelona 08034 Spain

ISBN: (纸本)9788494153150

The paper investigates the multi-objective design optimisation of a stamping process to control the final shape and the final quality using advanced high strength steels. The design problem of the stamping process is formulated to minimise the difference between the desired shape and the final geometry obtained by numerical simulation accounting elastic springback. In addition, the final product quality is maximised by improving safety zones without wrinkling, thinning, or failure. Numerical results show that the proposed methodology improves the final product quality while reduces its springback.

关键词： Forming process robust multi-objective optimization Genetic Algorithms Computational Plasticity

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Community-level decentralized energy system planning under uncertainty: A comparison of mathematical models for strategy development

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APPLIED ENERGY 2021年 283卷 116304-116304页

作者： Prabatha, Tharindu Karunathilake, Hirushie Shotorbani, Amin Mohammadpour Sadiq, Rehan Hewage, Kasun Univ British Columbia Sch Engn Okanagan Campus EME42871137 Alumni Ave Kelowna BC V1V 1V7 Canada Univ Moratuwa Dept Mech Engn Katubedda 10400 Moratuwa Sri Lanka

Distributed energy systems renewable energy are one solution to the environmental and economic concerns of energy use. While energy planning and optimization have been conducted mainly as a mathematical exercise, practical approaches that incorporate the engineering realities and uncertainties are limited. Decision makers find challenges in community energy planning due to the lack of expertise, planning tools, and information. While a multitude of models and tools are currently available, there are no means of identifying the most appropriate or accurate methods, especially considering uncertainty. The main objective of this study is to compare and identify the strengths and limitations of various mathematical modelling techniques used in energy planning for grid connected renewable energy systems. As a case study demonstration, different multi-objective optimization techniques with and without uncertainty consideration (i.e. robust optimization, linear optimization, Taguchi Orthogonal Array method, and Monte Carlo simulation) were applied on a selected neighborhood in British Columbia. The optimization outcomes and the time and effort for evaluation were compared for the different methods. The findings indicate that robust optimization can be used to develop an uncertainty-based decision model. It significantly reduces evaluation time compared to the other methods. Although the presence of uncertainties can change the optimal configuration of a planned energy system, the assessment method itself does not significantly impact the outcomes. The findings of this study will enable the energy planners and researchers to compare different multi-objective optimization techniques, and to select the best for planning renewable energy projects, especially during the pre-project planning stage.

关键词： Community energy planning Renewable energy Uncertainty modelling Linear programming robust multi-objective optimization Monte Carlo simulation

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Sustainable microgrid design considering blockchain technology for real-time price-based demand response programs

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 125卷 106418-106418页

作者： Tsao, Yu-Chung Vo-Van Thanh Wu, Qiuwei Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan Tech Univ Denmark Dept Elect Engn Lyngby Denmark

In recent years, rapidly increasing electric power demand and organizational concerns regarding social and environmental issues have resulted in significant attention being given to microgrids. However, sustainable microgrids that simultaneously address economic benefits and environmental and social issues have not been broadly explored by researches. This study addresses the sustainable microgrid design problem by leveraging blockchain technology to provide the real time-based demand response programs. Three sustainable objectives (economy, environment, and society) are formulated by a multi-objective mixed integer-linear programming model. A robust fuzzy multi-objective optimization approach is proposed to determine the optimal number, location, and capacity of renewable distributed generation units as well as the equilibrium supply and dynamic pricing decisions under uncertain demand, capacity, and economic, environmental, and social parameters. The proposed model and solution approach are then applied to a case study in Vietnam. We find that blockchain technology-based sustainable microgrid can result in a 1.68% and 2.61% increase of profitability and consumer satisfaction, respectively, and a 0.97% reduction of environmental impacts.

关键词： Sustainability Microgrid Blockchain technology Demand response robust multi-objective optimization Fuzzy programming

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Interval robust multi-objective Algorithm

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NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS 2009年第12期71卷 E1818-E1825页

作者： Soares, G. L. Parreiras, R. O. Jaulin, L. Vasconcelos, J. A. Maia, C. A. Pontificia Univ Catolica Minas Gerais PUCMinas Belo Horizonte MG Brazil Univ Fed Minas Gerais Belo Horizonte MG Brazil ENSIETA Brest France

This paper introduces a method for solving multi-objective optimization problems in uncertain environment. When the uncertainty factors of the optimization problem can be included in to the mathematical model, through bounded intervals, [I]RMOA (Interval robust multi-objective Algorithm) can find an enclosure of the robust Pareto frontier. In this approach, the robust Pareto solutions are the ones that have the best performance when the worst case scenario, characterized by the uncertainty parameters, is considered. [I]RMOA has some positive aspects: it does not require the calculus of derivatives;it has only two input parameters;it is a reliable tool for solving different robust optimization problems, which can be nonlinear and discontinuous or have non convex Pareto frontier, for instance. The success of the method depends only on the quality of the objective inclusion functions and the precision parameters. Its disadvantage lies in the fact that it requires high computational effort, when high-dimensional problems are considered or when a very accurate enclosure is needed. Two analytical robust test functions are proposed to be treated by [I]RMOA and to validate the results provided by a stochastic multi-objective optimization method. The results are satisfactory. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： robust multi-objective optimization robust Pareto frontier Interval Analysis Genetic algorithm

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robust multi-objective thermal and electrical energy hub management integrating hybrid battery-compressed air energy storage systems and plug-in-electric-vehicle-based demand response

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JOURNAL OF ENERGY STORAGE 2021年 35卷

作者： Zeynali, Saeed Rostami, Naghi Ahmadian, Ali Elkamel, Ali Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Univ Bonab Dept Elect Engn Bonab Iran Univ Waterloo Coll Engn Waterloo ON Canada

A compressed air energy storage (CAES) can operate together with a battery energy storage system (BESS) to enhance the economic and environmental features of the energy hubs (EH). In this regard, this paper investigates their mutual cooperation in a multi-objective thermal and electrical residential EH optimization problem, which aims to diminish the total operational cost and emission. The proposed formulation also includes a PEV-based demand response program (DRP), renewable energy production units, solar heat collectors (SHE), thermal energy storage (TES), and hot water storage. Additionally, the CAES is operated as a combined heat and power unit in discharging and simple cycle modes. A linearized battery degradation cost model is integrated into the cost objective function, which ensures global optimization. Moreover, the inherent uncertainties of wind speed, solar irradiation, residential loads, electricity market price and PEVs' load demand are handled by the computationally effective robust mixed-integer linear programming (RMILP) method. The PEVs' uncertain load demand is obtained from their corresponding arrival-departure time, daily traveled miles, and vehicle type. The optimal Pareto front of the multi-objective problem is obtained by epsilon-constrained method for different robustness adjustments. Different deterministic and robust case studies are designed to evaluate the functionality of the proposed formulation.

关键词： Energy hub robust multi-objective optimization Compressed air energy storage Demand response program Battery degradation Plug-in electric vehicles

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