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multi-objective decision-making framework for an electricity retailer in energy markets using lexicographic optimization and augmented epsilon-constraint

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2015年第12期25卷 3660-3680页

作者： Nezhad, Ali Esmaeel Ahmadi, Abdollah Javadi, Mohammad Sadegh Janghorbani, Mohammadreza Islamic Azad Univ Marvdasht Branch Dept Elect Engn Marvdasht Iran Univ New S Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Islamic Azad Univ Shiraz Branch Dept Elect Engn Shiraz Iran Islamic Azad Univ Cent Tehran Branch Young Researchers & Elite Club Tehran Iran

The objective of the retailer in medium-term planning is managing the portfolio of contracts from different sources as well as determining the optimal selling price offered to its customers. When supplying the electricity sold to the costumers, two main challenges are faced by retailers. The first problem occurs during the electricity procurement procedure. In this stage, the retailer must deal with the uncertainty due to the pool price that propels the retailers to move towards agreeing to forward contracts signed at higher average prices. Besides, when the retailer decides on selling the electricity, another problem is to face the uncertainty caused by the demand while taking into consideration the possibility of reducing its clients in the case of high selling price. In this regard, this paper proposes a stochastic multi-objective framework for the retailer with profit maximization and risk minimization as two objective functions. The risk, due to the market price uncertainty, is modeled, employing the expected downside risk. The problem is formulated as mixed-integer programming while the stochastic optimization problem is characterized using the roulette wheel mechanism and lattice Monte Carlo simulation. Furthermore, lexicographic optimization and augmented epsilon-constraint method are used to solve the proposed multi-objective problem, and the best compromise solution is determined employing a fuzzy satisfying method. The presented model has been implemented using a realistic case study to verify the effectiveness of the method used in this paper. Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： stochastic optimization downside risk retailer multi-objective programming

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Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2015年 52卷 142-155页

作者： Mavrotas, George Figueira, Jose Rui Siskos, Eleftherios Natl Tech Univ Athens Sch Chem Engn Lab Ind & Energy Econ Athens 15780 Greece Univ Lisbon Inst Super Tecn CEG IST P-1049001 Lisbon Portugal Natl Tech Univ Athens Sch Elect & Comp Engn Decis Support Syst Lab Athens 15780 Greece

multi-objective combinatorial optimization (MOCO) problems, apart from being notoriously difficult and complex to solve in reasonable computational time, they also exhibit high levels of instability in their results in case of uncertainty, which often deviate far from optimality. In this work we propose an integrated methodology to measure and analyze the robustness of MOCO problems, and more specifically multi-objective integer programming ones, given the imperfect knowledge of their parameters. We propose measures to assess the robustness of each specific Pareto optimal solution (POS), as well as the robustness of the entire Pareto set (PS) as a whole. The approach builds upon a synergy of Monte Carlo simulation and multi-objective optimization, using the augmented epsilon-constraint method to generate the exact PS for the MOCO problems under examination. The usability of the proposed framework is justified through the identification of the most robust areas of the Pareto front, and the characterization of every POS with a robustness index. This index indicates a degree of certainty that a specific POS sustains its efficiency. The proposed methodology communicates in an illustrative way the robustness information to managers/decision makers and provides them with an additional supplement/tool to guide and support their final decision. Numerical examples focusing on a multi-objective knapsack problem and an application to academic capital budgeting problem for project selection, are provided to verify the efficacy and added value of the methodology. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Robustness analysis multi-criteria Combinatorial optimization multi-objective programming Monte Carlo simulation Visualization

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multi-objective meta-heuristics: An overview of the current state-of-the-art

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2002年第1期137卷 1-9页

作者： Jones, DF Mirrazavi, SK Tamiz, M Univ Portsmouth Sch Comp Sci & Math Portsmouth PO1 2EG Hants England

This paper gives an overview of meta-heuristics methods utilized within the paradigm of multi-objective programming. This is an area of research that has undergone substantial expansion and development in the past decade. A literature review for this period is presented and analyzed. Analysis of the types of multi-objective techniques and meta-heuristics is undertaken and reasons for their use hypothesized. The strengths and weaknesses of meta-heuristic methods as applied to multi-objective programmes are discussed. Finally, a summary is given together with suggestions for future research. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： multi-objective programming compromise programming goal programming meta-heuristics genetic algorithms simulated annealing tabu search

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Sustainable operations-oriented painting process optimisation in automobile maintenance service

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JOURNAL OF CLEANER PRODUCTION 2021年 324卷 129191-129191页

作者： Yang, Qin Meng, Xin Zhao, Huan Cao, Cejun Liu, Yang Huisingh, Donald Sichuan Normal Univ Sch Business Chengdu 610101 Peoples R China Chongqing Technol & Business Univ Sch Management Sci & Engn Chongqing 400067 Peoples R China Linkoping Univ Dept Management & Engn SE-58183 Linkoping Sweden Univ Vaasa Dept Prod Vaasa 65200 Finland Univ Tennessee Inst Secure & Sustainable Environm Knoxville TN USA

Building a more sustainable society is an urgent requirement for today's service-oriented manufacturing enterprises, such as automobile maintenance firms. In automobile maintenance service, traditional painting process scheduling scheme usually only considers the profits of enterprises, ignoring the requirements of customers or other stakeholders. To address this gap and achieve sustainable operations of enterprises in the long term, this paper concentrated on the vehicle scheduling of painting process problem with the concern of the demands of managers, workers, customers, governments and non-government environmental protection organisations. This problem was formulated as a nonlinear 0-1 integer programming model to minimise makespan (MP), total pollutant emissions (TPE) and total customers' perceived dissatisfaction (TCPDS). A genetic algorithm was designed to solve the model, and a practical case using data from both the information system and the survey was performed to test the performance of the proposed model and algorithm. Computational results revealed that the genetic algorithm performed well in terms of validity and stability. Pareto solutions demonstrated that optimising task sequences helped increase customers' perceived satisfaction while improving the makespan of vehicle painting, decreased paint waste, and reduced worker health and safety risks. Some of the increases in the percentage of well-timed customer service reservations were catalysed by the method that combined tiered pricing, related to delivery times, the automobile painting efficiency, which improved customers' perceived satisfaction. This paper also further guides managers to incorporate sustainable development into operations in service-oriented manufacturing enterprises.

关键词： Painting scheduling Service-oriented manufacturing Sustainable operations multi-objective programming Pareto solutions

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multi-objective multi-item fixed-charge solid transportation problem under twofold uncertainty

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NEURAL COMPUTING & APPLICATIONS 2019年第12期31卷 8593-8613页

作者： Roy, Sankar Kumar Midya, Sudipta Weber, Gerhard-Wilhelm Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Midnapore 721102 W Bengal India Middle East Tech Univ Inst Appl Math Ankara Turkey

In this paper, we investigate a multi-objective multi-item fixed-charge solid transportation problem (MOMIFCSTP) with fuzzy-rough variables as coefficients of the objective functions and of the constraints. The main focus of the paper is to analyze MOMIFCSTP under a fuzzy-rough environment for a transporting system. In practical situations, the parameters of a MOMIFCSTP are imprecise in nature, due to several uncontrollable factors. For these reasons, we introduce the fuzzy-rough variables in MOMIFCSTP to tackle vague data which are different from fuzziness and roughness. Fuzzy-rough expected-value operator is employed to convert fuzzy-rough MOMIFCSTP into deterministic MOMIFCSTP. Thereafter, we develop a methodology to solve the deterministic MOMIFCSTP by technique for order preference by similarity to ideal solution (TOPSIS). Three distinct approaches, namely extended TOPSIS, weighted goal programming (WGP) and fuzzy programming, are used to derive Pareto-optimal solution from the suggested model. A comparison is drawn among the optimal solutions which are derived from different approaches. It is observed from the extracted results that TOPSIS provides a better optimal solution than WGP and fuzzy programming. TOPSIS also overcomes some difficulties which arise in WGP. Finally, a real-world (industrial) problem is incorporated to show the applicability and feasibility of the proposed problem.

关键词： Fixed-charge solid transportation problem multi-objective programming Fuzzy-rough variable Twofold uncertainty TOPSIS Fuzzy programming Weighted goal programming

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Monte-Carlo simulation based multi-objective optimum allocation of renewable distributed generation using OpenCL

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ELECTRIC POWER SYSTEMS RESEARCH 2019年第May期170卷 81-91页

作者： Abdelaziz, Morad Moradzadeh, Majid Univ British Columbia Sch Engn Fac Appl Sci Kelowna BC Canada

Monte-Carlo simulation (MCS) is the most accurate technique for considering the stochastic nature of renewable energy resources in power system analysis and planning. However, due to its heavy computational burden, MCS is rarely utilized when solving the multi-objective renewable distributed generation (DG) allocation problem. In order to address this problem, this paper proposes a novel methodology to exploit the massively parallel architecture of graphics processing units (GPU) in a way that enables the use of MCS when solving the multi-objective renewable DG allocation problem. First, the renewable DG allocation problem is formulated as a multi-objective optimization problem to minimize the lines losses and the costs pertaining to installing renewable DG units in the distribution network. Then, a parallelized implementation of NSGA-II using OpenCL is described in details to solve the formulated multi-objective renewable DG planning problem. The feasibility and effectiveness of the proposed methodology are validated using the IEEE 32-bus test system and two real distribution test systems. The results show that the proposed parallelized implementation can enable the use of MCS for modelling the generation and demand uncertainties when solving the multi-objective renewable DG allocation problem using a metaheuristic approach.

关键词： Distribution system High performance computing (HPC) multi-objective programming Renewable energy resources Parallel algorithms Uncertainties modeling

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A fuzzy compromise approach for solving multi-objective stratified sampling design

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NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 10829-10840页

作者： Ghufran, Shazia Gupta, Srikant Ahmed, Aquil Aligarh Muslim Univ Dept Stat & Operat Res Aligarh Uttar Pradesh India Jaipuria Inst Management Jaipur Rajasthan India

In this paper, we established a framework for finding out the optimal allocation in the multivariate stratified sample using the fuzzy compromise method. The problem of multivariate stratified sample is formulated as an all integer nonlinear programming problem, and a solution is obtained by using the criterion of "Minimizing the sum of the squares of coefficient of variation for different characteristics." There is also a quantitative illustration being carried out to explain the statistical nature of the approaches and solved through the LINGO program. We have also studied different techniques for comparison.

关键词： Fuzzy compromise allocation multivariate stratified sampling multi-objective programming

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Computational study of the relationships between feasible and efficient sets and an approximation

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 1999年第2期14卷 241-260页

作者： Mateos, A Rios-Insua, S Prieto, L Madrid Tech Univ Dept Artificial Intelligence Madrid 28660 Spain

The computational difficulty of obtaining the efficient set in multi-objective programming, specially in nonlinear problems, suggest the need of considering an approximation approach to this problem. In this paper, we provide the computational results of the relationships between an approximation to the efficient set and the feasible and efficient sets. Random problem generation is considered for different sizes of the feasible set and we study the implications with respect to the number of objective functions and various kinds of objective functions. Computational experience with this approximation suggests that we obtain a substantial improvement when it increases the number of objective functions.

关键词： multi-objective programming efficient set vector value functions approximation

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An interactive satisficing approach for multi-objective optimization with uncertain parameters

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JOURNAL OF INTELLIGENT MANUFACTURING 2017年第3期28卷 535-547页

作者： Zhong, Shuya Chen, Yizeng Zhou, Jian Liu, Yuanyuan Shanghai Univ Sch Management Shanghai 200444 Peoples R China

Uncertain variables are used to describe the phenomenon where uncertainty appears in a complex system. For modeling the multi-objective decision-making problems with uncertain parameters, a class of uncertain optimization is suggested for the decision systems in Liu and Chen (2013), which is called the uncertain multi-objective programming. In order to solve the proposed uncertain multi-objective programming, an interactive uncertain satisficing approach involving the decision-maker's flexible demands is proposed in this paper. It makes an improvement in contrast to the noninteractive methods. Finally, a numerical example about the capital budget problem is given to illustrate the effectiveness of the proposed model and the relevant solving approach.

关键词： multi-objective optimization multi-objective programming Uncertain variable Uncertain programming Interactive satisficing approach

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Modelling and constructing membership function for uncertain portfolio parameters: A credibilistic framework

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 71卷 40-56页

作者： Jalota, Hemant Thakur, Manoj Mittal, Garima Indian Inst Technol Mandi Mandi 175001 Himachal Prades India Indian Inst Management Lucknow Lucknow 226013 Uttar Pradesh India

In this paper we attempt to automate the process of fitting the uncertain parameters of a multi-objective portfolio selection problem by generating L-R fuzzy numbers that belong to power reference function family. Such an approach is advantageous when the fuzzy parameters of the portfolio are best represented as general functional forms. We also propose four new portfolio selection models in a multi-criteria credibilistic framework. The key financial criteria considered are return, illiquidity (antagonistic to liquidity) and risk. In the absence of joint possibility distribution of these parameters, the return and illiquidity of the entire portfolio are considered as historical data set instead of return and illiquidity of the individual assets. In the process of fitting the most appropriate L-R fuzzy number, the vagueness within the information and deviation of the L-R fuzzy number from historical data is measured using entropy and cross-entropy respectively. These principles are embedded into the modelling process of proposed portfolio selection problems. One of the key contribution of this study is that we propose and design a sub-algorithm namely "Entropy-Cross Entropy (ECE) Algorithm" that is appended within an "MIBEXSM" genetic algorithm and is used to solve the proposed portfolio optimization problems. This proposed solution methodology results in an automated system that is intelligent enough to extract information required for fitting of L-R fuzzy number from the historical data and does not need any human intervention in terms of stating the parameters of the problem. We also conduct an empirical study to demonstrate the impact of the solution approach and applicability of the proposed models in practical applications of portfolio selection. For this purpose we collected historical data from National Stock Exchange (NSE), Mumbai, India. The data for a period of 2008-2013 is first used to train the models. Then the data sets of one-year period of 2013 - 201

关键词： Fuzzy portfolio selection Credibility measure L-R Fuzzy numbers multi-objective programming Credibilistic sharpe ratio Genetic algorithm

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