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Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期308卷 864-883页

作者： Irawan, Chandra Ade Jones, Dylan Hofman, Peter S. Zhang, Lina Univ Nottingham Ningbo Nottingham Univ Business Sch China Ningbo Peoples R China Nottingham Ningbo China Beacons Excellence Res & I Ningbo Peoples R China Univ Portsmouth Ctr Operat Res & Logist Sch Math & Phys Portsmouth England James Cook Univ Singapore Singapore Univ Exeter Univ Exeter Business Sch Ctr Simulat Analyt & Modelling Exeter England

This paper proposes a combination of two optimization models for simultaneously determining strategic energy planning at both national and regional levels. The first model deals with a single-period energy mix where the electricity production configuration at a future date (e.g., 2050), based on the available generation sources, is optimally obtained. An optimization model, based on a non-linear goal programming method, is designed to ensure a mixed balance between national and regional goals. The desired energy mix configuration, which is the solution obtained by solving the first model, is then fed into the second model as the main data input. In the second model, a multiple-period generation expansion plan is designed which optimizes the energy transition over the time horizon from the present until the future planning date (2050). The model considers uncertain parameters, including the regional energy demand, fuel cost, and national peak load. A two-stage stochastic programming model is developed where the sample average approximation approach is used as a method of solution. The practical use of the proposed models has been assessed through application to the electricity generation system in China.(c) 2022 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Energy planning Goal programming Two -stage stochastic programming

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Introducing multiobjective complex systems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第2期280卷 581-596页

作者： Dietz, Tobias Klamroth, Kathrin Kraus, Konstantin Ruzika, Stefan Schaefer, Luca E. Schulze, Britta Stiglmayr, Michael Wiecek, Margaret M. Tech Univ Kaiserslautern Dept Math Kaiserslautern Germany Univ Wuppertal Sch Math & Nat Sci Wuppertal Germany Clemson Univ Dept Math Sci Clemson SC USA

This article focuses on the optimization of a complex system which is composed of several subsystems. On the one hand, these subsystems are subject to multiple objectives, local constraints as well as local variables, and they are associated with an own, subsystem-dependent decision maker. On the other hand, these subsystems are interconnected to each other by global variables or linking constraints. Due to these interdependencies, it is in general not possible to simply optimize each subsystem individually to improve the performance of the overall system. This article introduces a formal graph-based representation of such complex systems and generalizes the classical notions of feasibility and optimality to match this complex situation. Moreover, several algorithmic approaches are suggested and analyzed. (C) 2019 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Complex systems Linking Decomposition

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Warm-starting lower bound set computations for branch-and-bound algorithms for multi objective integer linear programs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第3期302卷 909-924页

作者： Forget, Nicolas Gadegaard, Sune Lauth Relund, Lars Aarhus Univ Sch Business & Social Sci Dept Econ & Business Econ Fuglesangs Alle 4 DK-8210 Aarhus V Denmark

In this paper we propose a generic branch-and-bound algorithm for solving multi-objective integer linear programming problems. In the recent literature, competitive frameworks has been proposed for bi-objective 0-1 problems, and many of these frameworks rely on the use of the linear relaxation to obtain lower bound sets. When increasing the number of objective functions, however, the polyhedral structure of the linear relaxation becomes more complex, and consequently requires more computational effort to obtain. In this paper we overcome this obstacle by speeding up the computations. To do so, in each branching node we use information available from its father node to warm-start a Bensons-like algorithm. We show that the proposed algorithm significantly reduces the CPU time of the framework on several different problem classes with three, four and five objective functions. Moreover, we point out difficulties that arise when non-binary integer variables are introduced in the models, and test our algorithm on problem that contains non-binary integer variables too. (C) 2022 The Author(s). Published by Elsevier B.V.

关键词： multiple objective programming Branch and bound Combinatorial optimization Linear relaxation Warm-starting

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A note on some of DEA models and finding efficiency and complete ranking using common set of weights

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APPLIED MATHEMATICS AND COMPUTATION 2005年第2期166卷 265-281页

作者： Jahanshahloo, GR Memariani, A Lotfi, FH Rezai, HZ Islamic Azad Univ Sci & Res Branch Dept Math Tehran Iran Bu Ali Sima Univ Dept Ind Engn Hamadan Iran Islamic Azad Univ Sci Branch Dept Math Mashhad Iran Islamic Azad Univ Res Branch Dept Math Mashhad Iran

Efficiency evaluation in Data Envelopment Analysis (DEA) depends on different factors. The most important factors arc the values of input and output. In this paper, we present an alternative proof that, if one component of output or input vectors of a DMU dominates the corresponding component of other DMUs whatever the value of other components of this DMU may be, then that DMU is efficient in some of the DEA models. An important outcome of such an analysis is a set of virtual multipliers or weights accorded to each factor taken into account. These sets of weights are, typically, different for each of the participating DMUs. In this paper, by means of solving only one problem, we can determine common set of weights (CSW) for all DMUs and their efficiencies. Finally, a method for ranking DMUs, is presented. In this method by solving only two problems, efficient DMUs are ranked. (c) 2004 Elsevier Inc. All rights reserved.

关键词： multiple objective programming Data envelopment analysis efficiency ranking common set of weights

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Handling of constraints in multiobjective blackbox optimization

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2024年第1期89卷 69-113页

作者： Bigeon, Jean Le Digabel, Sebastien Salomon, Ludovic Nantes Univ Ecole Cent Nantes CNRS LS2NUMR 6004 F-44000 Nantes France Polytech Montreal Dept Math & Genie Ind CP 6079 Montreal PQ H3C 3A7 Canada Grp Res Decis Anal GERAD Montreal PQ Canada

This work proposes the integration of two new constraint-handling approaches into the blackbox constrained multiobjective optimization algorithm DMulti-MADS, an extension of the Mesh Adaptive Direct Search (MADS) algorithm for single-objective constrained optimization. The constraints are aggregated into a single constraint violation function which is used either in a two-phase approach, where the search for a feasible point is prioritized if not available before improving the current solution set, or in a progressive barrier approach, where any trial point whose constraint violation function values are above a threshold are rejected. This threshold is progressively decreased along the iterations. As in the single-objective case, it is proved that these two variants generate feasible and/or infeasible sequences which converge either in the feasible case to a set of local Pareto optimal points or in the infeasible case to Clarke stationary points according to the constraint violation function. Computational experiments show that these two approaches are competitive with other state-of-the-art algorithms.

关键词： multiple objective programming Multiobjective optimization Derivative-free optimization Blackbox optimization Constrained optimization

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Lexicographic extension of the reference point method applied in radiation therapy treatment planning

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第1期263卷 247-257页

作者： van Haveren, Rens Breedveld, Sebastiaan Keijzer, Marleen Voet, Peter Heijmen, Ben Ogryczak, Wlodzimierz Erasmus MC Inst Canc Dept Radiat Oncol POB 2040 NL-3000 CA Rotterdam Netherlands Delft Univ Technol Delft Inst Appl Math POB 5031 NL-2600 GA Delft Netherlands Elekta Instrument AB Kungstensgatan 18Box 7593 SE-10393 Stockholm Sweden Warsaw Univ Technol Inst Control & Computat Engn PL-00665 Warsaw Poland

. In radiation therapy treatment planning, generating a treatment plan is a multi-objective optimisation problem. The decision-making strategy is uniform for each group of cancer patients, e.g. prostate cancer, and can thus be automated. Predefined priorities and aspiration levels are assigned to each objective, and the strategy is to attain these levels in order of priority. Therefore, a straightforward lexicographic approach is sequential epsilon-constraint programming where objectives are sequentially optimised and constrained according to predefined rules, mimicking human decision-making. The clinically applied 2-phase epsilon-constraint (2p epsilon c) method captures this approach and generates clinically acceptable treatment plans. However, the number of optimisation problems to be solved for the 2p epsilon c method, and hence the computation time, scales linearly with the number of objectives. To improve the daily planning workload and to further enhance radiation therapy, it is extremely important to reduce this time. Therefore, we developed the lexicographic reference point method (LRPM), a lexicographic extension of the reference point method, for generating a treatment plan by solving a single optimisation problem. The LRPM processes multiple a priori defined reference points into modified partial achievement functions. In addition, a priori bounds on a subset of the partial trade-offs can be imposed using a weighted sum component. The LRPM was validated for 30 randomly selected prostate cancer patients. While the treatment plans generated using the LRPM were of similar clinical quality to those generated using the 2p epsilon c method, the LRPM decreased the average computation time from 12.4 to 1.2 minutes, a speed-up factor of 10. (C) 2017 Elsevier B.V. All rights reserved.

关键词： OR in medicine multiple objective programming Radiation therapy treatment planning Lexicographic reference point method Partial trade-offs

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A multiple objective transportation problem approach to dynamic truck dispatching in surface mines

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期276卷 331-342页

作者： Afrapoli, Ali Moradi Tabesh, Mohammad Askari-Nasab, Hooman Univ Alberta Dept Civil & Environm Engn MOL Edmonton AB T6G 2W2 Canada

In surface mining operations, fleet management systems seek to make optimal decisions to handle material in two steps: path production optimization and real-time truck dispatching. This paper develops a multiple objective transportation model for real-time truck dispatching. The model addresses two major drawbacks of former models. The proposed model dispatches the trucks to destinations trying to simultaneously minimize shovel idle times, truck wait times, and deviations from the path production requirements established by the production optimization stage. To evaluate the performance of the proposed model, we developed a benchmark model based on the backbone of the most widely used fleet management system in the mining industry (Modular Mining DISPATCH). Afterward, we built a discrete event simulation model of the truck and shovel operation using an iron ore mine case study, implemented both of the dispatching models, and compared the results. The implementation of the models suggests that the multiple objective model developed in this paper is able to meet the production requirements of the operation using a fleet at 85% of the size of the deterministically calculated desired fleet. In addition, the model is able to meet the full capacity of the processing plants with a fleet of 30% less trucks than the desired fleet. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Simulation multiple objective programming Goal programming Truck dispatching Fleet management system

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Multiobjective optimization using an adaptive weighting scheme

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OPTIMIZATION METHODS & SOFTWARE 2016年第1期31卷 110-133页

作者： Deshpande, Shubhangi Watson, Layne T. Canfield, Robert A. Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24061 USA Virginia Polytech Inst & State Univ Dept Comp Math Blacksburg VA 24061 USA Virginia Polytech Inst & State Univ Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA

A new Pareto front approximation method is proposed for multiobjective optimization problems (MOPs) with bound constraints. The method employs a hybrid optimization approach using two derivative-free direct search techniques, and intends to solve black box simulation-based MOPs where the analytical form of the objectives is not known and/or the evaluation of the objective function(s) is very expensive. A new adaptive weighting scheme is proposed to convert a multiobjective optimization problem to a single objective optimization problem. Another contribution of this paper is the generalization of the star discrepancy-based performance measure for problems with more than two objectives. The method is evaluated using five test problems from the literature, and a realistic engineering problem. Results show that the method achieves an arbitrarily close approximation to the Pareto front with a good collection of well-distributed nondominated points for all six test problems.

关键词： multiple objective programming DIRECT MADS surrogates triangulation

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PriEsT: an interactive decision support tool to estimate priorities from pairwise comparison judgments

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2015年第2期22卷 217-235页

作者： Siraj, Sajid Mikhailov, Ludmil Keane, John A. Univ Portsmouth Portsmouth Business Sch Portsmouth PO1 3DE Hants England COMSATS Inst Informat Technol Dept Elect Engn Wah Cantonment Pakistan Univ Manchester Manchester Business Sch Manchester M15 6PB Lancs England Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England

Pairwise comparison (PC) is a well-established method to assist decision makers (DMs) in estimating their preferences. This paper considers the rationale, design, and evaluation of an open-source priority estimation tool, PriEsT, which has been developed to offer new features related to the PC method. PriEsT is able to assist DMs in interactively identifying and revising their judgments based on different consistency measures and graphical aids. When inconsistency cannot be improved due to practical limitations, PriEsT offers a wide range of Pareto-optimal solutions based on multiobjective optimization, unlike other tools that offer only a single solution. DMs have the flexibility to select any of these nondominated solutions according to their requirements. The features of PriEsT have been demonstrated and evaluated through its application to a real-world case study: the selection of the most appropriate telecom infrastructure for rural areas. This case study using PriEsT has highlighted the presence of intransitive judgments in the acquired data, and the correction of these judgments has led to a different ranking of the available alternatives.

关键词： decision support systems multiple criteria decision analysis software multiple objective programming analysis of algorithms

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On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期181卷 1630-1652页

作者： Kumar Shukla, Pradyumn Deb, Kalyanmoy Indian Inst Technol Dept Engn Mech Kanpur Genet Algorithms Lab Kanpur 208016 Uttar Pradesh India

In solving multi-objective optimization problems, evolutionary algorithms have been adequately applied to demonstrate that multiple and well-spread Pareto-optimal solutions can be found in a single simulation run. In this paper, we discuss and put together various different classical generating methods which are either quite well-known or are in oblivion due to publication in less accessible journals and some of which were even suggested before the inception of evolutionary methodologies. These generating methods specialize either in finding multiple Pareto-optimal solutions in a single simulation run or specialize in maintaining a good diversity by systematically solving a number of scalarizing problems. Most classical generating methodologies are classified into four groups mainly based on their working principles and one representative method from each group is chosen in the present study for a detailed discussion and for its performance comparison with a state-of-the-art evolutionary method. On visual comparisons of the efficient frontiers obtained for a number of two and three-objective test problems, the results bring out interesting insights about the strengths and weaknesses of these approaches. The results should motivate researchers to design hybrid multi-objective optimization algorithms which may be better than each of the individual methods. (C) 2006 Elsevier B.V. All rights reserved.

关键词： multiple objective programming generating methods performance analysis Pareto-optimal solutions evolutionary multiobjective optimization (EMO)

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