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Few-shot learning and deep predictive models for cost optimization and carbon emission reduction in energy-water management

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Journal of environmental management 2025年 389卷 126077页

作者： Wei Zhao Hao Chen Devrim Murat Yazan Hadi Taghavifar Yucheng Lyu Aldis Bulis IEBIS Department of High-tech Business and Entrepreneurship Faculty of Behavioural Management and Social Sciences University of Twente the Netherlands Faculty of Business Administration Turiba University Latvia. IEBIS Department of High-tech Business and Entrepreneurship Faculty of Behavioural Management and Social Sciences University of Twente the Netherlands. Electronic address: h.chen-3@utwente.nl. IEBIS Department of High-tech Business and Entrepreneurship Faculty of Behavioural Management and Social Sciences University of Twente the Netherlands. Department of Technology and Safety (ITS) UiT-The Arctic University of Norway Tromso Norway.

Effective management of energy and water resources is essential for mitigating environmental impacts and enhancing sustainability. This paper proposes a multiple-objective linear program tailored to accommodate energy-water applications in diverse climatic conditions in the Netherlands. It introduces an innovative approach that combines few-shot learning, designed to expand the datasets effectively, and sophisticated machine learning architectures such as Deep Autoregression to allow for more precise and reliable predictions. The method implements machine learning to enhance multi-objective operations research optimisation problems and significantly reduces the reliance on extensive datasets typically necessary for accurate predictive modelling. Experimental results show a notable increase in prediction accuracy, with the integrated approach surpassing traditional models by up to about 33 %. Additionally, we achieve an extension of the 8 solved scenarios to 800 similar scenarios so that the operational efficiency and sustainability of resource management are significantly improved, demonstrating the potential of machine learning technologies in this field. The strategic application of the proposed method compensates for the limitations associated with small datasets and ensures the scalability and effectiveness of the predictive models across various environmental scenarios.

关键词： Deep learning Few-shot learning multiple objective programming Resources optimisation Sustainability

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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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A cutting-plane method to non-smooth multiobjective optimization problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第3期275卷 822-829页

作者： Vieira, D. A. G. Lisboa, A. C. ENACOM Belo Horizonte MG Brazil CEFET Grad Program Math Modeling Belo Horizonte MG Brazil Gaia Solut Demand Belo Horizonte Technol Pk Belo Horizonte MG Brazil

The cutting-plane optimization methods rely on the idea that any subgradient of the objective function or the active/violated constraints defines a halfspace to be excluded from a set that contains an optimal solution: the localizing set. This algorithm converges towards a global minimum of any pseudoconvex subdifferentiable function. A naive extension for multiobjective optimization would be using simultaneously some subgradients of all objective functions for a given feasible point. However, as demonstrated in this paper, this approach can lead to a convergence towards non-optimal points. This paper introduces an optimization strategy for cutting-plane methods to cope with multiobjective problems without any scalarization procedure. The proposed strategy guarantees that its optimal solution is a Pareto Optimal solution of the original problem, which is also no worse than the starting point, and that any Pareto Optimal solution can be sampled. Moreover, the auxiliary problem is infeasible only if the original problem is also infeasible. The new strategy inherits the original theoretical guarantees of cutting planes methods and it can be applied to build other strategies. (C) 2019 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Cutting-plane method Pseudoconvex functions Subdifferencial Nonsmooth optimization

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Extension of Zoutendijk method for solving constrained multiobjective optimization problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期273卷 44-57页

作者： Morovati, Vahid Pourkarimi, Latif Amirkabir Univ Technol Fac Math & Comp Sci Dept Appl Math Tehran Iran Razi Univ Fac Sci Dept Math Kermanshah Iran

This paper extends the use of Zoutendijk method for constrained multiobjective optimization problems. This extension is a nonparametric direction-based algorithm. More precisely, considering all objective functions and binding constraints, this algorithm proposes a convex quadratic subproblem for generating a convenient improving feasible direction. Then, by using some elementary computation, the step length corresponding to the current direction is obtained. Some useful theoretical results corresponding to the proposed method are demonstrated. Using some of these theoretical results and under some mild conditions, the convergence of the proposed method is proved. The Zoutendijk multiobjective optimization (ZMO) method is not a population-based method. However, in order to find an approximation of the nondominated frontier, we need to have an appropriate population of initial feasible solutions. To achieve this aim, in this paper a cutting plane-like procedure which can generate an appropriate population of feasible solutions over the feasible set is proposed. Finally, in order to show its superiority, the proposed method is implemented for some well-known test problems. By employing some performance assessment criteria, the obtained numerical results are compared with the NSGA II method for all test problems. To have a more convenient comparison, the results are depicted in some performance profiles. Moreover, the obtained nondominated frontiers of these methods are compared for some test problems. The numerical results confirm the high performance of the ZMO method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Zoutendijk's method Nonparametric methods Efficient solution Nondominated point

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Interactive Nonconvex Pareto Navigator for multiobjective optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期275卷 238-251页

作者： Hartikainen, Markus Miettinen, Kaisa Klamroth, Kathrin Univ Jyvaskyla Fac Informat Technol POB 35 Agora FI-40014 Jyvaskyla Finland Univ Wuppertal Sch Math & Nat Sci Gaussstr 20 D-42119 Wuppertal Germany

We introduce a new interactive multiobjective optimization method operating in the objective space called Nonconvex Pareto Navigator. It extends the Pareto Navigator method for nonconvex problems. An approximation of the Pareto optimal front in the objective space is first generated with the PAINT method using a relatively small set of Pareto optimal outcomes that is assumed to be given or computed prior to the interaction with the decision maker. The decision maker can then navigate on the approximation and direct the search for interesting regions in the objective space. In this way, the decision maker can conveniently learn about the interdependencies between the conflicting objectives and possibly adjust one's preferences. To facilitate the navigation, we introduce special cones that enable extrapolation beyond the given Pareto optimal outcomes. Besides handling nonconvexity, the new method contains new options for directing the navigation that have been inspired by the classification-based interactive NIMBUS method. The Nonconvex Pareto Navigator method is especially well-suited for computationally expensive problems, because the navigation on the approximation is computationally inexpensive. We demonstrate the method with an example. Besides proposing the new method, we characterize interactive navigation based methods in general and discuss desirable properties of navigation methods overall and in particular with respect to Nonconvex Pareto Navigator. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Interactive multiobjective optimization Navigation Nonconvex problems Pareto optimality

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Quality recovering of university timetables

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期276卷 422-435页

作者： Lindahl, Michael Stidsen, Thomas Sorensen, Matias Tech Univ Denmark Dept Management Engn Lyngby Denmark MaCom AS Copenhagen Denmark

At universities, the timetable plays a large role in the daily life of students and staff, showing when and where lectures are given. But whenever a schedule is executed in a dynamic environment, disruptions will occur. It is then desirable to find a new timetable similar to the old one, so only a few people will be affected. This leads to a minimum perturbation problem, where the goal is to find a feasible timetable by changing as few assignments as possible. This solution will, however, often lead to timetables of low quality as it can have many undesired features that will cause much inconvenience for effected parties. In this paper we show that minimum perturbation solutions often have low quality and how using additional perturbations results in timetables with significantly higher quality while still keeping the number of perturbations low, so the solutions can be practically implemented. We formulate a bi-objective model and propose a method to solve it by using mixed integer programming. We test the method on standard instances of the Curriculum-based Course Timetabling Problem with four different types of disruptions. The use of bi-objective optimization enables us to generate multiple solutions whereby we provide the decision makers with multiple choices for handling the disruption. This allows the decision makers to determine the best trade-off between the number of perturbations and the quality, ultimately leading to better timetables for students and staff when disruptions occur. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Timetabling Disruptions multiple objective programming Minimum perturbation Integer programming

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Min-ordering and max-ordering scalarization methods for multi-objective robust optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期275卷 446-459页

作者： Schmidt, M. Schoebel, Anita Thom, Lisa Erasmus Univ Rotterdam Sch Management Dept Technol & Operat Management POB 1738 NL-3000 DR Rotterdam Netherlands Univ Gottingen Inst Numer & Angew Math Lotzestr 16-18 D-37083 Gottingen Germany

Several robustness concepts for multi-objective uncertain optimization have been developed during the last years, but not many solution methods. In this paper we introduce two methods to find min-max robust efficient solutions based on scalarizations: the min-ordering and the max-ordering method. We show that all point-based min-max robust weakly efficient solutions can be found with the max-ordering method and that the min-ordering method finds set-based min-max robust weakly efficient solutions, some of which cannot be found with formerly developed scalarization based methods. We then show how the scalarized problems may be approached for multi-objective uncertain combinatorial optimization problems with special uncertainty sets. We develop compact mixed-integer linear programming formulations for multi-objective extensions of bounded uncertainty (also known as budgeted or Gamma-uncertainty). For interval uncertainty, we show that the resulting problems reduce to well-known single-objective problems. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： multiple objective programming Robust optimization Multi-objective robust optimization Combinatorial optimization Scalarization

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On highly robust efficient solutions to uncertain multiobjective linear programs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期273卷 20-30页

作者： Dranichak, Garrett M. Wiecek, Margaret M. Clemson Univ Dept Math Sci Clemson SC 29634 USA

Decision making in the presence of uncertainty and multiple conflicting objectives is a real-life issue in many areas of human activity. To address this type of problem, we study highly robust (weakly) efficient solutions to uncertain multiobjective linear programs (UMOLPs) with objective-wise uncertainty in the objective function coefficients. We develop properties of the highly robust efficient set, characterize highly robust (weakly) efficient solutions using the cone of improving directions associated with the UMOLP, derive several upper and lower bound sets on the highly robust (weakly) efficient set, and present a robust counterpart for a class of UMOLPs. As various results rely on the acuteness of the cone of improving directions, we also propose methods to verify this property. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Robust multiobjective optimization objective-wise uncertainty Polar cones Acute cones

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A multiobjective hub-airport location problem for an airline network design

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期277卷 412-425页

作者： Soylu, Banu Katip, Hatice Erciyes Univ Dept Ind Engn TR-38039 Kayseri Turkey

This paper presents a biobjective multiple allocation p-hub median problem, discusses the properties of the Pareto frontier and proposes exact and heuristic algorithms for finding the Pareto frontier. Our motivation emanates from airline networks and their new hub investment strategies. The first objective minimizes the total transportation cost of the network, while the second one minimizes 2-stop journeys in order to improve customer satisfaction, which is negatively affected by the multiple-transit routes of airlines. Although using hubs reduces operating costs in networks, a cost-effective hub network may not imply minimum individual travel times for passengers, or happy passengers. It is well-known that airline customers prefer flights with fewer stops. However, reducing 2-stop routes increases the number of arcs, non-stop and 1-stop routes, and thus the total cost in the network. We analyzed the tradeoff between these objective functions. We performed experiments on well-Known data sets from the literature. We were able to find the Pareto frontier exactly for small/medium size instances. A variable neighborhood search (VNS) heuristic is presented to approximate the Pareto frontier of large size instances. We also performed an application on the current Turkish aeronautics network. The results are presented and discussed. (C) 2019 Elsevier B.V. All rights reserved.

关键词： multiple objective programming p-hub median problem Mixed-integer linear programming Multiobjective VNS Hub location

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Dominance for multi-objective robust optimization concepts

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期273卷 430-440页

作者： Botte, Marco Schoebel, Anita Georg August Univ Gottingen Lotzestr 16-18 D-37083 Gottingen Germany

In robust optimization, the parameters of an optimization problem are not deterministic but uncertain. Their values depend on the scenarios which may occur. Single-objective robust optimization has been studied extensively. Since 2012, researchers have been looking at robustness concepts for multi-objective optimization problems as well. In another line of research, single-objective uncertain optimization problems are transformed to deterministic multi-objective problems by treating every scenario as an objective function. In this paper we combine these two points of view. We treat every scenario as an objective function also in uncertain multi-objective optimization, and we define a corresponding concept of dominance which we call multi scenario efficiency. We sketch this idea for finite uncertainty sets and extend it to the general case of infinite uncertainty sets. We then investigate the relation between this dominance and the concepts of highly, locally highly, flimsily, and different versions of minmax robust efficiency. For all these concepts we prove that every strictly robust efficient solution is multi-scenario efficient. On the other hand, under a compactness condition, the set of multi-scenario efficient solutions contains a robust efficient solution for all these concepts which generalizes the Pareto robustly optimal (PRO) solutions from single-objective optimization to Pareto robust efficient (PRE) solutions in the multi-objective case. We furthermore present two results on reducing an infinite uncertainty set to a finite one which are a basis for computing multi scenario efficient solutions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Robustness and sensitivity analysis Pareto robust efficiency

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