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Domain-driven multiple-criteria decision-making for flight crew decision support tool

JOURNAL OF AIR TRANSPORT MANAGEMENT 2023年第1期112卷

作者： Montlaur, Adeline Delgado, Luis Prats, Xavier Tech Univ Catalonia BarcelonaTech Dept Phys Aerosp Engn Div Castelldefels Spain Univ Westminster Sch Architecture & Cities London England

During the flight, the crew might consider modifying their planned trajectory, taking into account currently available information, such as an updated weather forecast report or the already accrued amount of delay. This modified planned trajectory translates into changes on expected fuel and flying time, which will impact the airline's relevant performance indicators leading to a complex multiple-criteria decision-making problem. Pilot3, a project from the Clean Sky Joint Undertaking 2 under European Union's Horizon 2020 research and innovation programme, aims to develop an objective optimisation engine to assist the crew on this process. This article presents a domain-driven approach for the selection of the most suitable multiple-criteria decision making methods to be used for this optimisation framework. The most relevant performance indicators, based on airline's objectives and policies, are identified as: meeting on-time performance, leading to a binary value in a deterministic scenario;and total cost, which can be disaggregated into sub-cost components. The optimisation process consists of two phases: first, Pareto optimal solutions are generated with a multi-objective optimisation method (lexicographic ordering);second, alternative trajectories are filtered and ranked using a combination of multi-criteria decision analysis methods (analytic hierarchy process and VIKOR). A realistic example of use shows the applicability of the process and studies the sensibility of the optimisation framework.

关键词： Decision support systems multiple criteria analysis multiple objective programming OR in airlines Transportation

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A Mathematical programming Approach to Optimum Airspace Sectorisation Problem

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JOURNAL OF NAVIGATION 2020年第3期73卷 599-612页

作者： Oktal, Hakan Yaman, Kadriye Kasimbeyli, Refail Eskisehir Tech Univ Fac Aeronaut & Astronaut Eskisehir Turkey Eskisehir Tech Univ Fac Engn Eskisehir Turkey

The aim of this study is to provide a balanced distribution of air traffic controller workload (ATCW) across airspace sectors taking into account the complexity of airspace sectors and the factors affecting ATCW, both objective and perceived. Almost all the studies focusing on the airspace sectorisation problem use heuristic or metaheuristic algorithms in dynamic simulation environments instead of a mathematical modelling approach. The paper proposes a multi-objective mixed integer mathematical model for airspace sectorisation. The model is applied to the upper, en-route level of Turkish airspace. Geographical information systems (GIS) are used to advantage for airspace analysis. The multi-objective model developed in this paper is scalarised by using the conic scalarisation method. For solving the scalarised problem, the CPLEX and DICOPT solvers of GAMS software are implemented. Finally, the optimal sector boundaries of Turkish airspace are defined.

关键词： Air Transportation Airspace Sectorisation Air Traffic Controller Workload multiple objective programming Conic Scalarisation

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An exact approach for the Minimum-Cost Bounded-Error Calibration Tree problem

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ANNALS OF OPERATIONS RESEARCH 2020年第1期287卷 109-126页

作者： Carvalho, Iago A. Ribeiro, Marco A. Univ Fed Minas Gerais Dept Comp Sci Av Antonio Carlos 6627 BR-31270010 Belo Horizonte MG Brazil

The Minimum-Cost Bounded-Error Calibration Tree problem (MBCT) is a wireless network optimization problem that arises from the sensors' need of periodical calibration. The MBCT takes into account two objectives. The first is to minimize the communication distance between the network sensors, while the second is to reduce the maximum post-calibration error in the network. In this paper, we propose a mathematical formulation for the MBCT. Furthermore, we solve the problem using two different implementations of an Augmented epsilon that incorporate the proposed formulation. We also employed the Pareto-fronts obtained by aug epsilon-CM to evaluate the Node-depth Phylogenetic-based Non-dominated Sorting Artificial Immune System (NPE-NSAIS), the most recent heuristic in the literature for MBCT. Computational experiments showed that aug epsilon-CM can solve MBCT instances up to 50 nodes. Furthermore, a statistical test demonstrated that the running times of one of the aug epsilon-CM implementations was significantly smaller than those of the other. Finally, we show that NPE-NSAIS solutions are very close to the Pareto-fronts given by aug epsilon-CM, achieving good results on the evaluated metrics.

关键词： multiple objective programming Augmented epsilon-constraints Wireless sensors network Calibration error Spanning arborescence

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A projection multi-objective SVM method for multi-class classification

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 158卷

作者： Liu, Ling Martin-Barragan, Belen Prieto, Francisco J. Beijing Univ Technol Coll Stat & Data Sci Fac Sci 100 Pingleyuan Beijing 100124 Peoples R China Univ Edinburgh Business Sch 29 Buccleuch Pl Edinburgh EH8 9JS Midlothian Scotland Univ Carlos III Madrid Dept Stat C Madrid 126 Madrid 28903 Spain

Support Vector Machines (SVMs), originally proposed for classifications of two classes, have become a very popular technique in the machine learning field. For multi-class classifications, various single-objective models and multi-objective ones have been proposed. However,in most single-objective models, neither the different costs of different misclassifications nor the users' preferences were considered. This drawback has been taken into account in multi-objective models. In these models, large and hard second-order cone programs(SOCPs) were constructed ane weakly Pareto-optimal solutions were offered. In this paper, we propose a Projected Multi-objective SVM (PM), which is a multi-objective technique that works in a higher dimensional space than the object space. For PM, we can characterize the associated Pareto-optimal solutions. Additionally, it significantly alleviates the computational bottlenecks for classifications with large numbers of classes. From our experimental results, we can see PM outperforms the single-objective multi-class SVMs (based on an all-together method, one-against-all method and one-against-one method) and other multi-objective SVMs. Compared to the single-objective multi-class SVMs, PM provides a wider set of options designed for different misclassifications, without sacrificing training time. Compared to other multi-objective methods, PM promises the out-of-sample quality of the approximation of the Pareto frontier, with a considerable reduction of the computational burden.

关键词： multiple objective programming Support vector machine Multi-class multi-objective SVM Pareto-optimal solution

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A Fuzzy MOP Based Competence Set Expansion Method for Technology Roadmap Definitions 19

A Fuzzy MOP Based Competence Set Expansion Method for Techno...

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19th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT)

作者： Huang, Chi-Yo Lin, Yen-Chu Yang, Chia-Li Sun, Yu Cheng, Jeng-Chieh Kuo, Ying-Ting Wang, Liang-Chieh Wang, Sing-Yan Hsu, Hao-En Hsu, Hao-Hsiu Natl Taiwan Normal Univ Dept Ind Educ Taipei Taiwan Natl Ctr High Speed Comp Taipei Taiwan

ISBN: (纸本)9781643681153;9781643681146

Technology roadmaps have already been widely adopted as an important management tool during the past three decades after the invention of the management tool by Motorola in the 1980s. The technology road-mapping processes which can be integrated with firms' competence sets are very important for strategy definitions. However, how the uncertainties being associated with the costs, time, quality, etc. for technology road mapping were seldom discussed, not to mention how various objectives can be considered at the same time. Thus, this research aims to propose a fuzzy multiple objective programming based competence set expansion technique to resolve the above mentioned technology road-mapping problem. An empirical study based on the road-mapping of novel compressors for air conditioners will be used to demonstrate the feasibility of the proposed framework. The well-verified analytic framework can serve as a basis for research and development (R&D) strategy definitions by practitioners.

关键词： competence set expansion technology roadmap multiple objective programming

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Land use optimization through bridging multiobjective optimization and multicriteria decision-making models (case study: Tilabad Watershed, Golestan Province, Iran)

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NATURAL RESOURCE MODELING 2021年第2期34卷 e12301-e12301页

作者： Sheikh, Vahedberdi Salmani, Hossein Mahiny, Abdolrassoul Salman Ownegh, Majid Fathabadi, Abolhasan Gorgan Univ Agr Sci & Nat Resources Watershed Management Dept POB *** Gorgan Golestan Iran Gorgan Univ Agr Sci & Nat Resources Dept Environm Sci Gorgan Golestan Iran Gorgan Univ Agr Sci & Nat Resources Dept Arid Zone Management Gorgan Golestan Iran Gonbad Kavous Univ Dept Nat Resources Gonbad Golestan Iran

This study aims to present an efficient methodology for land use optimization based on minimization of runoff and sediment and maximization of economic benefits, occupational opportunities, and land use suitability in the Tilabad watershed in northeast of Iran. The land use map of the area was prepared using the Landsat satellite images and field surveys. The amounts of runoff and sediment were estimated via SWAT model. The TOPSIS multicriteria decision-making (MCDM) approach was applied on the results of the multiobjective optimization (MOO) based on non-dominated sorting genetic algorithm II (NSGA II) to choose the final optimal solution among the Pareto solutions front generated by MOO. The results indicated that the area of agriculture and rangelands should decrease, and the area of forests should increase to achieve the defined objectives. Overall, results indicated that integration of MOO and MCDM provides an efficient procedure for land use optimization in a complex watershed. Recommendations for Resource Managers The optimization of land use allocation across a complex watershed requires the combined application of several models and techniques to achieve a sustainable decision-making process. By optimization of land use patterns according to the final solution, the surface runoff and sediment load of the watershed will decline, while the economic profit and land use suitability will improve. Despite using a maximization objective function, the land-based job opportunities might decrease across a watershed by optimization of land use allocation, but this can be considered as an opportunity for provision of manpower to other socioeconomic sectors.

关键词： genetic algorithm NSGA‐ II land use planning multiple objective programming SWAT the Tilabad watershed TOPSIS

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Ratio-based data envelopment analysis: An interactive approach to identify benchmark

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RESULTS IN CONTROL AND OPTIMIZATION 2022年 6卷

作者： Mozaffari, M. R. Gerami, J. Wanke, P. F. Kamyab, P. Peyvas, M. Islamic Azad Univ Dept Math Shiraz Branch Shiraz Iran Univ Fed Rio de Janeiro COPPEAD Grad Business Sch Rio De Janeiro Brazil Islamic Azad Univ Dept Math Rasht Branch Rasht Iran

In the real world we are confronted with many cases where the ratio of input/output data is so important for managers, so in this regard we cannot use traditional Data Envelopment Analysis (DEA) models to evaluate the efficiency of decision -making units (DMU) and we should use DEA models based on ratio data. To get the corresponding benchmark for each inefficient decision -making unit, we need to reduce and increase the inputs and outputs, respectively, and get the unified projection of the decision maker unit on the efficiency frontier. In this paper we present a multi -objective linear programming (MOLP) model for evaluating efficiency based on defining the production possibility set in the presence of ratio data and to get the corresponding benchmark to each decision -making unit DMU. We use the Zionts and Wallenius (Z-W) interactive method to solve the MOLP model presented. Using the target setting by manager among the solutions resulting from the MOLP problem, we choose best solution according to the managers' preferences as benchmark and at the end we present the results of the research.

关键词： Efficiency DEA-R Benchmark multiple objective programming Interactive method

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Finding a representative nondominated set for multi-objective mixed integer programs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期272卷 61-77页

作者： Ceyhan, Gokhan Koksalan, Murat Lokman, Banu Middle East Tech Univ Dept Ind Engn TR-06800 Ankara Turkey

In this paper, we develop algorithms to find small representative sets of nondominated points that are well spread over the nondominated frontiers for multi-objective mixed integer programs. We evaluate the quality of representations of the sets by a Tchebycheff distance-based coverage gap measure. The first algorithm aims to substantially improve the computational efficiency of an existing algorithm that is designed to continue generating new points until the decision maker (DM) finds the generated set satisfactory. The algorithm improves the coverage gap value in each iteration by including the worst represented point into the set. The second algorithm, on the other hand, guarantees to achieve a desired coverage gap value imposed by the DM at the outset. In generating a new point, the algorithm constructs territories around the previously generated points that are inadmissible for the new point based on the desired coverage gap value. The third algorithm brings a holistic approach considering the solution space and the number of representative points that will be generated together. The algorithm first approximates the nondominated set by a hypersurface and uses it to plan the locations of the representative points. We conduct computational experiments on randomly generated instances of multi-objective knapsack, assignment, and mixed integer knapsack problems and show that the algorithms work well. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Mixed integer programming Nondominated point Representative subset

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A fuzzy multiple objective nonlinear optimization problem and its duality results

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第2期36卷 1361-1373页

作者： Gupta, S. K. Dangar, D. Ahmad, I. Al-Homidan, S. Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India Marwari Coll Dept Math Ranchi Bihar India King Fahd Univ Petr & Minerals Dept Math & Stat Dhahran Saudi Arabia

In this study, we propose a formulation of a Mond-Weir type dual program for a multiobjective nonlinear optimization problem under fuzzy environment. To deal with the multiobjectivity in the formulation, we consider the concept of weak Pareto optimal solution in the fuzzy sense. We use the Hukuhara metric/difference to define the distance/difference between two fuzzy numbers. Further, we establish weak and strong duality theorems under fuzzy pseudo/quasi-convexity assumptions. Moreover, we also validate these duality relations using various numerical illustrations.

关键词： multiple objective programming Fuzzy valued function Mond-Weir type duality Partial ordering Hukuhara difference

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Multi-criteria optimization and decision-making in radiotherapy

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期277卷 1-19页

作者： Breedveld, Sebastiaan Craft, David van Haveren, Rens Heijmen, Ben Univ Med Ctr Rotterdam Dept Radiat Oncol Erasmus MC Canc Inst Rotterdam Netherlands Massachusetts Gen Hosp Boston MA 02114 USA Harvard Med Sch Dept Radiat Oncol Boston MA 02115 USA

Radiotherapy (radiation therapy) is one of the main treatments for cancer. The aim is to deliver a prescribed radiation dose to the tumor, while keeping the unavoidable dose to the surrounding healthy organs as low as possible to minimize the probability of developing radiation induced complications. Radio-therapy treatment plan optimization strives to find machine parameters that result in desirable treatment plans. This is a large scale nonconvex multi-criteria optimization problem. In this review, we focus on the multi-criteria and decision-making aspects of radiotherapy treatment plan optimization. Shaping the 3D dose distribution within the patient involves balancing 10-30 highly correlated criteria, subject to the (in general) nonconvex mechanical machine parameters and time constraints, both in plan generation and delivery time of the treatment itself. Furthermore, each patient has a unique anatomy and unique (but unknown) radiosensitivity levels for each organ. This complicates decision-making, as the trade-offs are different for each patient, the patient-specific "safe" levels are unknown, and the interplay between different damaged organs to a physical complication is not always clear. There is no "best" plan for a patient, and decisions made are based on the insights and experience of the treating physician. In this review we describe the use of multi-criteria and decision-making methods used in modern radiotherapy. To understand the difficulties and the many levels in which multi-criteria optimization and decision-making are involved, a thorough background is given. We also provide basic treatment planning guidelines and directions to datasets for those who wish to further explore the field of radiotherapy. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Decision processes OR in health services Radiotherapy

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