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A time-dependent freight tour synthesis model

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2015年 78卷 144-168页

作者： Sanchez-Diaz, Ivan Holguin-Veras, Jose Ban, Xuegang (Jeff) Chalmers Univ Technol Dept Technol Management & Econ S-41296 Gothenburg Sweden Rensselaer Polytech Inst Dept Civil & Environm Engn Troy NY 12180 USA

This paper introduces a model of urban freight demand that seeks to estimate tour flows from secondary data sources e.g., traffic counts, to bypass the need for expensive surveys. The model discussed in this paper, referred as Freight Tour Synthesis (FTS), enhances current techniques by incorporating the time-dependent tour-based behavior of freight vehicles, and the decision maker's (e.g., metropolitan planning agency planner) preferences for different sources of information. The model, based on entropy maximization theory, estimates the most likely set of tour flows, given a set of freight trip generation estimates, a set of traffic counts per time interval, and total freight transportation cost in the network. The type of inputs used allows the assessment of changes in infrastructure, policy and land use. The ability of the model to replicate actual values is assessed using the Denver Region (CO) as a case study. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Freight demand model OD synthesis multi-attribute value function Freight tours Urban freight Urban freight data

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Decision support in nuclear emergencies

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JOURNAL OF HAZARDOUS MATERIALS 2000年第1-3期71卷 321-342页

作者： Papamichail, KN French, S Univ Manchester Sch Informat Manchester M13 9PL Lancs England

In a nuclear emergency, protective actions such as evacuation, sheltering and food bans can be taken to mitigate the consequences of any release of radioactivity. Within the RODOS project, an evaluation framework has been developed to support the assessment of the costs and benefits of potential actions. In order to help the decision makers gain insight into the decision problem and clarify their preferences, guidance can be given in three stages. First, the search of feasible portfolios of protective actions is seen as a constraint satisfaction problem;only those portfolios that satisfy constraints depending on factors such as feasibility are worth further evaluation. Second, the portfolios are ranked based on their consequences and the preferences of the decision makers using either a multi-attribute value or utility function. Third, a natural language report explaining the ranking is produced to help the decision makers gain insight into the decision problem and refine the decision parameters. An intelligent decision system has been developed to demonstrate the feasibility of the framework. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： constraint satisfaction techniques explanation multi-attribute value function risk analysis

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Maintenance scheduling of a fleet of fighter aircraft through multi-objective simulation-optimization

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2014年第9期90卷 1023-1040页

作者： Mattila, Ville Virtanen, Kai Aalto Univ Sch Sci Dept Math & Syst Anal Syst Anal Lab FI-00076 Aalto Finland

The maintenance scheduling problem of a fleet of fighter aircraft is considered through multi-objective simulation-optimization (MOSO). In the problem, a maintenance schedule consisting of target starting times of the maintenance activities of the aircraft is determined. The objectives are to minimize average deviation between the target and actual starting times of the activities and to maximize average aircraft availability. The objectives depend on the maintenance schedule through complex interactions due to a policy in which the need for maintenance is based on the flight hours of the aircraft cumulated during flight missions. In addition, the durations of the flight missions, maintenance activities, and failure repairs are uncertain. Therefore, an MOSO approach is applied to the problem. The approach includes a discrete-event simulation model and a state-of-the art multi-objective simulated annealing algorithm for determining non-dominated schedules. Moreover, a multi-attribute value (MAV) function is used for supporting a maintenance decision-maker (DM) in selecting the preferred non-dominated schedule for implementation. The MAV function captures incomplete information on the values of the objectives as well as on the DM's preference statements regarding the weights of the objectives. The approach is implemented as an MOSO tool whereby the DM can consider the complex interactions and uncertainties of the problem which have not been addressed in the existing literature on maintenance scheduling. The approach and the tool are illustrated with a set of test problems as well as a real-life example problem.

关键词： Aircraft maintenance scheduling multi-attribute value function multi-objective simulation-optimization simulated annealing

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Inducing probability distributions on the set of value functions by Subjective Stochastic Ordinal Regression

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KNOWLEDGE-BASED SYSTEMS 2016年 112卷 26-36页

作者： Corrente, Salvatore Greco, Salvatore Kadzinski, Milosz Slowinski, Roman Univ Catania Dept Econ & Business Corso Italia 55 I-95129 Catania Italy Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland Univ Portsmouth Portsmouth Business Sch CORL Richmond BldgPortland St Portsmouth PO1 3DE Hants England

Ordinal regression methods of multiple Criteria Decision Aiding (MCDA) take into account one, several, or all value functions compatible with the indirect preference information provided by the Decision Maker (DM). When dealing with multiple criteria ranking problems, typically, this information is a series of holistic and certain judgments having the form of pairwise comparisons of some reference alternatives, indicating that alternative a is certainly either preferred to or indifferent with alternative b. In some decision situations, it might be useful, however, to additionally account for uncertain pairwise comparisons interpreted in the following way: although the preference of a over b is not certain, it is more credible than preference of b over a. To handle certain and uncertain preference information, we propose a new approach that builds a probability distribution over the space of all value functions compatible with the DM's certain holistic judgments. A didactic example shows the applicability of the proposed approach. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multiple criteria decision aiding Ordinal regression Stochastic multiobjective acceptability analysis multi-attribute value function Uncertain preference information Probability distribution

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The behavioral TOPSIS

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 89卷 266-272页

作者： Yoon, K. Paul Kim, Won Kyung Fairleigh Dickinson Univ Dept Informat Syst & Decis Sci Silberman Coll Business 1000 River Rd Teaneck NJ 07666 USA Kyungnam Univ Dept Comp Engn Wolyundong 449 Masan 631702 Kyungnam South Korea

This paper updates and expands the TOPSIS that was introduced four decades ago. First, we re-present the logic of TOPSIS according to the traditional decision theory. It shows that TOPSIS also has a built-in multi-attribute value function that is not revealed explicitly. So far, this has been a hidden aspect of TOPSIS. Second, we incorporate the decision maker's (DM) behavioral tendency into TOPSIS. The behavioral TOPSIS accommodates the loss aversion concept in behavioral economics. A DM can set the loss aversion ratio to reflect his/her choice inclination. Two cases are illustrated to show the efficacy of the behavioral TOPSIS.(C) 2017 Elsevier Ltd. All rights reserved.

关键词： multiple attribute decision making TOPSIS Efficient frontier multi-attribute value function Behavioral economics Loss aversion Endowment effect

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Low cost carriers' destination selection using a Delphi method

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TOURISM MANAGEMENT 2008年第5期29卷 898-908页

作者： Chang, Yu-Chun Hsu, Chia-Jui Williams, George Pan, Mei-Ling Natl Taiwan Ocean Univ Dept Transportat & Navigat Sci Chilung Taiwan Kainan Univ Dept Air Transportat Tao Yuan Taiwan Cranfield Univ Air Transport Dept Cranfield MK43 0AL Beds England Natl Taiwan Univ Dept Civil Engn Taipei 10764 Taiwan

Over the past two decades, demand for air transport between Taiwan and China has grown rapidly, partly in response to the economic development of the two countries, but also as a result of political factors. Low cost carriers (LCC) that have operated for several decades in the United States and Europe will provide a good operational benchmark for direct cross-strait flights once these services become possible. This research involves the construction of a sequential destination selection model for direct cross-strait flights by LCC based on a multi-attribute value function. The selection framework comprises three elements: destination competitiveness, airport infrastructure and potential demand. Previous research focused only on a few alternative airports, but the 41 airports available for foreign flights in Mainland China have all been included in the empirical analysis undertaken in this paper. The results therefore fit better with the operational features of LCC who are normally seeking suitable secondary airports rather than traffic hubs. The analysis reveals that six airports would be suitable for LCC, namely, Shanghai Pudong, Shenzhen, Xiamen, Hangzhou, Wuhan and Nanjing. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： LCC direct cross-strait flights multi-attribute value function sensitivity analysis

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An active preference learning approach to aid the selection of validators in blockchain environments

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2023年 118卷

作者： Gehrlein, Jonas Miebs, Grzegorz Brunelli, Matteo Kadzinski, Milosz Web3 Fdn Zug Switzerland Poznati Univ Technol Inst Comp Sci Piotrowo 2 PL-60965 Poznan Poland Univ Trento Dept Ind Engn Trento Italy

We consider a real-world problem faced in some blockchain ecosystems that select their active validators-the actors that maintain the blockchain-from a larger set of candidates through an electionbased mechanism. Specifically, we focus on Polkadot, a protocol that aggregates preference lists from another set of actors, nominators, that contain a limited number of trusted validators and thereby influence the election's outcome. This process is financially incentivized but often overwhelms human decision makers due to the problem's complexity and the multitude of available alternatives. This paper presents a decision support system (DSS) to help the nominators choose the validators in an environment with frequently changing data. The system structures the relevant multiple attribute problem and incorporates a dedicated active learning algorithm. Its goal is to find a sufficiently small set of pairwise elicitation questions to infer nominators' preferences. We test the proposed solution in an experiment with 115 real nominators from the Polkadot ecosystem. The empirical results confirm that our approach outperforms the unaided process in terms of required interaction time, imposed cognitive effort, and offered efficacy. The developed DSS can be easily extended to other blockchain ecosystems.& COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Blockchain ecosystem Validator selection multiple criteria decision analysis Active learning Preference learning multi-attribute value function

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An Application of multi-attribute value Theory to Patient-Bed Assignment in Hospital Admission Management: an Empirical Study

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JOURNAL OF HEALTHCARE ENGINEERING 2014年第4期5卷 439-456页

作者： Tsai, Pei-Fang (Jennifer) Lin, Fu-Man Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan Mackay Mem Hosp Med Affairs Dept Taipei 10449 Taiwan

Hospital beds are considered economically scarce and hospitalists strive to balance between utilizing beds more efficiently and complying with preference of physicians and patients when pairing patients to beds. This research is to develop preference-based decision rules for patient-bed assignment in a dynamic environment. A multi-attribute value theory (MAVT) model with additive value function is proposed to quantitatively deploy hospital policies in bed management. To elicit scaling factors and value functions for attributes, a linear programming model is constructed for all preference conditions. An empirical study was conducted with real data collected from two branches of a medical center. The simulated results using value function showed greater benefits when the patient-bed ratio was high and more flexible ward assignment was allowed. Further, a detailed analysis showed that this MAVT model was better in preference matching for both physicians/nurses and patients. At least 79 percent of patients were given beds in designated wards in accordance with their attending physicians' subspecialty, and more than 48 percent of patients' room preferences were matched in the simulated assignment for one branch.

关键词： Preference-based decision multi-attribute value function waiting list prioritization healthcare admission process

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MCDM methods in strategic planning of forestry on state-owned lands in finland: Applications and experiences

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Journal of multi-Criteria Decision Analysis 2001年第5期10卷 257-271页

作者： Kangas, Jyrki Kangas, Annika Leskinen, Pekka Pykäläinen, Jouni Finnish Forest Research Institute Kannus Research Station Finland University of Joensuu Faculty of Forestry Finland

The forests in Finland have been under intensive planning for decades. Currently, mathematical programming is widely used in planning of wood production. Today’s multi-functional forestry, however, calls for more flexible decision support methods. MCDM tools have been used in responding to fresh planning challenges. For example, the Finnish Forest and Park Service, entrusted with the care of the vast majority of state-owned natural resources in Finland, endeavours to produce large-scale natural resource plans satisfying the needs of both economic, social, and ecological sustainability. Participatory approach is applied in the process. Several forestry applications of MCDM methods, particularly those making use of the AHP or the HIPRE program, have been presented. Also, the outranking methods ELECTRE and PROMETHEE have been tested. Due to the nature of forestry applications, statistical techniques for analysing uncertainties in pairwise comparisons and for utilizing interval judgement data have been developed to improve the usability of the AHP. Recently, a hybrid method called A’WOT, making use of the AHP and SWOT, was also introduced into strategic forest planning. This paper summarizes the experiences gained in applying a MAVT and two outranking methods in connection with a participatory natural resource planning process in Finland. In addition, some results of the method development work related to application needs are briefly presented. The details of the planning cases reviewed here have previously been presented in forestry journals. The purpose of this paper is not only to show how MCDM methods have been applied in forestry, but also to discuss the usability and usefulness of MCDM methods from the viewpoint of supporting forestry decision making-and how they might further be improved. Also, some perspectives for the future development work of MCDM applications in the field of natural resource management are focused on. As a conclusion, the use of more than j

关键词： Forest management Forestry decision support multi-attribute value function Outranking Participatory planning Statistical analysis

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