Consensus Building for Uncertain Large-Scale Group Decision-Making Based on the Clustering Algorithm and Robust Discrete Optimization

2022 | Group Decision and Negotiation | Citations: 0

Authors: Li, Yuanming; Ji, Ying; Qu, Shaojian

Abstract: Consensus reaching processes (CRPs) including the feedback adjustment mechanism ... Expand

Abstract: Consensus reaching processes (CRPs) including the feedback adjustment mechanism generally require extended periods of time to bridge the opinion gap among decision makers. Therefore, minimum cost consensus (MCC) problems with known adjustment costs have been widely reported. However, the exact unit adjustment costs are difficult to obtain through practical CRPs. To solve these problems, this paper proposes a novel CRP framework for uncertain large-scale group decision-making based on robust discrete optimization. First, an enhanced iterative self-organizing data analysis technique algorithm is provided to dynamically cluster decision makers together in small subgroups under interval opinions. Second, to establish the optimization-based consensus rules in the feedback process, an MCC integer optimization model is established to minimize the total consensus costs in consensus reaching. Furthermore, with the indeterminate unit adjusting costs, a robust discrete MCC optimization model is constructed, which can control the degree of conservatism of the optimal consensus opinion and compute the optimal modified opinions of decision makers. Finally, a case study and comparative analysis indicate the effectiveness and superiority of the proposed CRP method and that the robust discrete MCC model has stronger robustness in the uncertain decision environment. Collapse

Topics: decision making innovation management

Methods: cluster analysis computational algorithm discrete optimization case study k-means clustering

Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment

2019 | Group Decision and Negotiation | Citations: 0

Authors: Marsa-Maestre, Ivan; de la Hoz, Enrique; Gimenez-Guzman, Jose Manuel; Orden, David; Klein, Mark

Abstract: At the present time, Wi-Fi networks are everywhere. They operate in unlicensed r ... Expand

Abstract: At the present time, Wi-Fi networks are everywhere. They operate in unlicensed radio-frequency spectrum bands (divided in channels), which are highly congested. The purpose of this paper is to tackle the problem of channel assignment in Wi-Fi networks. To this end, we have modeled the networks as multilayer graphs, in a way that frequency channel assignment becomes a graph coloring problem. For a high number and variety of scenarios, we have solved the problem with two different automated negotiation techniques: a hill-climbing mediated negotiation and a simulated annealing mediated negotiation. As an upper bound reference for the performance of these two techniques, we have also solved the problem using a particle swarm optimizer. Results show that the annealer negotiator behaves as the best choice because it is able to obtain even better results than the particle swarm optimizer in the most complex scenarios under study, with running times one order of magnitude below. Moreover, we study how different properties of the network layout affect to the performance gain that the annealer is able to obtain with respect to the particle swarm optimizer. Finally, we show how the different strategic behavior of the participants affects the results. Collapse

Topics: wireless LAN economic surplus IS economics local area network

Methods: experiment optimization model computational algorithm discrete optimization cluster analysis

Theories: graph theory

Heterogeneous Electric Vehicle Charging Coordination: A Variable Charging Speed Approach

2019 | HICSS | Citations: 0

Authors: Valogianni, Konstantina; Ketter, Wolfgang; Collins, John; Adomavicius, Gediminas

Abstract: We present a coordination mechanism that reduces peak demand coming from EV char ... Expand

Abstract: We present a coordination mechanism that reduces peak demand coming from EV charging, supports grid stability and environmental sustainability. The proposed mechanism accounts for individual commuting preferences, as well as desired states of charge by certain deadlines, which can serve as a proxy for range anxiety. It can shape EV charging toward a desired profile, without violating individual preferences. Our mechanism mitigates herding, which is typical in populations where all agents receive the same price signals and make similar charging decisions. Furthermore, it assumes no prior knowledge about EV customers and therefore learns preferences and reactions to prices dynamically. We show through simulations that our mechanism induces a less volatile demand and lower peaks compared to currently used benchmarks. Collapse

Topics: price management environmental sustainability privacy

Methods: simulation computational algorithm trade-off analysis method multi objective optimization discrete optimization

Novel Optimization-Based Algorithms for a Substation Voltage Controller Using Local PMU Measurements

2018 | HICSS | Citations: 0

Authors: Amelian, S Mohammad; Badayos, Noah

Abstract: This paper presents a developed version of a local voltage controller for a tran ... Expand

Abstract: This paper presents a developed version of a local voltage controller for a transmission substation with available phasor measurement unit (PMU), through optimal usage of its reactive (VAr) control resources, i.e., shunt reactive devices and transformer taps. Two optimization formulations with different objectives are introduced based on various operating criteria in electric utilities. The first approach aims to minimize the required reactive power injection such that it corrects the substation bus voltages between the determined limits and as close as possible to the optimal values. The second one minimizes number of switching actions, that correct the voltages in the same way mentioned above. Genetic algorithm (GA) is used for solving these discrete optimization problems. Performance of the proposed formulations is tested and analyzed through simulations, for a typical substation in Southern California transmission network. Finally, comparison of the obtained results from the two approaches are presented and discussed. Collapse

Methods: simulation optimization model computational algorithm discrete optimization

An Introduction to the MISD Technology

2017 | HICSS | Citations: 0

Authors: Popov, Aleksey

Abstract: The growth of data volume, velocity, and variety will be the global IT challenge ... Expand

Abstract: The growth of data volume, velocity, and variety will be the global IT challenges in the next decade. To overcome performance limits, the most effective innovations such as cognitive computing, GPU, FPGA acceleration, and heterogeneous computing have to be integrated with the traditional microprocessor technology. As the fundamental part of most computational challenges, the discrete mathematics should be supported both by the computer hardware and software. But for now, the optimization methods on graphs and big data sets are generally based on software technology, while hardware support is promising to give a better result. Collapse

Topics: computer hardware enterprise architecture big data IT productivity productivity

Methods: computational algorithm experiment discrete optimization optimization model

Workload Reduction Through Usability Improvement of Hospital Information Systems - The Case of Order Set Optimization

2015 | International Conference on Information Systems | Citations: 1

Authors: Gartner, Daniel; Zhang, Yiye; Padman, Rema

Abstract: Order sets are a critical component in hospital information systems and expected ... Expand

Abstract: Order sets are a critical component in hospital information systems and expected to reduce physician workload, substantially. Order sets represent time interval-clustered order items (e.g. medications prescribed at hospital admission) which are administered to patients during their hospital stay. We develop a mathematical programming model, an exact and a heuristic solution procedure with the objective to minimize physician workload associated with prescribing order sets. In a case study using order data on Asthma patients with severe conditions from a major pediatric hospital, we compare the hospital’s current solution with the exact and heuristic solutions on a variety of performance metrics. Our computational results reveal that using an interval decomposition approach substantially reduces computation times. Our physician workload analysis revealed that our exact approach reduces the hospital’s current workload for these patients from 12% to 65% simply by allowing 1 to 5 order sets in each time interval, respectively. Collapse

Topics: health information system electronic health record Microsoft Windows operating system Java

Methods: optimization model cluster analysis computational algorithm literature study experiment