Consensus Modeling with Asymmetric Cost Based on Data-Driven Robust Optimization

Abstract

The robust optimization method has progressively become a research hot spot as a valuable means for dealing with parameter uncertainty in optimization problems. Based on the asymmetric cost consensus model, this paper considers the uncertainties of the experts' unit adjustment costs under the background of group decision making. At the same time, four uncertain level parameters are introduced. For three types of minimum cost consensus models with direction restrictions, including MCCM-DC, -MCCM-DC and threshold-based (TB)-MCCM-DC, the robust cost consensus models corresponding to four types of uncertainty sets (Box set, Ellipsoid set, Polyhedron set and Interval-Polyhedron set) are established. Sensitivity analysis is carried out under different parameter conditions to determine the robustness of the solutions obtained from robust optimization models. The robust optimization models are then compared to the minimum cost models for consensus. The example results show that the Interval-Polyhedron set's robust models have the smallest total costs and strongest robustness. Decision makers can choose the combination of uncertainty sets and uncertain levels according to their risk preferences to minimize the total cost. Finally, in order to reduce the conservatism of the classical robust optimization method, the pricing information of the new product MACUBE 550 is used to build a data-driven robust optimization model. Ellipsoid uncertainty set is proved to better trade-off the average performance and robust performance through different measurement indicators. Therefore, the uncertainty set can be selected according to the needs of the group.

Chaotic Arc Adaptive Grasshopper Optimization

“…Hence, the critical step in dealing with these problems is the solution approach. Optimization is a set of methods that can deal with single-objective tasks [6], multiobjective case studies [7], robust optimization scenarios [8], large-scale optimization tasks with many variables [9], [10],…”

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

“…Artificial neural networks (ANN), as one of the most known AI-based solutions, have received increasing attraction recently [58][59][60][61][62]. More technically, deep learning-based [63][64][65][66], machine learning [67][68][69], decision making-based theories, feature selection-based solutions [70][71][72], extremer machine learning solutions [73][74][75][76], as well as hybrid searching algorithms that enhanced conventional multilayer perceptron like harris hawks optimization [77,78], whale optimizer [79,80], bacterial foraging optimization [81], chaos enhanced grey wolf optimization [82], moth-flame optimizer [74,83], many-objective sizing optimization [84][85][86][87][88][89], Driven Robust Optimization [90], ant colony optimization [91], and global numerical optimization [92]. These techniques are successfully employed in different aspects such as building design [93][94][95][96][97][98][99]…”

New Models for Home Health Care under Uncertainty with Consideration of the Coordinated Development of Economy and Environment

“…Some scholars have studied the home health care routing problem through an intelligent algorithm and verified the feasibility of the paper model through practical cases [31][32][33][34]. In addition, some scholars have studied uncertain decisionmaking problems and multicriteria decision-making problems through robust optimization [35][36][37][38]. erefore, based on the above characteristics, the robust optimization method 2 Complexity has extensive research value in the theoretical and practical fields.…”