arXiv subjects

Renlong Wang

Publications and source records attributed to Renlong Wang.

3 recordsLinked to original sources

arXiv2024

Preference Robust Ordinal Priority Approach with Preference Elicitation under Incomplete Information for Multi-Attribute Robust Ranking and Selection

Ordinal Priority Approach (OPA) has recently been proposed to determine the weights of experts, attributes, and alternatives using ordinal preference without precise information for multi-attribute ranking and selection (MARS). This study extends OPA with preference elicitation under incomplete information to counter the parametric and preference uncertainty within MARS. Specifically, we propose Preference Robust Ordinal Priority Approach (OPA-PR) within a two-stage optimization framework to generalize marginal utility structure and resolve ambiguity in ranking parameters and utility preferences. In the first stage, the worst-case marginal utility functions are elicited from utility preference ambiguity sets, characterized by monotonicity, normalization, concavity, and Lipschitz continuity for global information, and moment-type preference elicitation for the local. In the second stage, decision weights are optimized based on the elicited marginal utility functions, considering the ranking parameters within norm-, budget-, and conditional value-at-risk-based ambiguity sets. We derive tractable reformulations of OPA-PR, especially through piecewise linear approximation for the marginal utility preference ambiguity sets for the first stage. This approximation is verified by the error bounds for both stages, establishing the foundation of preference elicitation strategy design. The proposed approach is demonstrated through a numerical experiment on the emergency supplier selection problem, including the case, sensitivity, and comparison tests.

math.OC↗

arXiv2024

A Novel {\delta}-SBM-OPA Approach for Policy-Driven Analysis of Carbon Emission Efficiency under Uncertainty in the Chinese Industrial Sector

Regional differences in carbon emission efficiency arise from disparities in resource distribution, industrial structure, and development level, which are often influenced by government policy preferences. However, currently, most studies fail to consider the impact of government policy preferences and data uncertainty on carbon emission efficiency. To address the above limitations, this study proposes a hybrid model based on $\delta$-slack-based model ($\delta$-SBM) and ordinal priority approach (OPA) for measuring carbon emission efficiency driven by government policy preferences under data uncertainty. The proposed $\delta$-SBM-OPA model incorporates constraints on the importance of input and output variables under different policy preference scenarios. It then develops the efficiency optimization model with Farrell frontiers and efficiency tapes to deal with the data uncertainty in input and output variables. This study demonstrates the proposed model by analyzing industrial carbon emission efficiency of Chinese provinces in 2021. It examines the carbon emission efficiency and corresponding clustering results of provinces under three types of policies: economic priority, environmental priority, and technological priority, with varying priority preferences. The results indicate that the carbon emission efficiency of the 30 provinces can mainly be categorized into technology-driven, development-balanced, and transition-potential types, with most provinces achieving optimal efficiency under the technology-dominant preferences across all policy scenarios. Ultimately, this study suggests a tailored roadmap and crucial initiatives for different provinces to progressively and systematically work towards achieving the low carbon goal.

econ.GN↗

arXiv2024

Generalized Ordinal Priority Approach for Multi-Attribute Decision-Making under Incomplete Preference Information

The Ordinal Priority Approach (OPA) is a multi-attribute decision-making (MADM) method to determine the relative importance (weights) of experts, attributes, and alternatives. This study formally establishes the fundamental properties of OPA, including solution efficiency, analytical solution expression, the decomposability of optimal decision weights, and its relationship with rank-based surrogate weights. Building on these properties, we propose a Generalized Ordinal Priority Approach (GOPA) based on an "estimate-then-optimize" contextual optimization framework for MADM when preference information is incomplete. In the first stage, we derive utility distributions for ranked alternatives in discrete and continuous prospects by minimizing cross-entropy utility under partial preference information, including weak order relations, absolute differences, ratio scales, and lower bounds. Rank-based surrogate weights and risk preference utility functions serve as the global utility structure for discrete and continuous prospects, respectively. The elicited utility information is then introduced into the second-stage problem to simultaneously optimize the weights of experts, attributes, and alternatives within a normalized weight space. Metrics for validating the group decision outcomes of GOPA, including percentage standard deviation, correlation coefficient, and confidence level measurement, are proposed. Theoretical analysis reveals several advantageous properties of GOPA, including model generalizability, analytical solvability, and risk preference independence. Furthermore, this study provides a lower bound reference for transforming the general optimization-based weight elicitation problems into optimization problems with stochastic dominance constraints. The applicability of GOPA is demonstrated through an improvisational emergency supplier selection problem.

math.OC↗