He Huang

Multi-criteria Decision Making (MCDM) involves evaluating alternatives based on a comprehensive set of conflicting criteria, often requiring the involvement of varied decision makers. This has led to the emergence of stakeholder-based multi-criteria group decision-making (MCGDM) frameworks. However, traditional MCGDM frequently overlooks the interactions and trade-offs among different actors and criteria. The Multi-actor Multi-criteria Analysis (MAMCA), developed in 2000, provides a transparent decision-making process explicitly considering these interrelationships. It allows diverse stakeholder groups to represent their priorities, thereby enhancing their understanding of their own and others’ positions. Over the past two decades, MAMCA has seen a significant rise in popularity and has been widely applied in diverse contexts, proving valuable as both a decision-making and stakeholder engagement tool. However, our analysis of publications on cases in which MAMCA was applied over the years shows that considerable variation exists in the overall process approach, contingent upon the specific goals and context. To address these variations, this paper proposes a modularized MAMCA structure, complemented by systematic application guidelines, to aid future users in navigating the process steps and identifying the most suitable methods for each step. Additionally, future research directions are suggested for potential enhancements to the MAMCA framework by integrating varied methodologies.

We recommend facilitating sustainable logistics policy development using multicriteria satisfaction analysis. With regard to this policy recommendation, through a case study of preference mapping for cargo bike last-mile delivery we demonstrate the following: (1) The proposed MUlticriteria Satisfaction Analysis (MUSA) based public perception elicitation survey tool offers an alternative approach to map public preferences in sustainable policy decision-making; (2) The findings suggests different cities have different sustainability priorities for sustainable urban freight transport; and (3) City managers and logistics practitioners could offer tailored policies and services to address citizens’ needs.

In the constantly evolving transportation and mobility industry, objective and reliable decision‐support systems (DSS) are crucial for addressing complex issues such as transit planning, mode selection, and policy formulation. This paper presents multiactor multicriteria analysis plus agent‐based modeling (MAMCABM), a novel framework that combines multiactor multicriteria analysis (MAMCA) and agent‐based modeling (ABM) to provide a comprehensive DSS. MAMCA excels in facilitating stakeholder‐centric evaluations, while ABM, enhanced by data analytics, adeptly models intricate, interactive systems. The combination of MAMCA and ABM enhances adaptability and precision in decision making. This integration utilizes data analytics and optimization algorithms to provide solutions that consider multifaceted criteria and diverse stakeholder perspectives in dynamic and uncertain contexts. The study outlines the mathematical underpinnings of MAMCABM and offers a practical guide for its implementation. The framework's efficacy is demonstrated through an empirical investigation that addresses mobility challenges in the Brussels Capital Region of Belgium. Compared to the previous study, this approach leverages simulated quantitative data alongside qualitative judgments from stakeholders. The integration of a consensus‐reaching algorithm further enhances the robustness of outcomes and effectively addresses uncertainties.

Multi-line charts are commonly used in multi-criteria decision-making (MCDM) to represent multiple data series on the same graph. However, the presence of conflicting criteria or divergent viewpoints introduces the challenge of accurately interpreting these charts, necessitating thoughtful design to improve their comprehensibility. In this paper, we model these multi-line charts as connected perfect matching bipartite graphs. We propose a metric called the Coefficient of Complexity (CoC) to quantify the complexity of these multi-line charts. In order to reduce the visual complexity of these charts, we propose to minimize the CoC by modeling it as an integer linear optimization problem (reminiscent of the traveling salesman problem). We demonstrate our techniques through multiple real-life case studies, wherein multi-line charts serve as data visualization across various MCDM software tools. Additionally, multi-line charts with specific requirements have been optimized using our approach, showcasing the adaptability and efficacy of our technique. We also formulate the radar chart as a specialized form of the multi-line chart, and adapt our technique to improve its comprehensibility. The proposed CoC and its optimization are important contributions to the field of analytics, as a number of methods use multi-line charts for visual aid. Consequently, enhancing their comprehensibility can facilitate the decision-making process and help decision-makers gain insights.