This paper contributes to the field of urban waste collection systems, which are crucial for advancing sustainability, urban cleanliness, and the aesthetic quality of cities. Specifically, it introduces a novel framework designed to support planners and decision makers in the design of efficient and responsive textile waste collection systems, aligned with both environmental objectives and citizen engagement. To this end, the framework exploits a hybrid simulation platform that realistically models the logistics infrastructure in a spatially explicit environment. Also, within the framework, citizens are represented as adaptive agents whose environmental attitudes evolve through personal experience, social influence, and perceived service quality. The behavioural layer is the core element of the framework. It enables dynamic analysis of the two-way feedback between citizen participation and service effectiveness to underscore the often-overlooked role of citizen behaviour in shaping overall system performance. The model was tested in a representative urban scenario under varying operational conditions. The results highlight how policy incentives and smart collection infrastructure can significantly boost participation, while social segregation may hinder the adoption of sustainable practices. The framework ultimately offers a generalisable decision-support tool to explore the behavioural dimension of circular economy initiatives and develop robust, scenario-based strategies.

Agent-based models (ABMs) are increasingly used to study supply chain (SC) systems due to their capacity to capture decentralised behaviour, heterogeneity, and emergent dynamics. However, the mere use of agent-based simulation platforms does not necessarily imply that the models fully exploit the agent-based paradigm. This study investigates the extent to which ABMs applied to SC contexts make meaningful use of agents, based on behavioural, interactional, and systemic criteria. A systematic and thorough review of 58 academic contributions was conducted, focusing on six analytical parameters: context and objectives, justification for adopting ABMs, simulated entities, nature of interactions, system dynamics, and final classification. A central distinction is introduced between the so-called Green and Red Flag models, where the former are coherent implementations of agent-based simulation, while the latter fall short of its essential features. Further nuance is provided by two subtypes of Green Flag models: those featuring cognitively evolved agents and those based on simple interacting entities that give rise to emergent dynamics. Since the results reveal that many SC models labelled as “agent-based” lack key characteristics (such as inter-agent influence or system-level evolution) to justify the use of ABM, the pa-per provides conceptual tools to aid in distinguish between representational and functionally justified agent-based approaches. In addition, it offers both a theoretical framework and a practical evaluation guide to support the development of future models and to foster critical analysis within the field.

Agent-based models are increasingly used to study supply chain systems due to their capacity to capture decentralised behaviour, heterogeneity, and emergent dynamics. However, the mere use of agent-based simulation platforms does not necessarily imply that the models fully exploit the agent-based paradigm. Although this concern, namely, the potential misuse of agent-based models, has been frequently raised in the literature, no previous study has precisely quantified how frequently this issue occurs, especially in the supply chain domain. To address this gap, a systematic review of 58 academic contributions was conducted to evaluate the extent to which agent-based models applied to supply chain contexts adhere to the fundamental principles of agent-based simulation. Specifically, the reviewed works were classified into two categories: Green Flag models, representing coherent and appropriate implementations of agent-based models, and Red Flag models, which fail to capture the essential characteristics of agent-based simulation. The classification was based on key discriminating factors such as the type and number of simulated entities or agents, the nature of agent interactions, and the incorporation of system-level dynamics. Further nuance is provided by two subtypes of Green Flag models: those featuring intelligent agents, and those based on responsive or reactive entities, which might generate emergent dynamics. Our results reveal that almost 64% of the analysed agent-based contributions lack key characteristics to justify the use of agent-based models. Hence, the paper also provides conceptual tools to aid in distinguishing between different agent-based approaches. In conclusion, the present work offers both a theoretical framework and a practical evaluation guide to support the development of future models and to foster critical analysis within the field.