## 1 Introduction  
A main challenge for the agri-food supply chain (ASC) relates to the need for logistic systems for planning and scheduling/rescheduling due to unpredictable variations in quality, moment and quantity in primary production; the need for high efficiency of technical equipment despite long food industry production times; and an intricate network structure where many farms and food processors trade with multinationals in the wholesaler/retail sector. This challenge is derived from the following ASC characteristics [1]: (i) for the primary producer echelon, the seasonal growth is often limited to a specific period in a geographic region, dependent on weather and agricultural practices management, (ii) for the food processor echelon, high volume and low variety production systems combined with high volume frequent deliveries, and (iii) for the wholesaler/retailer echelon, the variability of quality and quantity of supply of farm-based inputs coupled with high demands from consumers, including food safety legislation and quality standards. This challenge calls for joint decision making in order to leverage the knowledge resources in the ASC. From an agent-based modeling and simulation approach, a system is modeled as a set of autonomous agents that interact with each other and the environment; the agents have behaviors that are influenced by agent's interactions [2]. According to an agent's behavior [3] (i) agents can respond in an event-action-mode (reactive agent), (ii) agents can have domain knowledge to undertake a sequence of actions in order to achieve a goal (deliberative agent), and (iii) agents can encompass both of this features. These behaviors have important implications in the use of the agent-based modeling approach as a valid methodology to model the supply chain (SC). Moreover, for SC researches and practitioners, [PERSON] and [PERSON] [4] stressed the benefits of agent-based decision support systems including the ability to convert manager experience into agents, the ability to conduct experiments and what-if analysis through simulation-based decision support systems and the increased predictability of operations in the real system. The core functionality component of a simulation model-driven decision support system is a quantitative model and is used by decision-makers to help in analyzing a real system by means of modeling and data collection, model validation, system parameter setting, and system evaluation [5]. Agent-based modeling use and simulation for decision-making within the agri-food industry has been applied considering the integration of logistics, quality decay and sustainability modeling [6; 7]. According to findings from the review conducted by [PERSON] et al. [8], most agent-based modeling and simulation applications in agri-food supply chains focus on the simulation of production planning and investment decisions. However, although there is a volume of literature about applications of agent-based modeling and simulation in the agri-food supply chains, there is a lack of studies that consider important actors, such as food processors and retailers in the scope of the model, since most agent-based modeling and simulation applications focus on one echelon [8]. A make-to-order ASC is considered for this study. For these types of chains, [PERSON] et al. [9] classified the improvement opportunities for the SC modeling approaches into three categories: model building, model quality, and model execution. Regarding the agent-based modeling approach, the authors highlighted the issue with model quality as a measure of how well a model represents the aspects of interest in a real system and how a completely agent-based approach is not the best way to represent the entire supply chain. Consequently, as the agent-based approach focus on the behavior and decision processes of individual participants, often at the expense of event-oriented aspects of the supply chain, hybrid configurations are often necessary. The aim of this paper is to contribute to these strands of research.