## 2 Literature Overview and Work Position  
The present work will try to integrate the following strands of theory to underpin the decision support system for the ASC. The first strand related to the use of agents in SC modeling and simulation. Based on the concept of SC uncertainty revised by [PERSON] and [PERSON] [11] as a decision-making situation in which the decision-maker lacks understanding; information processing capacities and effective control actions; the authors assert that SC uncertainty could be reduced through the implementation of specific-scenario redesign strategies regarding configuration, control structure, information systems, and governance structures. Supply chain value stream mapping is a technique to leverage the knowledge of a company's supply actors [12]. [PERSON] and [PERSON] [13] proposed a modeling framework for decision-making improvement based on agents modeling the SC actors as autonomous objects assigned with decision making intelligence, jobs representing the SC activities, and types of flows (goods, information, resources, and job definitions). Broadly, an agent possesses skills and knowledge to interact with the environment including applications for cooperation, communication, command and control [14]. A multi-agent system is defined by [PERSON] et al. [15] as: \"a computer-based environment that contains multiple software agents to perform certain tasks\"; therefore, its scope refers to the breakdown of a complex solution into sub-problems then assigned to agents supported by a knowledge base. Furthermore, decision support systems agents could be classified into five types [15]: data monitoring, data gathering, modeling, domain managing, and preference learning. Indeed, agent-based decision support systems enable decision-making activities such as knowledge representation, knowledge reuse, reasoning, and inference techniques [16]. These properties have implications on industrial environment agent-based solution adoption, feasibility, breakdown robustness, ready reorganizability, effective response to external disruptions, and reconfigurability. Nevertheless, the adoption barriers comprise cost, guarantees for operational performance, scalability and standards definition [17]. Other issues related to barriers of agent-based decision support systems comprise [4] the difficulty to access data from partners in the SC, long development and validation time, long learning time, and the difficulty to develop agent rules that generates the wanted behavior. Consequently, the next generation of decision agents in SC management must consider these barriers in order to develop agents embedded in systems that will be distributed, dynamic, intelligent, integrated, responsive, reactive, cooperative, interactive, reconfigurable, and adaptable [18]. In the review conducted by [PERSON] et al. [19] regarding optimization methods for short-term scheduling, the authors stated that within the artificial intelligence field, scheduling problems have been solved by a set of individual agents which can work parallel and their coordination may bring a more effective way to find an optimal solution; the agents are expected to interact together to achieve the goals of the overall system. The second strand of theory comprises to agent-based decision making for scheduling in supply chains. According to [PERSON] et al. [20], the agent-based modeling technique is the most promising distributed approach to tackle the integration of process planning and scheduling adaptiveness. For their part, [PERSON] et al. [21] asserted that a relevant number of applications of agent-based models are devoted to SC planning problems.