Agent-based simulations for advanced supply chain planning and scheduling: The FAMASS methodological framework for requirements analysis

(2012): Agent-based simulations for advanced supply chain planning and scheduling: The FAMASS methodological framework for requirements analysis, This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Agent-based systems have been employed in the Supply Chain Management field since the 1990s. In spite of its appealing and extensive use in both research and practice, the agent technology and its integration with advanced supply chain planning and scheduling tools still represent an emergent field with many open research questions. Particularly, the literature fails to provide an integrated framework to identify, model and conduct simulation experiments covering the whole simulation cycle. Indeed, the initial modelling effort performed at the analysis phase is especially neglected by the literature concerned. This early phase is critical because it considerably influences the whole development process as well as the resulting simulation experiments. Thus, this article presents a novel methodological framework called FAMASS (FORAC Architecture for Modelling Agent-based Simulation for Supply chain planning), which provides: (i) a uniform representation of distributed advanced supply chain planning and scheduling systems using agent technology; and (ii) a methodological approach supporting analysts in defining functional requirements of possible simulation experiments. The proposed methodological framework was tested through a real-scale proof-of-concept case employing data from two industrial partners. 1. Introduction Supply chain (SC) planning is an important and complex business process. It aims to obtain a balance between supply and demand, from suppliers to customers , in order to deliver superior goods and services through the optimisation of SC assets. This is quite a difficult task since it involves synchronising a large quantity of complex decisions. To cope with the complexity of the SC planning and scheduling process, decision support tools have been developed since the last decade (Shapiro 2000). Perhaps one of the most prominent approaches in this area …