Proceedings of the 2011 Winter Simulation Conference S. Jain, R.R. Creasey, J. Himmelspach, K.P. White, and M. Fu, eds. LOGISTICS SYSTEMS MODELING AND SIMULATION George Thiers Leon McGinnis The Georgia Institute of Technology School of Industrial & Systems Engineering Atlanta, GA 30332-0205 USA ABSTRACT Modern logistics systems are much more than simply networks of material flow. They involve collabora- tion between firms that are also competitors. The supply chain can be a key consideration in product de- sign, with its design and operations influenced by concerns about uncertain energy costs, sustainability, economic security, and other complex issues. Because of these and other considerations, the contempo- rary practice in which an analysis model is the first ―formal‖ model of the logistics system is no longer feasible. Rather, what is required for a sustainable practice of simulation in logistics is a model-based ap- proach which begins with a formal language for capturing a defining description of the logistics system itself. This formal language must be sufficiently accessible to the logistics systems stakeholders so that they can validate the resulting system description. The resulting descriptive model will be the basis for subsequent analyses, including simulation. In this context, we address the requirements for such a formal language, describe our initial progress in developing such a language for logistics systems, and place it in the context of prior work on ―reference models.‖ 1 INTRODUCTION Global supply chains (GSCs) are complex socio-technical systems, and a key feature of modern civiliza- tion. GSCs can link many firms, involve many locations and transportation channels, concern thousands of part numbers, and be responsible for hundreds of thousands of shipments on an annual basis. Stake- holders include the firms involved in producing the goods, the customers for the goods, and all the firms involved in moving and storing the goods. The cost of GSCs is significant; for example, recent logistics costs in the US have ranged between 7.7% and 9.3% of GDP (Zhao 2010). Even small improvements in GSCs have potentially large benefits to society. This helps to explain the sustained strong interest in lo- gistics systems modeling and analysis. The research literature on global supply chains is dominated by OR models, particularly optimization and simulation models. Optimization models generally answer questions of the form, ―Where should we produce, where should we have warehouses, how much inventory of which parts should be kept in each warehouse, how often should shipments be made, and what should be the origin and destination for ship- ments in order to satisfy the requirements of our customers at minimum expected logistics costs?‖ Opti- mization models are ―normative‖ in that they assume all facts are known, and produce a ―best‖ answer to those questions given those facts. Simulation models, in contrast, assume that the answers to the ques- tions are known but recognize uncertainties associated with some of the facts, and attempt to produce a more realistic picture of what the performance will really be in terms of measures such as customer ser- vice and cost. Contemporary research on GSCs admits two possibilities for significant enhancement in their design and analysis: (1) modern GSCs introduce issues not captured in legacy modeling approaches; and (2) modern GSCs often are so large and complex that no single stakeholder can fully understand and describe the entire system. The thesis of this paper is that addressing these two needed enhancements will require 1536 978-1-4577-2109-0/11/$26.00 ©2011 IEEE