Improving Expert Judgment in Scenario Simulation for ContainerTerminals and Supply Chain

Abstract

This study explores the use of Modeling and Simulation (M&S) and Structured Expert Judgment (SEJ) to handle complex problems in the logistics sector, with an emphasis on container terminals. This paper focuses on expert judgment within the Generator of Logistics Flow (GOLF), a model that uses SEJ to produce synthetic logistics data sets when empirical data are incomplete, uninformative, or unavailable. Combining SEJ with a stochastic generator enables quantification of unknown parameters through uncertainty adjustment and propagating that uncertainty into scenario outputs. Partial validation shows high precision for rail container mode approximately 3%, while larger errors for transshipped containers reveal sensitivity in this submodel. Furthermore, GOLF highlights the role of strategic engineering, which integrates modeling, simulation, and data analytics in a closed loop to provide decision makers for policy implementation and strategy design. It allows decision makers to perform “What if?” analyzes to obtain a variety of useful data in different scenarios and demonstrate the potential of intelligent systems to address data scarcity, enhance operational efficiency, and foster innovation in supply chain and container terminal management.