Unraveling hidden possibilities for a modal shift shift: A computational approach combining agent-based modeling and geographic information systems

Purpose: Synchromodal transport incorporates real-time events in a dynamic manner in order to facilitate the most suitable selection of modes, routes and handling points. Up until now, current modeling approaches that focus on the dynamic context of synchromodal transport make use of analytical models. Most of these models average distances for barges and trains via route mapping platforms that provide realistic distances for road only. To reflect on real-world developments more accurately, new thinking and modeling approaches are necessary to bridge academic models with physical transport processes.

Design/methodology/approach: The paper introduces a computational model which computes freight movements based on scheduling, decentralized behaviour rules and information flows of entities/agents. The distance data for each mode is computed by making use of Geographic Information Systems and agents which record their individual covered distances and delivery times.

Findings: Computational experiments are applied in a case study which concerns imports of goods of one retailer by unimodal truck transport from France to Belgium. The simulation output provides modal choice alternatives, combination of freight deliveries and its impact in terms of cost, delivery times and emissions. The dynamic synchromodal reconfiguration yields better outputs compared to static intermodal flows when tested for resilience. However in some cases, being overly proactive may be less beneficial in terms of cost and emission increase by (unnecessary) deviations and detours.

Originality/value: The objective is to support the decision making of a retailer with regard to synchromodal barge and/or train solutions. The model considers many O-D pairs (supplier locations – DC locations) at once in an automated manner. It accounts for stochastic parallel processes for each mode and simulates decentralized delivery performance of each order in terms of cost, time and emissions at an operational decision level. Furthermore, the model accounts for routing of individual orders and their responsiveness to disruptions. The end-goal is to identify the share of orders that meet cost and lead-time constraints, and offer possibilities for a modal shift and emission reduction.