Two-layer network design is a subclass of network design problems that simultaneously addresses two interwoven sets of design decisions, each pertaining to a different network layer. We focus on the Two-Layer Network Design Problem with Capacity Decisions (2LNDCD), where two network layers are interconnected through design, flow, and capacity requirements. The objective is to design a cost-efficient two-layer network that meets multicommodity demand and capacity restrictions while adhering to standard network design constraints and specific inter-layer coupling requirements. The 2LNDCD problem introduces additional modelling and algorithmic challenges due to its complex connectivity relations and requirements. In this presentation, we provide the fundamental definitions and formulation of 2LNDCD and we propose a Benders decomposition method to solve the resulting optimization model. Preliminary results are discussed to highlight the intricacies associated with the application of this solution approach in the given context.

The objective of this research is to narrow the divide between theoretical frameworks regarding tactical planning within service network design models and their practical applications at the operational level, particularly in response to emerging information. While the concept of tactical planning is well-established within consolidation-based freight transportation systems, its adaptability in operational contexts remains insufficiently explored. Through the systematic definition of various scenarios and strategies, this study seeks to furnish operations managers with the expertise necessary to effectively tackle unforeseen operational hurdles, ultimately aiming to improve network efficiency and resilience. The findings of this research encompass refined decision-making procedures, enhanced operational efficacy, and a solid plan for setting tactical planning choices into action at the operational level.

Inland waterway transport presents a sustainable alternative to road transport, offering the potential to alleviate congestion and reduce costs. However, increasingly frequent and severe drought seasons, attributed to climate change, challenge the resilience of this mode of transport. Decreased water levels restrict navigation, impact vessel size, and reduce vessel capacity.
This study introduces a comprehensive modeling framework for tactical planning in consolidation-based freight transportation on inland waterways. By examining the relationship between water levels and vessel load capacity, with a specific focus on vessel dimensions, the framework aims to evaluate possible impacts on the efficiency of inland waterway transport.
We also introduce three Stochastic Scheduled Service Network Design with Resource and Revenue Management models to tackle water-level uncertainties, each addressing a distinct tactical plan adjustment strategy. These models aim to establish a tactical plan, given predicted water levels, that maximizes the expected carrier’s revenue while accounting for future adjustments to the plan when information is revealed and predictions are reliably updated, to fulfill the demands of shippers and optimize the utilization of the carrier’s resources.
Through extensive experimentation using commercial software and a novel decision-based scenario clustering algorithm, we assess the quality of the solutions obtained with the different model variants and analyze the impact of the stochastic parameters on the results.

A two-sided digital platform for logistics service providers is defined as an integrated multi-stakeholder freight transportation system where the demands of many shippers (e.g., retailers, distributors, and manufacturers) are matched to the capacity offers from many carriers (e.g., freight carriers, terminal managers, and other logistics service providers) using a centralized intelligent decision-support entity. From a system design perspective, this platform has a Many-to-One-to-Many structure, which we refer to as an M1M system.
The intelligent platform efficiently matches the demand and supply sides, ensuring timely satisfaction of demand while respecting operational restrictions and generating profit for the platform. We investigate the tactical planning required for M1M systems under uncertainty in demand volume and service capacity offers. A tactical plan involves selecting and matching requests and offers, and consolidating them in time and space to maximize the platform's profit.