This supplementary material provides supporting methodological and numerical information for the study of uncertainty-aware fleet planning for heavy-duty freight. It expands on the construction of the case study, including the truck technologies, route characteristics, freight classes, operating scenarios, and economic and environmental assumptions used in the stochastic optimization model.
The document also provides additional formulation details, data tables, computational settings, and supporting analyses needed to interpret and reproduce the results. Together, these materials complement the main manuscript by documenting assumptions and calculations that could not be included in the article because of space limitations.

Supporting Information document to submission for Foundations of Computer Aided Process Operations - Chemical Process Control Conference 2027

Recent research has introduced large language model (LLM)-based chatbots to help practitioners interpret solutions of optimization models. Existing chatbots primarily support the interpretation of a solution obtained from a single solve. In rolling horizon optimization, however, practitioners need to understand how and why solutions change across successive solves as the planning window advances and changes occur in model data and/or structure. We present an LLM-based agentic framework for this comparison task, works for time-indexed linear programming (LP) and mixed-integer linear programming (MILP) models represented in Pyomo. Four deterministic analytical modules detect model changes, quantify solution differences, assess whether prior decisions remain feasible under the updated model, and and links differences between successive plans to changes in model parameters and structure. LLM agents interpret user questions, select the corresponding module, and synthesize its numerical resu... [more]

This document presents the objective functions for the deterministic case study and the reformulated stochastic model, along with the tables and data used in the modeling process.