Grant: Discovery Grants Program - Individual

Trucking is a significant contributor to North American freight transportation and economy. In Canada, in particular, 90% of all consumer products and food are transported via trucks. Most firms in this sector are for-hire motor carriers and exhibit a wide range of sizes. Small carriers tend to focus on the freight spot market as it is difficult for them to access contract shipments. However, the market's uncertainty makes their decision-making process more complex; hence, freight market information serves as a valuable asset to them. Advance load information is one type of valuable information in which the knowledge window (KW) is short, and the information is usually partial and imperfect due to load cancellations and rescheduling. On the other hand, spot market demand forecasts are also valuable as they offer information beyond the limited KW; however, they are rife with uncertainties. Despite their shortcomings, advance load information and spot market demand forecasts still have potential benefits for smaller carriers and are worth investigating. Larger carriers, on the other hand, prioritize securing long-term contracts for more consistent freight. While these contracts contribute to greater demand predictability, operational uncertainties still exist for such carriers - mainly in the form of expectations related to the provision of advance notices by clients about upcoming shipping services. The proposed program has the following research objectives: (1) it assesses the impact of partial and imperfect advance information within the KW, and (2) evaluates the benefits of spot market demand forecasts beyond the KW for small carriers, as well as (3) explores the benefits of client demand information in contracts with advance notice. We employ varying methodologies from different disciplines, including management science, data analytics, and economics. For the first objective, we use optimization techniques under uncertainty to capture partial and imperfect advance information in the spot market. For the second objective, we examine influential parameters through the design of experiments and spot market demand forecasting by utilizing machine learning and deep learning techniques. For the third objective, we apply game theory approaches. In addition, simulation studies are conducted to evaluate the benefits of different information types for each research objective. While the research program provides training opportunities for 10 students, it also offers (a) theoretical contributions by adopting and applying a wide range of methodologies to handle real-world trucking issues, (b) practical implications by offering managerial insights into the true potential of information and how trucking companies can use it to enhance their competitiveness, as well as (c) direct and indirect social impacts, including quicker deliveries for consumers and enhanced environmental sustainability, achieved by reducing fuel consumption in freight transport.