Prof. Dr. Arne Heinold

Abstract

Intermodal rail/road transportation combines advantages of both modes of transport and is often seen as an effective approach for reducing the environmental impact of freight transportation. This is because it is often expected that rail transportation emits less greenhouse gases than road transportation. However, the actual emissions of both modes of transport depend on various factors like vehicle type, traction type, fuel emission factors, payload utilization, slope profile or traffic conditions. Still, comprehensive experimental results for estimating emission rates from heavy and voluminous goods in large-scale transportation systems are hardly available so far. This study describes an intermodal rail/road network model that covers the majority of European countries. Using this network model, we estimate emission rates with a mesoscopic model within and between the considered countries by conducting a large-scale simulation of road-only transports and intermodal transports. We show that there are high variations of emission rates for both road-only transportation and intermodal rail/road transportation over the different transport relations in Europe. We found that intermodal routing is more eco-friendly than road-only routing for more than 90% of the simulated shipments. Again, this value varies strongly among country pairs.

Abstract

Eco-labels are a way to benchmark transportation shipments with respect to their environmental impact. In contrast to an eco-labeling of consumer products, emissions in transportation depend on several operational factors like the mode of transportation (e.g., train or truck) or a vehicle’s current and potential future capacity utilization when new orders are added for consolidation. Thus, satisfying eco-labels and doing this cost efficiently is a challenging task when dynamically routing orders in an intermodal network. In this paper, we model the problem as a multiobjective sequential decision process and propose a reinforcement learning method: value function approximation (VFA). VFAs frequently simulate trajectories of the problem and store observed values (violated eco-labels and costs) for states aggregated to a set of features. The observations are used for improved decision making in the next trajectory. For our problem, we face two additional challenges when applying a VFA, the multiple objectives and the “delayed” realization of eco-label satisfaction due to future consolidation. For the first, we propose different feature sets dependent on the objective function’s focus: costs or eco-labels. For the latter, we propose enhancing the suboptimal decision making and observed pessimistic primal values within the VFA trajectories with optimistic dual decision making when all information of a trajectory is known ex post. This enhancement is a general methodological contribution to the literature of approximate dynamic programming and will likely improve learning for other problems as well. We show the advantages of both components in a comprehensive study for intermodal transport via trains and trucks in Europe.

Abstract

Sustainability is a common concern in intermodal transport. Collaboration among carriers may help in reducing emissions. In this context, this work establishes a collaborative planning model for intermodal transport and uses eco-labels (a series of different levels of emission ranges) to reflect shippers’ sustainability preferences. A mathematical model and an Adaptive Large Neighborhood Search heuristic are proposed for intermodal transport planning of carriers and fuzzy set theory is used to model the preferences towards eco-labels. For multiple carriers, centralized, auction-based collaborative, and non-collaborative planning approaches are proposed and compared. Real data from barge, train and truck carriers in the European Rhine-Alpine corridor is used for extensive experiments where both unimodal carrier collaboration and intermodal carrier collaboration are analyzed. Compared with non-collaborative planning without eco-labels, the number of served requests increases and emissions decrease significantly in the collaborative planning with eco-labels as transport capacity is better utilized.

Abstract

This paper provides an introductory tutorial on Value Function Approximation (VFA), a solution class from Approximate Dynamic Programming. VFA describes a heuristic way for solving sequential decision processes like a Markov Decision Process. Real-world problems in supply chain management (and beyond) containing dynamic and stochastic elements might be modeled as such processes, but large-scale instances are intractable to be solved to optimality by enumeration due to the curses of dimensionality. VFA can be a proper method for these cases and this tutorial is designed to ease its use in research, practice, and education. For this, the tutorial describes VFA in the context of stochastic and dynamic transportation and makes three main contributions. First, it gives a concise theoretical overview of VFA’s fundamental concepts, outlines a generic VFA algorithm, and briefly discusses advanced topics of VFA. Second, the VFA algorithm is applied to the taxicab problem that describes an easy-to-understand transportation planning task. Detailed step-by-step results are presented for a small-scale instance, allowing readers to gain an intuition about VFA’s main principles. Third, larger instances are solved by enhancing the basic VFA algorithm demonstrating its general capability to approach more complex problems. The experiments are done with artificial instances and the respective Python scripts are part of an electronic appendix. Overall, the tutorial provides the necessary knowledge to apply VFA to a wide range of stochastic and dynamic settings and addresses likewise researchers, lecturers, tutors, students, and practitioners.