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The increase of intensity of the logistics sector and the impact of global warming are raising the need for more sustainable types of freight transportation. Therefore, more renewable energy sources and greener transportation modes are needed in the road transportation sector. Fiscal and regulatory policies are introduced to reduce the number of carbon emissions caused by road freight transportation. In this thesis, a theoretical model is established that examines several fiscal and regulatory policies in the market for the demand for different transportation modes, which consist of electric and diesel heavy trucks. These policies are analyzed along with their impacts on the economic surplus, such as the reduction of CO2 emissions, the promotion of electric heavy goods vehicles production, the effect on the consumers, and the impacts on the government income. Research on the impact of these policies, combined with a numerical analysis, shows the following results. The carbon tax is proven to be the most efficacious in reducing the amount of carbon emissions and has a positive impact on the number of electric heavy trucks in the market. Although the subsidy and renewables portfolio mandate increase the number of electric heavy trucks in the market, these policies achieve to reduce carbon emissions in a less efficient approach and have a more negative impact on the economic surplus. These outcomes can be used strategically for policymakers to implement the most effective incentives in the road transportation sector.

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Under pressure from various stakeholders, logistics service providers (LSPs) continuously strive for more cheap, flexible, and environmentally friendly ways of transporting freight. One way to increase the economic and environmental performance of freight logistics is to shift freight volumes from road to rail transportation. However, train services typically have less flexibility due to longer shipping times, predefined schedules, and capacity that needs to be reserved in advance. For that reason, this thesis takes the perspective of an LSP and aims to determine the optimal frequency and capacity level for rail services on a hinterland corridor when road transportation is the only alternative. Since the frequency and capacity booking decision on rail services must be taken months in advance, the exact details of future orders are unknown. Therefore, a mixed-integer linear programming model is developed that considers probabilistic knowledge of future orders and determines the optimal capacity to be reserved on rail services. The numerical experiments reveal that an optimal train frequency and corresponding capacity level exists that minimizes overall costs for an LSP. Next, sensitivity analyses on carbon taxes and overdue penalty costs are performed. These analyses demonstrate that carbon taxes stimulate the use of greener trains and that overdue penalties increase the LSP’s service level. Furthermore, the analysis concludes that the decision concerning the optimal train frequency is mainly a trade-off between providing high service quality and endeavouring low environmental damage. As a result, this study concludes that increasing the scale of the LSP or i.e. bundling more orders is the only way to ameliorate the service level, environmental performance, and economic performance at the same time.

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This thesis develops an inventory model to manage multiple perishable products, each with a fixed maximum shelf life, in the context of meal-kit services. Using a scenario-based approach on a stochastic demand to capture uncertainty, the proposed mixed integer linear programming (MILP) model determines the optimal order quantities for ingredients in the different meal-kits. This occurs periodically over a finite horizon. To satisfy the demand, using a First-In, First-Out (FIFO) depletion policy, so-called last-minute orders can be ordered at a higher cost in case there are shortages once the actual demand is revealed. The meal-kit service faces a typical tradeoff: it will either have significant waste levels or high last-minute order costs. The functioning of the MILP is tested by implementing the model in a Julia script with the Gurobi solver, using test data to analyze the trends obtained by perturbations of different parameters. The model shows that offering more recipes per period does not necessarily result in higher costs. It is also advisable for the meal-kit service to obtain the best demand forecasts as possible. However, the results indicate that there is no need to have an extremely large number of scenarios, causing high computational costs, to capture the demand uncertainty more accurately since the effect only improves up to a certain level. Lastly, an additional extension to the model incorporates demand forecasts of multiple periods (instead of only one period) in the optimization model.

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Environmental concerns raise the need for more efficiency and sustainability in the freight transportation sector. For this purpose, the Physical Internet is introduced, which aims to connect logistics networks into one hyperconnected supernetwork. To transport freight over such an integrated network, the innovative concept of synchromodality is presented. Synchromodality is defined by the usage of multiple modalities when planning shipments, where real-time switching between transportation modes is possible. In this thesis, a synchromodal planning model is developed that constructs optimal transportation routes in a multimodal network with stochastic transit times, formulated as a mixed-integer linear programming problem. To cope with this transit time stochasticity, transportation routes are adapted in accordance to real-time information about the transit time outcomes. Hence, the model determines the optimal decision to be taken in a terminal depending on the realized transit time. A numerical study demonstrates the potential advantages of real-time planning adaptation in terms of costs, service quality and environmental impact.

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Global supply chains face mounting pressure to transition to sustainable transport modes in order to address the challenges posed by climate change. In response to this concern, synchromodal transport is emerging as a solution to enhance the economic and environmental efficiency of logistics networks. Synchromodality refers to the dynamic and adaptive selection of transport modes based on real-time information about the supply chain conditions. At the basis of synchromodality lies the concept of the Physical Internet, which refers to the digital and physical interconnectivity of logistics networks into one integrated supply system. This thesis contributes to the field by developing an inventory replenishment heuristic tailored to synchromodal transport systems. The heuristic is designed to effectively integrate a sustainable and cost-efficient, yet inflexible, transport mode with a flexible transport mode that has a higher economic and environmental cost. The model considers a scenario where replenishment orders are initially transported by rail transport and can be (partially) expedited through transshipment to road transport. The heuristic has a nested order-up-to structure and the parameters are optimized by means of exhaustive search simulation, with the objective of optimizing the Total Logistics Costs (TLC). A numerical analysis demonstrates promising performance in terms of TLC and emissions compared to unimodal, intermodal and dual sourcing policies.