Conference Agenda

Renewable liquid fuels produced from biomass, hydrogen, and carbon dioxide play an important role in reaching climate neutrality in the transportation sector. For large-scale deployment, production facilities and corresponding logistics have to be established. However, the implementation of such a large-scale renewable fuel production network requires acceptance by citizens. To gain insights into the structure of efficient and socially accepted renewable fuel production networks, we propose a bi-objective mixed-integer programming model. In addition to an economic objective function, we consider social acceptance as a second objective

function. We use results from a conjoint analysis study on the acceptance and preference of renewable fuel production networks, considering the regional topography, facility size, production pathway, and raw material transportation to model social acceptance. We find significant trade-offs between the economic and social acceptance objective. The most favorable solution from a social acceptance perspective is almost twice as expensive as the most efficient economical solution. However, it is possible to strongly increase acceptance at a moderate expense by carefully selecting sites with preferred regional topography.

We consider the problem setting of a less-than-truckload carrier serving stochastic customer requests. Each request must be answered dynamically by accepting or rejecting it immediately. On the next day, accepted requests are served in routes using a set of vehicles with limited load capacity and route duration. After the request acceptance phase and before the fulfillment, multiple carriers participate in a combinatorial auction to exchange requests. An auctioneer allocates the bundles of requests to carriers according to their bids in a cost-minimizing way and distributes the auction profits. This type of horizontal collaboration provides cost savings and contributes to reducing negative impacts of transportation. We describe the carrier’s optimization problem of maximizing profit as a Markov decision process that comprises the sequential decisions in all phases, i.e., request acceptance, request selection for the auction, bidding, and routing. For solving a version of the vehicle routing problem with pickups and deliveries, heuristic approaches are proposed that achieve efficient and balanced routes. We design overbooking policies for strategically accepting more requests bearing in mind the options provided by the auction. Computational results show that – by trading requests in an auction –

carriers can accept more requests than they could serve on their own. The carriers’ request acceptance decisions impact their individual profits and the overall collaboration savings. The largest benefits can be achieved with an overbooking policy that prescribes which requests should be accepted by all carriers, based on the locations of both the request and the carriers’ depots.

After a promising ramp up, free-floating car sharing is about to establish itself as a mainstream mobility option in many urban areas. This form of short-term car rental allows users to begin trips wherever they are offered an available car and end them at their most convenient position. Current implementations are not based on optimization; each user decides locally whether to place a short-term reservation among available cars. This paper evaluates the potential gains for a car sharing provider if, instead, a sophisticated optimization algorithm is applied to match demand and supply centrally. For this purpose, we formulate the car-request assignment problem, provide a heuristic solution approach, and show how to apply it in different booking processes. Specifically, we compare the status quo with different optimization-based matching approaches, where either the booking with all its details is instantaneously confirmed to the customer or only a service promise is accredited, but the final specification of the car is postponed. Furthermore, we differentiate whether incoming customer requests are collected for a short batching interval and then jointly optimized, or if each customer receives immediate feedback. In a computational study, based on generated and real-world data, these five different booking policies are benchmarked in a dynamic environment where new requests appear over time. The computational tests also evaluate the impact of no-shows, late car returns, and the application of relocators. The results reveal that, once customers are willing to accept an altered booking process, an optimization-based matching mechanism promises considerable improvement of services.

In this paper, we introduce a last-mile delivery concept that is well suited for urban

areas. By jointly optimizing the tour of a truck and a cargo bike, we ensure that each

vehicle is used optimally. Here, we assume that the bike is restocked by meeting

up with the truck so that no dedicated mini-hubs have to be constructed. We model

different objective functions and analyze the different variants in comparison to the

traveling salesperson problem as well as the capacitated vehicle routing problem.

In an experimental evaluation, we compare MIP formulations for different problem

variants and assess several heuristic approaches to solve large-scale instances. These

results show that we can outperform the truck-only delivery in terms of completion

time while reducing the distance driven by the truck.