The strategic and operational planning of routes, (delivery) tours and fleets is one crucial point in today’s logistics systems. Application areas are, among others, the delivery of parcels and goods, the milk-run problem, (intermodal) trip planning of passengers, the planning of the future transportation network, the fleet size and mix problem, operations in Mobility as a Service (MaaS), and novel logistics concepts. AIT’s main focus is on true intermodality and applications in synchromodal contexts. Emphasis is given on integrated planning, i.e. upstream and downstream planning tasks to be considered in the transport planning problem.
Route planning, vehicle routing, or fleet management build the core of many transport planning problems. AIT’s focus is to approach these problems under real-world constraints, i.e., conditions and scenarios as they occur in real-world applications. Solution approaches developed for the pure academic versions of these problems are not applicable since either additional constraints are neglected or the instance sizes are too large for the proposed solution methods. Therefore, AIT’s main approach is to apply the following rather generic four-step method, which proved to be suitable in a lot of research projects in the field of transport planning:
- first, understand the problem statement, i.e., where is the shoe pinching?
- second, model the problem using standard modelling techniques like linear programming or constraint programming
- third, numerically solve the model, e.g., by applying metaheuristic or hybrid solution approaches
- finally, evaluate the results of numerical experiments and assess the improvement potential
- Details on this approach are also explained in more depth here.
As at AIT the main focus lies on the real-world application, the core planning problems are enriched by additional constraints, which are typically stated by stakeholders. These comprise, among others, constraints on multiple time windows (e.g. opening hours of customers), capacity of delivery vehicles, synchronization between different delivery vehicles (e.g. rendezvous logistics), restrictions with respect to CO2 emissions or noise, and range limitations with respect to e-mobility. In addition, additional planning problems are considered in an integrated planning approach like bin packing (packing of loads in delivery vehicles), staff scheduling, time tabling, machine scheduling, terminal optimization, or the planning of upstream or downstream processes in supply chains.
A related and nonetheless important topic is the planning of intermodal routes. Intermodality refers in this context to the usage of two or more modes of transportation along one individual route. Although this is quite natural for our everyday lives, the planning algorithms are still rather preliminary with respect to the requirement of intermodality. The state-of-the-art approach in practical applications is to consider either a fixed selection of modes of transport (e.g. the proposed route is park&ride because the user pre-selected park&ride) or a limited number of possible transmission points (e.g., only the ten closest public transport stations are considered for entering the public transport). AIT’s approach is to be open-minded in the sense that the user pre-determines only the set of possible modes of transportation while all other decisions are made by the routing algorithm based on the chosen objective function (e.g. travel time). With this approach, it might happen that for one request a unimodal route is suggested (e.g. just bike) while for another request the most appropriate combination of public and individual transport modes is chosen (e.g. it is proposed to first take the bus, then use a public bike-sharing and then continue with another bus again). Important is, however, that neither the user nor assumptions of the software engineer limit the route planning process.
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"Synchronizing vans and cargo bikes in a city distribution network";
Central European Journal of Operations Research, Springer Berlin Heidelberg (2016), 25 (2); 32 S.
P. Matl, P. Nolz, U. Ritzinger, M. Ruthmair, F. Tricoire:
"Bi-objective orienteering for personal activity scheduling";
Computers & Operations Research, 82 (2017), S. 69 - 82.
T. Eiter, T. Krennwallner, M. Prandtstetter, C. Rudloff, P. Schneider, M. Straub:
"Semantically Enriched Multi-Modal Routing";
International Journal of Intelligent Transportation Systems Research, 14 (2014), 1; S. 20 - 35.
L. Kang, X. Zhu, H. Sun, J. Wu, Z. Gao, B. Hu:
"Last Train Timetabling Optimization and Bus Bridging Service Management in Urban Railway Transit Networks";
Omega, - (2018), S. 1 - 14.
P. Pop, C. Sabo, B. Biesinger, B. Hu, G. Raidl:
"Solving the Two-Stage Fixed-Charge Transportation Problem with a Hybrid Genetic Algorithm";
Carpathian Journal of Mathematics, 33 (3) (2017), S. 365 - 371.
"The Meaning and Importance of True Intermodal Route Planning in the Context of the Physical Internet";
Vortrag: IPIC 2018 - International Physical Internet Conference 2018, Groningen; 18.06.2018 - 22.06.2018; in: "Proceedings of the 5th International Physical Internet Conference IPIC 2018", (2018).
M. Prandtstetter, M. Straub, J. Puchinger:
"On the Way to a Multi-Modal Energy-Efficient Route";
in: "IEEE Industrial Electronics Society, IECON 2013-39th Annual Conference of the IEEE", IEEE (Hrg.); (2013), ISBN: 978-3-85403-298-4; S. 4779 - 4784.