Generation of logistics networks in freight transportation models

Liedtke, Gernot; Friedrich, Hanno

doi:10.1007/s11116-012-9386-9

Cited by 12 publications

(10 citation statements)

References 19 publications

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“…Latest advances in modelling freight transport mainly focus on capturing logistics aspects (Liedtke 2006;Friedrich 2010;Liedtke and Friedrich 2012;Schröder et al 2012;Davydenko 2015). Even in the cases in which the developed freight transport models explicitly consider business establishments and the relations between them, they are not applied for estimating the impact of changes in the population of establishments or their linkage, i.e.…”

Section: Motivationmentioning

confidence: 99%

“…In the context of freight transport modelling, this task is part of decision making on the level of logistics system (Roorda et al 2010;Liedtke and Friedrich 2012;Schröder et al 2012). In contrast, commodity flows are temporal aggregations of shipments between a combination of origin and destination location ignoring any logistics or infrastructure induced detours.…”

Section: Commodity Flowsmentioning

confidence: 99%

“…These meso structures are accountable for the micro-macro gap, which renders the direct translation of cargo flows into vehicle flows impossible due to detours induced for bundling reasons (Liedtke and Friedrich 2012). A more vivid description of logistics service provider's role within the transport system can be found in Schröder et al Often, the transport chains organised by logistics service providers apply a combination of pre-carriage, main carriage and onward carriage as sketched in figure 3.3.…”

Section: System Analysis Of Freight Transport Demandmentioning

confidence: 99%

“…de Jong, Gunn, Walker, and Widell 2002;Liedtke 2009;Roorda et al 2010), which become more and more applicable due to advances in modelling methods, computational performance, and data availability. In this regard, meso-structures emerging from economic behaviour of (decentrally organised) actors have gained attention, especially for the case of logistics structures (Liedtke and Friedrich 2012;Schröder et al 2012;Tavasszy, Ruijrok, et al 2012). Nevertheless, the scope of these disaggregate approaches remains limited to effects that occur on the higher levels of the transport system, leaving out dynamics within the overall economic structure and spatial pattern of production and consumption.…”

Section: Conclusion and Findings From The Area Of Freight Transportmentioning

confidence: 99%

“…For certain paths of decomposition a direct disaggregation from the macro to the micro level is not possible due to the bundling and unbundling of potentially independent movements during their travel on the infrastructure (e.g. the micro-macro-gap in freight transport as described in Liedtke and Friedrich 2012). …”

mentioning

confidence: 99%

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Modelling change in supply-chain-structures and its effect on freight transport demand

Ottemöller

Friedrich

2019

Transportation Research Part E: Logistics and Transportation Re

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Economic activity, especially in the manufacturing industries, is a major determinant of freight transport. Since the actual manufacturing processes are bound to specialised business establishments, which are only partially dispersed across space, commodity flows are required that connect locations of excess supply with locations that show unfulfilled demand for goods. In this context, economic reasoning of the involved actors leads to the formation of industry specific supply chain structures. Various authors emphasise the interrelation of these continuously evolving supply chain structures and freight transport demand. However, so far no disaggregate model exists that explicitly captures this interdependence.The study at hand addresses this gap by developing a disaggregate model for simulating the impact of change in supply chain structures on the corresponding freight transport demand. The proposed model covers centralisation and vertical disintegration as examples of structural change. For this purpose, the model quantitatively describes spatially disaggregate supply chain structures, consisting of business establishments and commodity flows, on the level of entire sectors. The model development is accompanied by an interdisciplinary literature review that gives an overview of existing research on supply chain structures and freight transport demand.The developed model consists of two phases. A first phase generates an artificial industry landscape of business establishments and commodity flows according to available aggregate statistics. The generation relies on elements of stochastic simulation and directed choice procedures. The model's second phase simulates change in the supply chain structures from the first phase. Using linear programming, a maximum solution range regarding the impact on freight transport is calculated. Increasing the degree of assumptions, the solution space can be narrowed. Here, the model applies a combination of stochastic simulation, linear programming, and fitting procedures.The model is applied for analysing centralisation in the poultry industry and vertical disintegration in the automotive industry of Germany. For both cases, a broad range of data sources is used, e.g. common public statistics on establishment sizes and spatial distribution of employment but also sectoral data, e.g. from industry associations or case studies. The real-world consistency of spatial flow patterns is ensured by assigning commodity flows according to statistical macroscopic flows.Overall, the simulation results show that an increase in freight transport performance is to be expected for the case of centralisation as well as vertical disintegration. However, the maximum solution ranges also indicate that assuming suitable location choice and flow assignment a reduction in freight transport performance is mathematically possible. The analysis also addresses the suitability of state measures for mitigating the impact of changes in the supply chain structure on freight transport demand.In summ...

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Section: Motivationmentioning

confidence: 99%

Section: Commodity Flowsmentioning

confidence: 99%

Section: System Analysis Of Freight Transport Demandmentioning

confidence: 99%