Tactical planning on freight transport networks: service design and pricing

4OR ◽  
2019 ◽  
Vol 17 (4) ◽  
pp. 447-448
Author(s):  
Christine Tawfik
2009 ◽  
Vol 43 (2) ◽  
pp. 129-143 ◽  
Author(s):  
Tadashi Yamada ◽  
Bona Frazila Russ ◽  
Jun Castro ◽  
Eiichi Taniguchi

2021 ◽  
Vol 207 ◽  
pp. 107315
Author(s):  
Zhidong He ◽  
Kumar Navneet ◽  
Wirdmer van Dam ◽  
Piet Van Mieghem

Transport ◽  
2016 ◽  
Vol 33 (1) ◽  
pp. 280-290 ◽  
Author(s):  
Ondrej Stopka ◽  
Rudolf Kampf

The main advantages of maritime transport are (1) lowest costs, (2) large-scale carriage capacity, (3) carriage of different goods over long distances and (4) the most acceptable mode of transport in the context of the environment. This mode of transport is considered more profitable and more cost-effective than all other transport modes. Modern maritime ports have become the essential nodal components of freight transport networks. This paper is focused on determining the most suitable layout of space for the loading units warehousing and handling in the maritime port using the particular method. In the paper, four types of layout and five criteria were taken into account. Layout of warehousing and handling space can affect the entire transport process and can have a great effect on the economics of enterprises.


2003 ◽  
Vol 94 (4) ◽  
pp. 424-438 ◽  
Author(s):  
Isabelle Thomas ◽  
Jean-Pierre Hermia ◽  
Thierry Vanelslander ◽  
Ann Verhetsel

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Ahmed Karam ◽  
Kristian Hegner Reinau ◽  
Christian Richter Østergaard

AbstractIn the freight transport sector, competing companies horizontally collaborate through establishing Collaborative Transport Networks (CTNs). Fruitful implementation of CTNs will leverage environmental and socio-economic goals of sustainable development in the freight transport sector. The benefits of CTNs in horizontal collaborative settings have been widely demonstrated through several modelling approaches. However, in practice, the real applications of CTNs have been challenging and most did not achieve satisfactory performances. Some studies have addressed this issue by identifying different barriers to CTN implementation. However, a conceptual framework for the barriers is not well-established. In addition, the literature lacks a decision-making framework for the CTN implementation which considers the different barriers. To address this gap, this paper conducted a literature review of the barriers to CTN implementation. In total, 31 different barriers were identified. A conceptual barrier framework is developed by grouping the 31 barriers into five categories: the business model, information sharing, the human factors, the Collaborative Decision Support Systems (CDSSs), and the market. The paper additionally proposes a stage-gate model integrating the conceptual barrier framework into the CTN implementation decision-making process. The current work contributes to the existing literature by developing both theoretical and practical understandings of the barriers to implementing CTNs and will support decision makers in CTN implementation to maximize the CTN benefits and minimize the risk of CTN failure.


Author(s):  
Zhujun Li ◽  
Amer Shalaby ◽  
Matthew J. Roorda ◽  
Baohua Mao

2020 ◽  
Vol 62 (5) ◽  
pp. 403-416
Author(s):  
Andreas Balster ◽  
Ole Hansen ◽  
Hanno Friedrich ◽  
André Ludwig

Abstract Transparency in transport processes is becoming increasingly important for transport companies to improve internal processes and to be able to compete for customers. One important element to increase transparency is reliable, up-to-date and accurate arrival time prediction, commonly referred to as estimated time of arrival (ETA). ETAs are not easy to determine, especially for intermodal freight transports, in which freight is transported in an intermodal container, using multiple modes of transportation. This computational study describes the structure of an ETA prediction model for intermodal freight transport networks (IFTN), in which schedule-based and non-schedule-based transports are combined, based on machine learning (ML). For each leg of the intermodal freight transport, an individual ML prediction model is developed and trained using the corresponding historical transport data and external data. The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail. Consequently, the outcome of this research allows decision makers to proactively communicate disruption effects to actors along the intermodal transportation chain. These actors can then initiate measures to counteract potential critical delays at subsequent stages of transport. This approach leads to increased process efficiency for all actors in the realization of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs.


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