Measuring capacity flexibility of a transportation system

Morlok, E K; Chang, David J.

doi:10.1016/j.tra.2004.03.001

Cited by 92 publications

(75 citation statements)

References 7 publications

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“…This is because this practice contributes to a reduction in the transportation lead-time and consequently an increase in the customer satisfaction. Moreover, it is critical that the transportation system has the ability to accommodate also changing demands and traffic patterns allowing flexibility in traffic patterns, which reduces the amount of excess capacity needed at any point in the system to accommodate increased demand (Morlok and Chang (2004) and contributes to reduce the CO 2 emissions through more efficient transportation (Barany et al 2011). This implies that the ability to shift travel routes, times, modes and destinations, through transportation demand management, can increase capacity to deal with sudden increases in peak demand, becoming supply chain more resilient, lean and green.…”

Section: Discussionmentioning

confidence: 99%

Lean, green and resilient practices influence on supply chain performance: interpretive structural modeling approach

Govindan

Azevedo

Carvalho

et al. 2013

Int. J. Environ. Sci. Technol.

250

174

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Nowadays, companies are struggling to find an appropriate supply chain strategy to achieve competitiveness. Among the available strategies lean, green and resilient are considered as a new management strategies for the supply chain management to achieve competitiveness. The major issues with theses strategies are the integration and identification of critical issues related to the strategies. This paper aims to identify the critical lean, green and resilient practices on which top management should focus in order to improve the performance of automotive supply chains. The systematic analysis of the lean, green and resilient practices is expected to be of great value for their effective implementation by the automotive companies. The interpretive structural modeling approach is used as a useful methodology to identify inter-relationships among lean, green and resilient practices and supply chain performance and to classify them according to their driving or dependence power. According to this research, the practices with the main driving power are just-in-time (lean practice), flexible transportation (resilient practice) and environmentally friendly packaging (green practice). Customer satisfaction is the performance measure with strong dependence and weak driving power; that is, it is strongly influenced by the other researched variables but does not affect them.

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Section: Discussionmentioning

confidence: 99%

Lean, green and resilient practices influence on supply chain performance: interpretive structural modeling approach

Govindan

Azevedo

Carvalho

et al. 2013

Int. J. Environ. Sci. Technol.

250

174

View full text Add to dashboard Cite

show abstract

“…Recognizing that spatial considerations and network topology play an important role in transportation planning, freight logistics/supply chain management, and in trip generation modeling is not new (Wisetjindawat et al 2006;Scott et al 2006;Zhang et al 2005;Morlok and Chang 2004;Chen et al 2002;Bell 2000;Feitelson and Salomon 2000;Bell and Iida 1997). While these studies consider spatial dimensions of network topology, many do not explicitly consider the impact of spatial variables in modeling.…”

Section: Literaturementioning

confidence: 99%

Nationwide Freight Generation Models: A Spatial Regression Approach

et al. 2008

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“…Thus, to measure the ability of transportation networks that can deal with the variation of travel demand, Chen and Kasikitwiwat [16] discussed the concept of the network capacity flexibility using three typical network capacity models. The network capacity flexibility is defined as the ability of a transport system to accommodate changes in traffic demand while maintaining a satisfactory level of performance [16,17]. In this study, integrated with the uncertainties from the existing demand in transportation networks, the network capacity flexibility is further illustrated in Figure 1.…”

Section: Network Spare Capacity and Its Flexibilitymentioning

confidence: 99%

Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

Jiang

Cheng

et al. 2017

Journal of Advanced Transportation

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As more and more cities in worldwide are facing the problems of traffic jam, governments have been concerned about how to design transportation networks with adequate capacity to accommodate travel demands. To evaluate the capacity of a transportation system, the prescribed origin and destination (O-D) matrix for existing travel demand has been noticed to have a significant effect on the results of network capacity models. However, the exact data of the existing O-D demand are usually hard to be obtained in practice. Considering the fluctuation of the real travel demand in transportation networks, the existing travel demand is represented as uncertain parameters which are defined within a bounded set. Thus, a robust reserve network capacity (RRNC) model using min-max optimization is formulated based on the demand uncertainty. An effective heuristic approach utilizing cutting plane method and sensitivity analysis is proposed for the solution of the RRNC problem. Computational experiments and simulations are implemented to demonstrate the validity and performance of the proposed robust model. According to simulation experiments, it is showed that the link flow pattern from the robust solutions to network capacity problems can reveal the probability of high congestion for each link.

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Measuring capacity flexibility of a transportation system

Cited by 92 publications

References 7 publications

Lean, green and resilient practices influence on supply chain performance: interpretive structural modeling approach

Lean, green and resilient practices influence on supply chain performance: interpretive structural modeling approach

Nationwide Freight Generation Models: A Spatial Regression Approach

Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

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