A bi-objective model for uncertain multi-modal shortest path problems

Zhang, Yan; Liu, Pei; Yang, Lixing; Gao, Yuan

doi:10.1186/s40467-015-0032-x

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…In the area of urban passenger mobility, some studies investigate the multimodal transportation planning problem. However, these studies either did not take the transportation efficiency into consideration and formulated the multimodal transportation routing problem as one without time constraints (Zhang et al., 2006; Wang and Han, 2010), or did not include ridesharing and consider the combination of fixed routes (Ambrosino and Sciomachen, 2014; Sun and Lang, 2015; Zhang et al., 2015). In our study, we integrate ride‐sharing with the public transportation system by considering time constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal routing of multimodal mobility systems with ride‐sharing

Miao

Bayram

et al. 2020

Int Trans Operational Res

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Multimodal transportation systems are a combination of more environmentally friendly shared transport modes including public transport, ride-sharing, shuttle-sharing, or even completely carbon-free modes such as cycling to better meet customer needs. Multimodal mobility solutions are expected to contribute in mitigating traffic congestion and carbon emissions, and to result in savings in costs. They are also expected to improve access to transportation, more specifically for those in rural or low-populated communities (i.e., difficult to serve by public transportation only). Motivated by its benefits, in this study, we consider the combination of the ride-sharing and public transportation services and formulate a mixed integer programming model for the multimodal transportation planning problem. We propose a heuristic approach (i.e., anglebased clustering [AC] algorithm) and compare its efficiency with the exact solution for different settings. We find that the AC algorithm works well in both small and large settings. We further show that the multimodal transportation system with ride-sharing can yield significant benefits on travel distances and travel times.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal routing of multimodal mobility systems with ride‐sharing

Miao

Bayram

et al. 2020

Int Trans Operational Res

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“…Uncertain programming [29] has been adopted and improved to solving problems like network optimization in [30] and shortest path problem in [31] and so on. Recently, uncertain multi-objective programming and uncertain goal programming are generated by Liu and Chen [32] to apply in more research works.…”

Section: Uncertain Programming Using Evmmentioning

confidence: 99%

Determination of Target Values of Engineering Characteristics in QFD Using Uncertain Programming

Miao

Liu

Chen

2015

J. Uncertain. Anal. Appl.

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Quality function deployment (QFD) is a new product development tool remarked with interpreting customer requirements into engineering characteristics of the design process. On account of the inherent imprecise and uncertain elements in the weights of customer requirements, the relationships between customer requirements and engineering characteristics, and the correlations among engineering characteristics, uncertain variables are preferred to be applied in this paper. By taking advantage of expected value modelling to determine the target values of engineering characteristics in handling different practical design scenarios, two uncertain programming models are proposed for optimizing the QFD process in an uncertain environment. Subsequently, the proposed uncertain models are implemented in a motor car design for quality development.

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“…This means that it cannot depend on a single objective and should be considered as a multi-objective optimization problem [4,13,15,31]. In general, there were two conventional methods for addressing multi-objective optimization problems, which are the -constraint method and the weighted sum method [6,20,39].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

The route problem of multimodal transportation with timetable under uncertainty: multi-objective robust optimization model and heuristic approach

2021

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The uncertainty of transportation duration between nodes is an inherent characteristic and should be concerned in the routing optimization of the multimodal transportation network to guarantee the reliability of delivery time. The interval number is used to deal with the uncertainty of transportation duration, and the multi-objective robust optimization model is established which covers the transportation duration and the cost. To solve the combinatorial optimization problem of this study, Non-Dominated Sorting Genetic Algorithm-II (NSGA- II ) is designed, which integrates the (μ+λ) selection method elite retention and the external filing elite retention. Our findings verify the efficiency of the proposed approach by analyzing the diversity, distribution and convergence of the frontier solutions. Finally, near-optimal solutions are obtained with the proposed algorithm in the numerical example. The present study can provide decision reference for multimodal transportation carriers in making transportation plan under uncertainty.

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A bi-objective model for uncertain multi-modal shortest path problems

Cited by 11 publications

References 37 publications

Optimal routing of multimodal mobility systems with ride‐sharing

Optimal routing of multimodal mobility systems with ride‐sharing

Determination of Target Values of Engineering Characteristics in QFD Using Uncertain Programming

The route problem of multimodal transportation with timetable under uncertainty: multi-objective robust optimization model and heuristic approach

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