Optimization of Gas Production Systems Using Fuzzy Nonlinear Programming and Co-evolutionary Genetic Algorithm

Park, H.-J.; Lim, Jong‐Se; Kang, Jungmin

doi:10.1080/15567030600817852

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…e CEAs have been widely applied in various fields [53][54][55][56][57][58][59][60][61] Speed section Departure time part CEAs using population, individual, algorithm, operation, parameter, and strategy cooperation. A population CEA was employed in this study.…”

Section: Cea For Joint Departure Time and Speed Scheduling Of Multiple Linesmentioning

confidence: 99%

Dynamic Bus Scheduling of Multiple Routes Based on Joint Optimization of Departure Time and Speed

Liu

Luo

Cheng

et al. 2021

Discrete Dynamics in Nature and Society

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Dynamic bus scheduling is a rational solution to the urban traffic congestion problem. Most previous studies have considered a single bus line, and research on multiple bus lines remains limited. Departure schedules have been typically planned by making separate decisions regarding departure times. In this study, a joint optimization model of the bus departure time and speed scheduling is constructed for multiple routes, and a coevolutionary algorithm (CEA) is developed with the objective function of minimizing the total waiting time of passengers. Six bus lines are selected in Shenyang, with several transfer stations between them, as a typical case. Experiments are then conducted for high-, medium-, and low-intensity case of smooth, increasing and decreasing passenger flow. The results indicate that combining the scheduling departure time and speed produces better performances than when using only scheduling departure time. The total passengers waiting time of the genetic algorithm (GA) group was reduced by approximately 25%–30% when compared to the fixed speed group. The total passengers waiting time of the CEA group can be reduced by approximately 17%–24% when compared to that in the GA group, which also holds true for a multisegment convex passenger flow. The feasibility and efficiency of the constructed algorithm were demonstrated experimentally.

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Section: Cea For Joint Departure Time and Speed Scheduling Of Multiple Linesmentioning

confidence: 99%

Dynamic Bus Scheduling of Multiple Routes Based on Joint Optimization of Departure Time and Speed

Liu

Luo

Cheng

et al. 2021

Discrete Dynamics in Nature and Society

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“…In the second phase, QCD transforms the configuration scheme for GQPs into a configuration scheme for LQPs and a configuration scheme for CPs. Methods that include fuzzy least absolute regression (FLAR) [13] and fuzzy nonlinear programming [14] are incorporated in the HoQ of classical QFD so that the optimal configuration schemes for GQPs, LQPs, and CPs are generated in the topdown manner. VQD-QCD can facilitate transboundary service designers to precisely design quality/capability characteristics of innovative services.…”

Section: Introductionmentioning

confidence: 99%

A QFD-Based Quality and Capability Design Method for Transboundary Services

Liu

et al. 2020

Mathematical Problems in Engineering

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The “transboundary”, an emerging phenomenon in the Internet service ecosystem, is leading to the flourishing of innovative services. A transboundary service incorporates services, resources, and technologies from multiple domains into its business to create a particular competitive advantage and unique user experiences. It is difficult to comprehensively consider all the constraints from multiple domains to precisely design the nonfunctional characteristics of transboundary services, such as quality attributes and capability attributes. We propose a two-phase quality design method for transboundary services called value quality deployment-quality capability deployment (VQD-QCD) based on quality function deployment (QFD). Given the restrictions of transboundary services, VQD-QCD translates the value expectations of multiple stakeholders into an optimal configuration for global quality parameters (GQPs), local quality parameters, and capability parameters. Details of VQD are illustrated. Considering the inherent vagueness and uncertainty of relationships between value expectations and GQPs, and among GQPs, fuzzy least absolute regression and fuzzy nonlinear programming methods are incorporated into QFD to identify the quantitative relations between value indicators and GQPs, and among GQPs, and obtain an optimal configuration scheme for GQPs. Usability of the proposed method is validated through a case study on the “DiDi mobile transportation service”, which is a representative transboundary service in China. Compared with the current method, which is inaccurate and inefficient because its translation between value expectations and relevant quality and capability parameters is artificial and subjective, the proposed method integrates fuzzy least absolute regression and fuzzy nonlinear programming methods into QFD, which facilitate transboundary service designers to precisely and efficiently design the quality and capability characteristics of innovative services in the manner of semiautomatisation, which promotes the innovative design of transboundary services.

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