A system dynamics approach for regional environmental planning and management: A study for the Lake Erhai Basin

Guo, Huaqing; Liu, L.; Huang, Guohe; Fuller, G.A.; Zou, Rui; Yin, Yongyuan

doi:10.1006/jema.2000.0400

Cited by 251 publications

(125 citation statements)

References 15 publications

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“…Improved understanding of the real system X X X Improved communication within and between organizations in the supply chain X X ABDS systems also make it possible to conduct different experiments and what-if analyses. This is similar to earlier findings reported in the literature (Nilsson and Darley, 2006;Legato and Mazza, 2001;Guo et al 2001;Vlachos 2007;Julka et al, 2002;Gao et al, 2009;van Dam et al, 2009;Hilletofth et al, 2010a). For instance, the decision maker in the Alpha case could investigate how the desired stock levels would impact supply chain performance.…”

Section: Discussionsupporting

confidence: 86%

Agent based decision support in the supply chain context

Hilletofth

Lättilä

2012

Industrial Management &Amp; Data Systems

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Purpose: The purpose of this paper is to investigate the benefits and the barriers of agent based decision support (ABDS) systems in the supply chain context. Design/methodology/approach: Two ABDS systems have been developed and evaluated. The first system concerns a manufacturing supply chain while the second concerns a service supply chain. The systems are based on actual case companies. Findings:This research shows that the benefits of ABDS systems in the supply chain context include the possibility to increase versatility of system architecture, to improve supply chain visibility, to conduct experiments and what-if analyses, to improve the understanding of the real system, and the possibility to improve communication within and between organizations in the supply chain. The barriers of ABDS systems in the supply chain context include the difficulty to access data from partners in the supply chain, the difficulty to access data on a higher level of granularity, and the difficulty to retrieve data from other information systems. Research limitations/implications:The research is explorative in nature therefore empirical data from similar and other research settings should be gathered to reinforce the validity of the findings. Practical implications:This research provides knowledge and insights on how ABDS systems may be developed and used in the supply chain context and demonstrates its main benefits and barriers. Originality/value:This research expands the current research of benefits of ABDS systems to the supply chain domain and also addresses the barriers of ABDS systems to a larger extent than previous research. Comparisons to other simulation based decision support systems are also given.

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Section: Discussionsupporting

confidence: 86%

Agent based decision support in the supply chain context

Hilletofth

Lättilä

2012

Industrial Management &Amp; Data Systems

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“…However, reciprocal validation between any two multivariate analyses is still required to validate the results obtained by each approach [7]. Although the spatial and temporal variations of surface water quality in reservoirs or lakes have been extensively studied [17][18][19], how to identify the pollution sources for watershed systems still remains elusive due to the lack of pollution census data [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Patterns and Source Identification of Water Pollution in Lake Taihu (China)

Chen

Zhao

et al. 2016

Water

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Abstract:Various multivariate methods were used to analyze datasets of river water quality for 11 variables measured at 20 different sites surrounding Lake Taihu from 2006 to 2010 (13,200 observations), to determine temporal and spatial variations in river water quality and to identify potential pollution sources. Hierarchical cluster analysis (CA) grouped the 12 months into two periods (May to November, December to the next April) and the 20 sampling sites into two groups (A and B) based on similarities in river water quality characteristics. Discriminant analysis (DA) was important in data reduction because it used only three variables (water temperature, dissolved oxygen (DO) and five-day biochemical oxygen demand (BOD 5 )) to correctly assign about 94% of the cases and five variables (petroleum, volatile phenol, dissolved oxygen, ammonium nitrogen and total phosphorus) to correctly assign >88.6% of the cases. In addition, principal component analysis (PCA) identified four potential pollution sources for Clusters A and B: industrial source (chemical-related, petroleum-related or N-related), domestic source, combination of point and non-point sources and natural source. The Cluster A area received more industrial and domestic pollution-related agricultural runoff, whereas Cluster B was mainly influenced by the combination of point and non-point sources. The results imply that comprehensive analysis by using multiple methods could be more effective for facilitating effective management for the Lake Taihu Watershed in the future.

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“…However, dynamic model itself is not alien to planning researches. Previous studies related to dynamic model application in the planning field are largely concerned on the application of the model in the planning and management of the environment, and in evaluating the impacts of planning policies (Deal & Schunk, 2004;Guo et al, 2001;Han, Hayashi, Cao, & Imura, 2009;Simmonds, 1999). This is most likely due to the fact that dynamic model does not handle spatial analysis well (Guo et al, 2001;Han et al, 2009), especially when most planners seem to believe that spatial analysis is the main important analysis in planning context.…”

Section: Public Motorized Transportmentioning

confidence: 99%

Special Issue 4 : 2016

Conference¹

2016

PMJ

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A system dynamics approach for regional environmental planning and management: A study for the Lake Erhai Basin

Cited by 251 publications

References 15 publications

Agent based decision support in the supply chain context

Agent based decision support in the supply chain context

Spatio-Temporal Patterns and Source Identification of Water Pollution in Lake Taihu (China)

Special Issue 4 : 2016

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