Environmental criteria in industrial facility siting decisions: An analysis

Briassoulis, Helen

doi:10.1007/bf02471998

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…Building upon the work of Briassoulis (1995) in which the social implications of economic activities have not been developed, a more general model representing the interrelationship between economic activities, environment, and society is proposed (see Figure 3). The previously-mentioned differentiation between suitability and impact is utilized to build a more general model in which examples of negative and positive risks imposed by economic activities are identified.…”

Section: Discussionmentioning

confidence: 99%

“…Given that equity is one of the main themes of sustainability, the classification proposed in Table 1 might be appropriate for sustainable facility location. Briassoulis (1995) presented a number of alternative classification schemes for facility siting criteria. In addition, common characteristics of the criteria and the factors conditioning the use of the criteria have been identified.…”

Section: Sustainable Facility Locationmentioning

confidence: 99%

“…Reams and Templet (1988) found that political, ideological, social, demographic, physical, and economic factors are influential in communities' responses in the case of locating incinerators. Briassoulis (1995) emphasized the need to recognize the purpose of environmental decision making criteria. As a result, he proposed differentiating between suitability and impact in criteria, whereby, the former addresses how appropriate the local environment is for the facility, and the latter investigates how the local environment will be influenced by placing the facility (see Figure 2).…”

Section: Holistic and Lifecycle Approachmentioning

confidence: 99%

“…For instance, in the case of locating an industrial plant, the development and construction stages in addition to the plant operation have to be considered. Briassoulis (1995) presented several classification schemes for environmental criteria used in facility siting decisions. These indicators can also be utilized for the purpose of Environmental Impact Assessment (EIA).…”

Section: Life Cycle Consciousnessmentioning

confidence: 99%

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Towards Sustainable Facility Location – A Literature Review

Terouhid¹,

Ries²,

Fard³

2012

JSD

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Facility location methods play a crucial role in specifying the optimum location options for various types of facilities. A question that arises is what makes a facility location decision a sustainable one? Facility location, also known as location analysis, is a known concept in the literature, but sustainable facility location is not. This requires appropriately defining the concept and framing the problem in order to address the relevant issues. Facility location models in the existing literature do not effectively include all the requirements of sustainable development. This paper serves as a discussion of the current literature concerning the sustainability aspects of the location problem. The aim is to conduct a comprehensive literature review to identify the characteristics of the sustainable facility location problem and propose a framework for classification of sustainability characteristics. The study shows that the location literature has steadily progressed toward considering not only economic but also social and environmental criteria in location decisions; but that many steps remain to be taken toward developing location models that integrate all three aspects of sustainability into decision making. The main motivation for the current study is to provide a foundation from which issues of sustainable development can be built into facility location and siting models.

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Section: Discussionmentioning

confidence: 99%

Section: Sustainable Facility Locationmentioning

confidence: 99%

Section: Holistic and Lifecycle Approachmentioning

confidence: 99%

Section: Life Cycle Consciousnessmentioning

confidence: 99%

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Towards Sustainable Facility Location – A Literature Review

Terouhid¹,

Ries²,

Fard³

2012

JSD

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“…The criteria selected must also meet social and environmental factors as prescribed by state laws and regulations. Based on the literature review conducted in this study, including theories, models, documents, indicators/criteria (Rikalovic, Ilija & Djordje, 2014;Ramaul, & Ramaul, 2016;Rachdawong & Apawootichai, 2003;Yasouri, 2013;Nasorllahi & Salehi, 2012;Masoumi et al, 2010;Faraji Sabokbar, 2016;Ashornezhad et al, 2016;Shiri, 2001;Soleimani & Zangane, 2005;Ahmadizadeh et al, 2012;Khoshmouz, 2010;Rezaei & Ranjbaran, 2009& Rachdawong & Apawootichai, 2003Briassoulis, 1995 and the aforementioned ones) as well as the result of a Delphi questionnaire, 18 indices were extracted that were divided into 6 main categories. Figure 1 shows the general classification of these indicators.…”

Section: Industrial Location Its Determinants and Criteriamentioning

confidence: 99%

An Intelligent Method for Industrial Location Selection: Application to Markazi Province, Iran

Aliverdilou

Hajilou

Sabokbar

et al. 2021

jrcp

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Decision-making and selection are important and sensitive aspects of planning. An important part of land-use planning is the location of human activities. Locating activities in the right places determines the future space of a region. Selection and definition of natural and human indices and criteria for location always face uncertainty. Thus, this study aimed to develop an intelligent method for industrial location. In this study a developmental-applied approach was used along with a descriptive-analytical method for data analysis. Through the review of related literature and a Delphi survey, 18 criteria were extracted and 6 main components were categorized. The data were analyzed and modeled by GIS, MATLAB software, and the Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods. For each modeling three industrial domains were extracted, i.e. weak, medium, and premium. A total of 42,968 hectares of premium industrial location with a score higher than 0.7 resulted from combining the produced maps. Other important findings were related to the architecture and methodology applied in the research based on computational intelligence and knowledge-based systems to analyze and understand the processes that influence the score of locations. The novelty of this method lies in the use of high computing power and information evaluation based on artificial intelligence (AI), making it possible to analyze and understand the processes influencing industrial location. Abstrak. Pengambilan keputusan dan seleksi adalah aspek-aspek penting dan sensitive dalam perencanaan. Bagian yang penting dalam sebuah perencaan penggunaan lahan adalah terkait lokasi kegiatan manusia. Alokasi kegiatan manusia pada tempat yang benar adalah penentu ruang masa depan dari suatu wilayah. Dalam hal seleksi dan definisi index, juga kriteria lokasi selalu menghadapi ketidakpastian. Sehingga, studi ini dilakukan untuk mengembangkan metode yang berguna dalam alokasi industri. Pada artikel ini, digunakan pendekatan terapan-terkembangkan dengan metode analisis deskriptif dalam hal analisis data. Berdasarkan tinjauan pada literatur terkait dan survey Delphi, 18 kritersia diekstraksi yang dikategorikan pada 6 komponen utama. Data dianalisis dan dimodelkan menggunakan GIS, MATLAB, Fuzzy Inference System (FIS), dan metode Adaptive Neuro-Fuzzy Inference System (ANFIS). Untuk setiap model, tiga domain industry ditentukan, yakni: lemah, moderat, dan premium. Terdapat lokasi industry premium dengan total 42,968 ha dengan nilai lebih dari 0.7. Hasil penting lainnya berkaitan dengan arsitektur dan metode terapan dalam penelitian yang berdasar kepada ilmu komputasi untuk memahami proses yang memengaruhi nilai untuk suatu lokasi. Kebaruan dari metode ini ada pada penggunaan model komputasi tinggi dan evaluasi informasi berdasarkan kecerdasan buatan (AI) yang memungkinkan untuk melakukan analisis dan memahami proses yang memengaruhi lokasi industri. Kata kunci. Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), lokasi industri, Provinsi Markazi.

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