A Comprehensive Method for Industrial Site Selection: The Macro-Location Analysis

Rikalović, Aleksandar; Ćosić, Ilija; Labati, Ruggero Donida; Piuri, Vincenzo

doi:10.1109/jsyst.2015.2444471

Cited by 21 publications

(22 citation statements)

References 17 publications

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“…The industrial location problem requires complex knowledge management and comprehensive analysis. A Comprehensive Method for Industrial Site Selection (CMISS) [15], based on an intelligent decision support system for industrial location criteria analysis, a geographic information system for generating location alternatives, and a spatial decision support system for evaluating location alternatives is a good example of combining various complex interacting decision support systems synergistically. Unfortunately, the approaches described above rarely adopt efficient data optimization strategies and fail to consider the uncertainty that is always present in the information acquired for the analysis of geographical sites.…”

Section: ) Industrial Site Selection Using Computational Intelligencmentioning

confidence: 99%

“…We designed the proposed ANFIS classifiers starting from an expert system that we implemented from a knowledge base created in collaboration with other experts in the field. Similar to [15], the expert system consists of an FIS that assigns weights to each industrial site. The main difference between this approach and [15] lies in the fact that the output value of the FIS is thresholded to obtain a two-class classifier in which each rank level represents a class.…”

Section: Classificationmentioning

confidence: 99%

“…A number of decision support tools have been used to support industrial site selection including geographic information systems (GIS) [9], multi-criteria decision analysis (MCDA) [10], fuzzy expert systems (FIS) [11], genetic algorithms (GA) [12], artificial neural networks (ANNs) [13] and various combinations of these approaches [14], [15]. Geographic information systems are mainly used to collect spatial (geographic) data and support their spatial analyses.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Decision Support System for Industrial Site Classification: A GIS-Based Hierarchical Neuro-Fuzzy Approach

Rikalović

Ćosić

Labati

et al. 2018

IEEE Systems Journal

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Industrial site selection involves a large number of criteria and location alternatives; consequently, the selection process leads to extended decision-making periods and requires complex knowledge management, classification and analysis skills. The selection criteria are generally described by a number of different features expressed as both quantitative and qualitative measures that can involve some uncertainty. Moreover, the goals considered in the selection process are frequently nonlinearly related to the criteria; thus, they give rise to an optimization problem that is nonlinear with respect to each goal. Consequently, decision making requires appropriate support to enable efficient data optimization and classification under uncertainty before the final selection of an industrial site is made. This paper presents a novel intelligent decision support system for classifying industrial sites according to quality criteria estimated by exploiting a geographic information system, expert knowledge and machine learning techniques. The proposed system is based on a geographic information system for generating location alternatives and a hierarchical neuro-fuzzy approach for site classification. The neuro-fuzzy method is based on a knowledge base designed by experts in the field and uses a neural approach to tune the parameters of the membership functions. Experimental results on real-world problems show that the proposed system provides accurate results for industrial site classification at the local level (micro locations).

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Section: ) Industrial Site Selection Using Computational Intelligencmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intelligent Decision Support System for Industrial Site Classification: A GIS-Based Hierarchical Neuro-Fuzzy Approach

Rikalović

Ćosić

Labati

et al. 2018

IEEE Systems Journal

Self Cite

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“…Selection of industrial sites is a strategic decision that involves several criteria: technical, economic, social, environmental and political. Deciding that the site requires appropriate instruments to enable data collection, storage, analysis, fusion and knowledge management to solve this complex problem (Rikalovic et al, 2015).…”

Section: Location Theorymentioning

confidence: 99%

Site selection of the construction of the system for the production of pasta in AP Vojvodina

Paunović¹,

Soleša²,

Ivanis³

2017

Ekon Polj

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In the growing race for the market share, companies geographically position their production and office buildings according to the new business rules. In order for the company to survive in the market, it is necessary to maximize the profit, minimize the costs, maintain a good quality of the product and constantly improve it. The choice of location is one of the vital decisions in the process of building of a production system, although the selection of the location is a problem related to certain time sections in the life of a given production system, the need for research on the effect locations have is stable in time. The aim of the research is the methodological approach to the selection criteria of broader and narrower location for the construction of the production system for pastry production in Vojvodina as an area which has a rich base in raw material. The Agricultural and food industry as branches of industry have a long time tradition as well as a great potential in strengthening economic performances of the Economy of Vojvodina. Evaluation criteria and their use in the model of dimensional analysis will allow a good selection of a good location for the construction of the business systems for the production of pastry.

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“…In recent studies, a macro-level-based approach for industrial site selection that uses GIS, fuzzy inference systems (FIS) and AHP has been employed. In these studies, FIS and AHP were used to establish the weights for the different criteria, and the final aggregation was done in MCDA4ArcMap (Rikalovic et al 2015). However, this system is useful only when the spatial analysis is performed on vector-based polygon databases.…”

Section: Introductionmentioning

confidence: 99%

GIS-based multi-criteria analysis modeling used to locate suitable sites for industries in suburban areas in Bangladesh to ensure the sustainability of agricultural lands

2017

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Land use changes significantly affect the sustainability of food security, environmental quality and the ecological balance in developing countries. Bangladesh is one such country, and it faces challenges caused by its limited resources of arable land. The urbanization of agricultural lands and urban developments in suburban areas add to these challenges. Therefore, the aim of this study is to characterize the land use changes that occur over time in suburban areas that have potential for industrial growth. This study also assesses potential locations for industrial development by using land suitability analysis (LSA) to emphasize both agricultural and industrial development in terms of sustainable growth. A geographical information system (GIS)-based multi-criteria analysis (MCA) model is developed for the LSA to distinguish compact parcels that are suitable for industrial economic zones. Nine criteria, including seven constraints and 23 factors, are evaluated using the spatial analysis tools in ArcGISÒ . An analytical hierarchy process (AHP) is applied to prioritize these criteria based on experts opinion for the decision-making process of LSA. This study finds that the construction of densely clustered industrial areas has decreased the area of agricultural land by more than 10% in the last two decades. Furthermore, the results of the LSA show that only 4% of the land was highly suitable for industrial sites. On the other hand, four compact parcels that contain 16-10 ha of land and suitable for small industrial zones exist within the study area. Thus, the integrated GIS-MCA model can serve as a policy and planning tool for locating industrial economic zones while preserving agricultural lands and retaining environmental protections.& Tofael Ahamed

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A Comprehensive Method for Industrial Site Selection: The Macro-Location Analysis

Cited by 21 publications

References 17 publications

Intelligent Decision Support System for Industrial Site Classification: A GIS-Based Hierarchical Neuro-Fuzzy Approach

Intelligent Decision Support System for Industrial Site Classification: A GIS-Based Hierarchical Neuro-Fuzzy Approach

Site selection of the construction of the system for the production of pasta in AP Vojvodina

GIS-based multi-criteria analysis modeling used to locate suitable sites for industries in suburban areas in Bangladesh to ensure the sustainability of agricultural lands

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