A Hybrid Analytic Hierarchy Process for Clustering and Ranking Best Location for Logistics Distribution Center

Simić, Dragan; Ильин, В. И.; Tanackov, Ilija; Svirčević, Vasa; Simić, Svetlana

doi:10.1007/978-3-319-19644-2_40

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…In the literature, studies on problem of DCs' location selection under certainty have focused on three categories of solution techniques [35] (see Table 1 the problem in question. We cite the method based on Analytic Hierarchy Process (AHP) and -means method [11] and the method based on REGIME [12]. The methods in this first category are used in the case where the number of predetermined alternatives are limited.…”

Section: Related Methodsmentioning

confidence: 99%

“…Multicriteria decision-making Method based on AHP and -means method Simić et al [11] Method based on REGIME Chakraborty et al [12] Metaheuristics for the multiobjective decision-making…”

Section: Proposed Bymentioning

confidence: 99%

“…Nature of the Problem. Much of the literature has studied the problem of selecting DCs' location under a certain and a deterministic environment [9,11,12,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. This kind of problem was characterized as static and deterministic, and parameters are known and fixed [33].…”

Section: Related Literaturementioning

confidence: 99%

“…Step 9 (filtering the alternatives). This step allows extracting, from all starting actions, the set of actions which respect (11). From this set, one action will finally be retained.…”

Section: Electre I Based Relevance Decision-makers Feedbackmentioning

confidence: 99%

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ELECTRE I Based Relevance Decision-Makers Feedback to the Location Selection of Distribution Centers

Agrebi

Abed

Omri

2017

Journal of Advanced Transportation

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The location selection of distribution centers is one of the important strategies to optimize the logistics system. To solve this problem, under certain environment, this paper presents a new multicriteria decision-making method based on ELECTRE I. The proposed method helps decision-makers to select the best location from a given set of locations for implementing. After having identified decision-makers, the criteria, and the set of locations, the factors influencing the selection are analyzed in order to identify the best location. A sensitivity analysis is then performed to determine the influence of criteria weights on the selection decision. The strength of the proposed method is to incorporate decision-makers’ preferences into the decision-making process. In addition, the proposed method considers both quantitative and qualitative criteria. Finally, the selected solution is validated by both tests of concordance and discordance simultaneously. A case study is provided to illustrate the proposed method.

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Section: Related Methodsmentioning

confidence: 99%

“…Multicriteria decision-making Method based on AHP and -means method Simić et al [11] Method based on REGIME Chakraborty et al [12] Metaheuristics for the multiobjective decision-making…”

Section: Proposed Bymentioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

“…Step 9 (filtering the alternatives). This step allows extracting, from all starting actions, the set of actions which respect (11). From this set, one action will finally be retained.…”

Section: Electre I Based Relevance Decision-makers Feedbackmentioning

confidence: 99%

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ELECTRE I Based Relevance Decision-Makers Feedback to the Location Selection of Distribution Centers

Agrebi

Abed

Omri

2017

Journal of Advanced Transportation

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“…The proposed intelligent system is tested on the real-world data set collected from the Clinical Centre of Vojvodina, Serbia. This paper presents a research that extends from and is applied on the basis of our previous research [ 15 ] and on the research in computer-assisted diagnosis methods [ 16 , 17 , 18 ], as well as on the industrial applications developed for clustering methods [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

An Application of a Hybrid Intelligent System for Diagnosing Primary Headaches

Simić

Villar

Calvo-Rolle

et al. 2021

IJERPH

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(1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelligence techniques, among which the most important are: Calinski-Harabasz index, Analytical Hierarchy Process, and Weighted Fuzzy C-means Clustering Algorithm. These methods, techniques and methodologies are used to create a hybrid intelligent system for diagnosing primary headache disorders. The proposed intelligent diagnostic system is tested with original real-world data set with different metrics. (3) Results: First at all, nine of 20 attributes – features from International Headache Society (IHS) criteria are selected, and then only five most important attributes from IHS criteria are selected. The calculation result based on the Calinski–Harabasz index value (178) for the optimal number of clusters is three, and they present three classes of headaches: (i) migraine, (ii) tension-type headaches (TTHs), and (iii) other primary headaches (OPHs). The proposed hybrid intelligent system shows the following quality metrics: Accuracy 75%; Precision 67% for migraine, 74% for TTHs, 86% for OPHs, and Average Precision 77%; Recall 86% for migraine, 73% for TTHs, 67% for OPHs, Average Recall 75%; F1 score 75% for migraine, 74% for TTHs, 75% for OPHs, and Average F1 score 75%. (4) Conclusions: The hybrid intelligent system presents qualitative and respectable experimental results. The implementation of existing diagnostics systems and the development of new diagnostics systems in medicine is necessary in order to help physicians make quality diagnosis and decide the best treatments for the patients.

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