A model for prioritizing outsourceable activities in universities through an integrated fuzzy-MCDM method

Kiani, Mehrdad; Bagheri, Moslem; Ebrahimi, Abolghasem; Alimohammadlou, Moslem

doi:10.1080/15623599.2019.1645264

Cited by 13 publications

(2 citation statements)

References 34 publications

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“…Zarbakhshnia et al [36] integrated a fuzzy AHP method and grey multiobjective optimization by ratio analysis (MOORA-G) for evaluating reverse logistics 3PL providers. Kiani et al [37] utilized a fuzzy MCDM approach for prioritizing outsourceable activities in universities. They combined the AHP, SAW, TOPSIS, and VIKOR methods.…”

Section: Research Methods For Analyzing the Freight Distribution Conceptmentioning

confidence: 99%

Picture Fuzzy ARAS Method for Freight Distribution Concept Selection

et al. 2020

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Companies can perform their freight distribution in three different ways. The first concept, the in-house concept, represents the use of a company’s own resources and knowledge to organize transportation from the production to retailers or from the warehouse to customers. The opposite concept is to outsource distribution activities by hiring third-party logistics providers. The third concept represents a combination of the previous two. Although the arguments in favor of outsourcing can be found in the literature, an appropriate selection of a freight distribution concept is specific for each company and depends on many evaluation criteria and their symmetrical roles. This paper presents a methodology that can be used by companies that need to choose their freight distribution concept. An advanced extension of the Additive Ratio ASsessment (ARAS) method is developed to solve the freight distribution concept selection problem. To illustrate the implementation of the proposed methodology, a tire manufacturing company from the Czech Republic is taken as a case study. However, the proposed picture fuzzy ARAS method is general and can be used by any other company. To validate the novel picture fuzzy ARAS method, a comparative analysis with the nine existing state-of-the-art picture fuzzy multi-criteria decision-making methods is provided.

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Section: Research Methods For Analyzing the Freight Distribution Conceptmentioning

confidence: 99%

Picture Fuzzy ARAS Method for Freight Distribution Concept Selection

et al. 2020

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“…In the recent literature, TOPSIS and Fuzzy TOPSIS have been used in numerous fields such as assessment of energy systems (Taylan et al, 2020), maintenance management (Olugu et al, 2021), occupational accident (Abbasinia & Mohammadfam, 2022), petrochemical industry (Abbassinia et al, 2020), noise control (Mousavi et al, 2019), outsourcing (Kiani et al, 2022), reverse logistics (Naseem et al, 2021), manufacturing systems (Mathew et al, 2020) and renewable energy (Solangi et al, 2019). Usage of TOPSIS can be found in healthcare literature for analysis of dose planning (Malekpoor et al, 2022), hospital waste management (Baghapour et al, 2018), medical device risk factors (Kirkire et al, 2018), triage in intensive care unit (Davodabadi et al, 2021), human errors factors in the emergency departments (Hsieh et al, 2018), mobile health (Rajak & Shaw, 2019), medical diagnosis (Baccour, 2018), and nano material selection (K. Zhang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach

Hassan¹,

Umer²,

Hassan³

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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Prioritizing patients is a growing concern in healthcare. Once resources are limited, prioritization is considered an effective and viable solution in provision of healthcare treatment to awaiting patients. Prioritization is a preferred approach that helps clinicians to apportion scarce resources fairly and transparently. In this study, a novel methodology of prioritizing the patient is formulated using fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The objective is based on actual hospital conditions in Pakistan. The proposed methodology has two contributions: objective scoring mechanism that translates the patient’s condition given in human linguistic terms; and second methodology to prioritize patients according to corresponding scores. To validate the proposed methodology, simulation was carried out on actual data collected in real-time by surgeons, while providing consultations to their patients. The proposed methodology outperforms the traditional methodology by reducing average waiting time by 34% (from 4.246 to 2.810 days), minimize wait time and delays by 46.7% (from 15 to 8 days), and number of surgery days by 18%. The majority of the previously presented researched methodologies prioritize the patients subjectively. This study presents an objective methodology to prioritize the patients and decrease wait-times while ensuring transparency and equity.

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