Location Modeling
Alireza Roshani; Mohammad Reza Gholamian; Mahsa Arabi
Abstract
Purpose: Due to the increasing complexity of uncertainty and its impact on the supply chain network, many researchers have resorted to coping approaches with data uncertainty. In addition, the occurrence of any disruption in the supply chain networks can cause irreparable damage. Therefore, adopting ...
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Purpose: Due to the increasing complexity of uncertainty and its impact on the supply chain network, many researchers have resorted to coping approaches with data uncertainty. In addition, the occurrence of any disruption in the supply chain networks can cause irreparable damage. Therefore, adopting appropriate strategies to increase the level of the supply chain network resilience toward any disruptive events seem to be necessary.Methodology: In this paper, a multi-objective, multi-period, and scenario-based mathematical model is presented in which objective functions of delivery time and total network cost are minimized, and to increase network resilience, non-resilience measures are also minimized. Furthermore, a Two-Stage Stochastic Programming (TSSP) approach has been utilized to overcome the uncertain nature of the input parameters. Goal programming has also been used to transform the model into a single-objective one.Findings: In order to prove the model's applicability, the real-world data of a case study of Mashhad has been implemented. Eventually, according to the validation and sensitivity analysis results, the proposed uncertain model has clear superiority over the deterministic model.Originality/Value: This paper presents a multi-objective linear mathematical model for designing the Pharmaceutical Supply Chain (PSC) network under the COVID-19 situation. Two indicators of time and resilience as optimization tools have been considered simultaneously.
supply chain management analyzing/modelling
Mostafa Ziyaei Hajipirlu; Houshang Taghizadeh; Mortaza Honarmand azimi
Abstract
Due to the complex and multifaceted nature of supply chain resilience, hasn't yet been proposed a comprehensive, concrete, and aggregative model that includes the prevailing consensus of researchers in this field. In order to try to achieve that, the present study was conducted with the aim of forming ...
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Due to the complex and multifaceted nature of supply chain resilience, hasn't yet been proposed a comprehensive, concrete, and aggregative model that includes the prevailing consensus of researchers in this field. In order to try to achieve that, the present study was conducted with the aim of forming a comprehensive model for supply chain resilience assessment using an integrated approach based on scientometrics and various artificial intelligence methods based on knowledge extraction from the text. The statistical population includes all indexed articles related to supply chain resilience from 2002 to 2020 in the two scientific databases Scopus and WOS. During the three stages of document refinement with a systematic review approach, scientometric information, and the full text of 346 articles were extracted and used in the analysis process. Utilizing an integrated approach based on the fusion of scientometrics of related metadata, and artificial intelligence tools to extraction of supply chain resilience assessment tool obtain the main innovation of this research which makes feasible establishing an evaluation model without interfering with researcher source biases. Finally, the supply chain resilience evaluation model including 4 main structures and 25 sub-structures was extracted from related scientific documents.