Supplier selection decision making in disaster response

Olanrewaju, Oluwasegun G.; Dong, Zhijie; Hu, Shaolong

doi:10.1016/j.cie.2020.106412

Cited by 46 publications

(22 citation statements)

References 28 publications

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“…This paper focuses on social media data-driven analytics for improving disaster response efficiency, especially for essential relief supplies, such as housing, transportation, food, and medical supplies. While previous studies (Olanrewaju et al 2020; have studied supplier selection and pre-positioning strategies in humanitarian relief. Based on the current research, we are inspired to develop an integrated disasterrelated social media analytics platform for better disaster management.…”

Section: Discussionmentioning

confidence: 99%

Social media information sharing for natural disaster response

et al. 2021

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Social media has become an essential channel for posting disaster-related information, which provides governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient attention, and extracting useful information is still challenging. This paper aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes toward disaster response and public demands for targeted relief supplies during different types of disasters. We focus on different natural disasters based on properties such as types, durations, and damages, which contains a total of 41,993 tweets. In this paper, public perception is assessed qualitatively by manually classified tweets, which contain information like the demand for targeted relief supplies, satisfactions of disaster response, and public fear. Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models. To better provide decision-makers with the appropriate model, the comparison of machine learning models based on computational time and prediction accuracy is conducted. The change of public opinion during different natural disasters and the evolution of peoples' behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are studied. The results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster management.

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Section: Discussionmentioning

confidence: 99%

Social media information sharing for natural disaster response

et al. 2021

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“…Supplier selection has also been considered to reduce the damage caused by natural disasters when relief items are urgently needed in large amounts. The study conducted by Olanrewaju et al (2020) proposes integrating the supplier selection for the timely distribution of relief supplies. Similarly, another study (Balcik and Ak 2014) tackled the problem from the perspective of organizations in humanitarian relief.…”

Section: Approaches For Supplier Selectionmentioning

confidence: 99%

“…In He et al (2009), the authors consider a multiobjective supplier selection problem and convert it into a single objective, non-linear chance-constrained programming problem. A multi-stage stochastic programming approach for supplier selection, which models different types of natural disasters, was presented in Olanrewaju et al (2020).…”

Section: Approaches For Supplier Selectionmentioning

confidence: 99%

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A multi-objective supplier selection framework based on user-preferences

et al. 2021

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This paper introduces an interactive framework to guide decision-makers in a multi-criteria supplier selection process. State-of-the-art multi-criteria methods for supplier selection elicit the decision-maker’s preferences among the criteria by processing pre-collected data from different stakeholders. We propose a different approach where the preferences are elicited through an active learning loop. At each step, the framework optimally solves a combinatorial problem multiple times with different weights assigned to the objectives. Afterwards, a pair of solutions among those computed is selected using a particular query selection strategy, and the decision-maker expresses a preference between them. These two steps are repeated until a specific stopping criterion is satisfied. We also introduce two novel fast query selection strategies, and we compare them with a myopically optimal query selection strategy. Computational experiments on a large set of randomly generated instances are used to examine the performance of our query selection strategies, showing a better computation time and similar performance in terms of the number of queries taken to achieve convergence. Our experimental results also show the usability of the framework for real-world problems with respect to the execution time and the number of loops needed to achieve convergence.

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“…Based on the above analysis, the total procurement costs of these five suppliers are calculated as follows: rough the calculation of the procurement costs of the above suppliers, when the purchase cost is constant, the higher the company's purchase cost for a certain supplier, the more transactions the supplier has with the company, and the more important it is to the company. [28][29][30][31], so it can be concluded that the order of the 5 suppliers to be selected is g 1 > g 2 > g 5 > g 3 > g 4 . According to the situation of these 5 suppliers, a multiobjective mathematical programming model is established to calculate the distribution of the calculated purchase amount.…”

Section: The Data Analysismentioning

confidence: 99%

Exploiting the Chain Convenience Store Supplier Selection Based on ANP-MOP Model

Liu

2021

Mathematical Problems in Engineering

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The purchase cost of chain convenience stores accounts for a large proportion of the total cost. Enterprises are facing the problem of how to reasonably reduce the purchase cost while ensuring the quality of service. This paper first considers the uniqueness of chain convenience stores, draws on existing research results, and establishes an evaluation index system for chain convenience store suppliers through field research. Then, we use the principal component analysis method of selection to determine the weight of each index and make a preliminary ranking of the importance of chain convenience store suppliers. Secondly, according to the relevant weights determined by the network hierarchy method, the collected data are substituted into the multiobjective mathematical programming model to analyze the distribution of supplier procurement and calculate the procurement cost. The level of procurement expenditures accounted for by suppliers confirms the importance of suppliers. The results show that through the evaluation and ranking of suppliers and the effective management of suppliers according to the ranking results, not only the goal of cost minimization is achieved but also the reasonable service level is guaranteed, which is scientific.

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Supplier selection decision making in disaster response

Cited by 46 publications

References 28 publications

Social media information sharing for natural disaster response

Social media information sharing for natural disaster response

A multi-objective supplier selection framework based on user-preferences

Exploiting the Chain Convenience Store Supplier Selection Based on ANP-MOP Model

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