Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management

Abbasi, Babak; Babaei, Toktam; Hosseinifard, Zahra; Smith‐Miles, Kate; Dehghani, Maryam

doi:10.1016/j.cor.2020.104941

Cited by 77 publications

(30 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides the benefits regarding speed and accuracy of the obtained solution, the authors point out the low requirements in terms of computing power and needed data: they show that, unlike optimization models, the end-to-end learning method succeeds in solving the problem under imperfect information. Abbasi et al (2020) apply this method to the stochastic optimization problem of blood transshipment in a network of hospitals, aimed at minimizing overall costs. Since commercial solvers and expertise in the optimization field are often not available in these contexts, the authors propose to run the optimization only for a limited number of days, with the support of an external partner, aiming to gather a data sample for the training of a supervised ML model that can be later applied to support daily transshipment decisions.…”

Section: End-to-end Learning Methodsmentioning

confidence: 99%

“…Recently, a few studies have introduced approaches based on the combined use of optimization and machine learning (ML), committing to predict the optimal solution (Abbasi et al 2020;Bengio et al 2020) with a lower data collection effort compared to optimization approaches (Larsen et al 2018). So far, combined optimization-ML approaches have been applied in the fields of energy systems (Fischetti and Fraccaro 2019) and transportation management (Larsen et al 2018;Abbasi et al 2020), but they raise the attention towards the opportunity to support data-driven decision making in many more fields. As regards production systems, the available literature includes several contributions in which ML techniques are applied (Kang et al 2020;Bertolini et al 2021), but never in combination with optimization.…”

Section: Introductionmentioning

confidence: 99%

“…However, a number of issues should be better tackled in order to foster further research on combined optimization-ML approaches. The solution quality compared to optimization has been evaluated only by Abbasi et al (2020). Moreover, the feasibility of the solution predicted through the ML model is an open challenge in the research on combined optimization-ML approaches (Bengio et al 2020), as well as the required data collection effort (Larsen et al 2018).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring the application of machine learning to the assembly line feeding problem

2021

View full text Add to dashboard Cite

As a large number of companies are resorting to increased product variety and customization, a growing attention is being put on the design and management of part feeding systems. Recent works have proved the effectiveness of hybrid feeding policies, which consist in using multiple feeding policies in the same assembly system. In this context, the assembly line feeding problem (ALFP) refers to the selection of a suitable feeding policy for each part. In literature, the ALFP is addressed either by developing optimization models or by categorizing the parts and assigning these categories to policies based on some characteristics of both the parts and the assembly system. This paper presents a new approach for selecting a suitable feeding policy for each part, based on supervised machine learning. The developed approach is applied to an industrial case and its performance is compared with the one resulting from an optimization approach. The application to the industrial case allows deepening the existing trade-off between efficiency (i.e., amount of data to be collected and dedicated resources) and quality of the ALFP solution (i.e., closeness to the optimal solution), discussing the managerial implications of different ALFP solution approaches and showing the potential value stemming from machine learning application.

show abstract

Section: End-to-end Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the application of machine learning to the assembly line feeding problem

2021

View full text Add to dashboard Cite

show abstract

“…For example, machine learning can support data-driven decisionmaking in pre-production, production, processing, and distribution stages of agricultural SCs (Sharma et al 2020). Prior research has examined the role of AI for effective and efficient SCM, including forecasting (Chien, Lin, and Lin 2020), configuration and optimisation (Abbasi et al 2020;Fragapane et al 2020), forecasting (Chien, Lin, and Lin 2020), risk management (Baryannis et al 2020;Soleymani and Nejad 2018), col-laboration between human operators and AI-based systems (Klumpp 2018), increased operational efficiency in replenishment policies (Priore et al 2019), and supplier selection (Zhao and Yu 2011;Choy et al 2004). While such studies have made valuable contributions, knowledge about the influential and mediating role of culture is limited.…”

Section: Evolution Of Ai In Modern Operations and Supply Chain Managementmentioning

confidence: 99%

Understanding the influential and mediating role of cultural enablers of AI integration to supply chain

Cadden

Dennehy

Mäntymäki

et al. 2021

International Journal of Production Research

View full text Add to dashboard Cite

Artificial Intelligence (AI) has been claimed to offer transformational power across industries and sectors. To date, research has largely focused on the technical characteristics of AI and its influence on organisational capabilities. Despite the hype surrounding AI, there is a scarcity of rigorous research that examines the organisational and behavioural factors that foster AI integration in supply chains is lacking. This quantitative study addresses this gap in knowledge by developing a research hypothesis that examines the relationships between supply chain culture and AI. We extend the generalisability of culture to provide novel insights about AI-driven supply chains that have not been reported in previous studies. The findings demonstrate the influential role that cultural enablers have on the successful integration of AI technologies in supply chains, which has implications for operations and supply chain management.

show abstract

“…Machine learning is widely used in the medical field. For example, Abbasi et al [62] proposed a new method for solving large-scale stochastic operation optimization problems (SOPs) using a machine learning model and applying the proposed decision-making method of blood unit transportation in the hospital network. The results show that compared with the current strategy, the average daily cost is reduced by 29% with the trained neural network model.…”

mentioning

confidence: 99%

Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods

Zhu

et al. 2021

IEEE Access

View full text Add to dashboard Cite

This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction. INDEX TERMSCOVID-19, coronavirus, fuzzy K-nearest neighbor, Harris hawk optimization, disease diagnosis, feature selection. I. INTRODUCTION Coronavirus disease 2019 (COVID-19) is a highly contagious viral disease, and the World Health Organization (WHO) declared that the COVID-19 was an international public health emergency [1], [2]. First described COVID-19 in December 2019 in Wuhan, Hubei Province, China. The ongoing outbreak of COVID-19 is affecting multiple countries in the world [1]. Until Mar 11th, 2020, The associate editor coordinating the review of this manuscript and approving it for publication was Juan Wang . 118,326 cases of COVID-19 were diagnosed worldwide, including 80,955 cases in China and 37,371 cases outside China. Additionally, 4,292 deaths have been triggered by COVID-19 [3]. Many countries are facing increased pressures on health care resources. Up to now, a great deal of studies is focused on using traditional statistical methods to identify risk factors of COVID-19 patients. As an example, older age, pre-existing diseases, abnormal liver function, and T-lymphocyte count were correlated closely with COVID-19 progression and prognosis [4]-[6]. However, traditional statistical methods could not rapidly identify changes

show abstract

Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management

Cited by 77 publications

References 28 publications

Exploring the application of machine learning to the assembly line feeding problem

Exploring the application of machine learning to the assembly line feeding problem

Understanding the influential and mediating role of cultural enablers of AI integration to supply chain

Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods

Contact Info

Product

Resources

About