Optimization under uncertainty of the pharmaceutical supply chain in hospitals

Franco, Carlos; Alfonso-Lizarazo, Edgar

doi:10.1016/j.compchemeng.2019.106689

Cited by 66 publications

(36 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve their proposed model, multi-objective social engineering optimization, simulated annealing, Keshtle algorithm, and firefly algorithm meta-heuristic approaches have been utilized. Franco and Alfonso-Lizarazo (2020) designed a stochastic simulation–optimization model for operational and strategic decisions in the medicine supply chain. For this purpose, two mathematical models have been considered with the levels of hospital and pharmacy.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated sustainable medical supply chain network during COVID-19

Goodarzian

Taleizadeh

Ghasemi

et al. 2021

Engineering Applications of Artificial Intelligence

165

View full text Add to dashboard Cite

Nowadays, in the pharmaceutical industry, a growing concern with sustainability has become a strict consideration during the COVID-19 pandemic. There is a lack of good mathematical models in the field. In this research, a production–distribution–inventory–allocation–location problem in the sustainable medical supply chain network is designed to fill this gap. Also, the distribution of medicines related to COVID-19 patients and the periods of production and delivery of medicine according to the perishability of some medicines are considered. In the model, a multi-objective, multi-level, multi-product, and multi-period problem for a sustainable medical supply chain network is designed. Three hybrid meta-heuristic algorithms, namely, ant colony optimization, fish swarm algorithm, and firefly algorithm are suggested, hybridized with variable neighborhood search to solve the sustainable medical supply chain network model. Response surface method is used to tune the parameters since meta-heuristic algorithms are sensitive to input parameters. Six assessment metrics were used to assess the quality of the obtained Pareto frontier by the meta-heuristic algorithms on the considered problems. A real case study is used and empirical results indicate the superiority of the hybrid fish swarm algorithm with variable neighborhood search.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

An integrated sustainable medical supply chain network during COVID-19

Goodarzian

Taleizadeh

Ghasemi

et al. 2021

Engineering Applications of Artificial Intelligence

165

View full text Add to dashboard Cite

show abstract

“…Their outcomes show the suitable efficiency of their presented model for a case study in Tehran/Iran. Two stochastic simulation-optimization models for strategic and operational decisions in the pharmaceutical supply chain is presented by Franco and Alfonso-Lizarazo (2020). In their first model, the time of medicine perishable and service level and in their second model, inventory control of medicine is considered.…”

Section: Literature Reviewmentioning

confidence: 99%

A sustainable-resilience healthcare network for handling COVID-19 pandemic

et al. 2021

View full text Add to dashboard Cite

In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and multi-echelon mixed-integer linear programming model for the current network is formulated and designed.Formulating a new MILP model to design a sustainable-resilience healthcare network during the COVID-19 pandemic and developing three hybrid meta-heuristic algorithms are among the most important contributions of this research. In order to estimate the values of the required demand for medicines, the simulation approach is employed. To cope with uncertain parameters, stochastic chance-constraint programming is proposed. This paper also proposed three meta-heuristic methods including Multi-Objective Teaching-learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. Since heuristic approaches are sensitive to input parameters, the Taguchi approach is suggested to control and tune the parameters. A comparison is performed by using eight assessment metrics to validate the quality of the obtained Pareto frontier by the heuristic B Angappa Gunasekaren

show abstract

“…We demonstrated the remarkable effect of this assumption in the Introduction Section (see the models developed by Soleimani et al (2017), Yadollahinia et al (2018), Yavari and Geraeli (2019), Banasik et al (2019) and Zahiri et al (2018)). We also insist on considering uncertainty features in all main parameters of a GSC or SSC model that is ignored in several relevant models (Chibeles-Martins et al 2016;John et al 2017;Reddy et al 2019;Franco and Alfonso-Lizarazo 2020). Finally, researchers have had less attention to compare different carbon policies and they usually focused on carbon cap-and-trade policy (see Kaur and Singh (2018) and Gholizadeh et al (2020)).…”

Section: Contributions Of the Studymentioning

confidence: 99%

A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty

et al. 2021

View full text Add to dashboard Cite

Adoption of carbon regulation mechanisms facilitates an evolution toward green and sustainable supply chains followed by an increased complexity. Through the development and usage of a multi-choice goal programming model solved by an improved algorithm, this article investigates sustainability strategies for carbon regulations mechanisms. We first propose a sustainable logistics model that considers assorted vehicle types and gas emissions involved with product transportation. We then construct a bi-objective model that minimizes total cost as the first objective function and follows environmental considerations in the second one. With our novel robust-heuristic optimization approach, we seek to support the decision-makers in comparison and selection of carbon emission policies in supply chains in complex settings with assorted vehicle types, demand and economic uncertainty. We deploy our model in a case-study to evaluate and analyse two carbon reduction policies, i.e., carbon-tax and cap-and-trade policies. The results demonstrate that our robust-heuristic methodology can efficiently deal with demand and economic uncertainty, especially in large-scale problems. Our findings suggest that governmental incentives for a cap-and-trade policy would be more effective for supply chains in lowering pollution by investing in cleaner technologies and adopting greener practices.

show abstract

Optimization under uncertainty of the pharmaceutical supply chain in hospitals

Cited by 66 publications

References 43 publications

An integrated sustainable medical supply chain network during COVID-19

An integrated sustainable medical supply chain network during COVID-19

A sustainable-resilience healthcare network for handling COVID-19 pandemic

A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty

Contact Info

Product

Resources

About