Samar Shetaban; Mir Mehdi Seyyed Esfahani; Abbas Saghaei; Abbas Ahmadi
Abstract
Todays, human health is in the best situation ever. The progress of vaccines, development of hospitals, new medicines, advanced medical equipment, and new treatments preventing death will place the health system indicators at its best state in all ages and centuries. In addition, healthcare is one of ...
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Todays, human health is in the best situation ever. The progress of vaccines, development of hospitals, new medicines, advanced medical equipment, and new treatments preventing death will place the health system indicators at its best state in all ages and centuries. In addition, healthcare is one of the biggest industries in developed and developing countries and is a service-oriented industry that is significant, high-quality, and safe in medical services. Healthcare has become one of the biggest sectors in terms of income and employment. Health care involves hospitals, medical devices, clinical trials, outsourcing, telemedicine, medical tourism, health insurance, and medical equipment. Nowadays, the application of operations research in various fields, including health, is on increase. Although many issues face operations research in healthcare, such issues are not analytically different from the issues in other industries. Operations research, as a quantitative systemic method, can considerably solve the problems related to the health system. The present study aimed to evaluate the application of operations research models based on the research process in health systems including Markov decision-making processes (MDPs) and partially observable Markov decision process (POMDP), etc., and compare these methods with each other. The basis of this study was evaluating the publication of scientific studies on operations research models in the health systems.
Ehsan Dehghani; Peyman Taki
Abstract
This paper addresses an integrated multi-echelon location-allocation-inventory problem in a stochastic supply chain. In a bid to be more realistic, the demand and lead time are considered to be hemmed in by uncertainty. To tackle the proposed supply chain network design problem, a two-phase ...
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This paper addresses an integrated multi-echelon location-allocation-inventory problem in a stochastic supply chain. In a bid to be more realistic, the demand and lead time are considered to be hemmed in by uncertainty. To tackle the proposed supply chain network design problem, a two-phase approach based on queuing and optimization models is devised. The queuing approach is first deployed, which is able to cope with inherent uncertainty of parameters. Afterwards, the proposed supply chain network design problem is formulated using a mixed-integer nonlinear model. Likewise, the convexity of the model is proved and the optimal inventory policy as closed-form is acquired. Inasmuch as the concerned problem belongs to NP-hard problems, two meta-heuristic algorithms are employed, which are capable of circumventing the complexity burden of the model. The numerical examples evince the efficient and effective performance of the solving algorithms. Lastly, sensitivity analyses are conducted through which interesting insights are gained.