جستجوی مقالات مرتبط با کلیدواژه « مدیریت موجودی در زنجیره تامین سلامت » در نشریات گروه « مدیریت »

This study aims to investigate a supply chain problem of operating room consumable items that are not reused after consumption. In this supply chain, maximizing the satisfaction of the surgeons and minimizing the total costs are considered. Also, due to the importance of choosing suppliers from the surgeons' point of view, it is possible to prioritize suppliers based on criteria such as quality and cost. Furthermore, to get closer to real-world situations, uncertain demands of patients due to their physical conditions and various diseases, the capacities of the pharmacy, operating rooms, and the sterile core used for sterilizing the non-sterile items have been considered. The scope of this research includes different operating rooms, and the initially required number of consumable items according to the opinion of the surgeon. If an emergency occurs during the operation (such as sudden bleeding, item failure, or operating room personnel error) and the patient needs more items, the nurse goes to the hospital pharmacy to get the necessary items and brings them to the operating room, during the operation.
Design/methodology/approach: In this research, due to the uncertain demand for consumable items in the operating room, three pessimistic, probable, and optimistic scenarios have been used; and due to the discreteness and uncertainty of the data distribution, Mulvey's robust method has been applied. The problem has been solved in two phases. In the first phase, the additive ratio assessment (ARAS) multi-criteria decision-making method has been used to prioritize suppliers, and in the second phase, according to the size of the problem, the epsilon-constraint method, for the small-sized problem, and Non-dominated Sorting Genetic Algorithms (NSGA-II) and Multi-Objective Harmony Search (MOHS) for large-sized problems have been used to minimize the total costs of the supply chain, and maximize surgeons’ satisfaction. In addition, to set the parameters of both meta-heuristic algorithms, the Taguchi method, which is one of the most well-known parameter-setting methods, has been used.
To compare exact and metaheuristic algorithms, 10 examples were designed randomly. The comparisons showed that the results of the epsilon-constraint method were better than the meta-heuristic algorithms but it could only solve small-sized problems, and it required more time as a sensitive influencing factor in operating room planning. Also, to analyze the NSGA-II and MOHS algorithms, the obtained results were examined from the perspective of solution time, Number of Pareto Solutions (NPS), Mean Ideal Distance (MID), Diversification Metric (DM), and Spacing Measure (SM) indicators. They were also compared with each other using statistical hypothesis tests. The results showed that such algorithms had a significant difference from the point of view of the NPS and DM indicators at the significance level of 0.05, but they did not differ much in terms of the other two indicators. However, in terms of solution time, the MOHS was more suitable than the NSGA-II algorithm.
Research limitations/implications: One of the limitations of this research is the collection of real-world data, especially in estimating the demand for each item according to different conditions.
Practical implications: Comparing the NSGA-II and MOHS algorithms using different indicators, especially solution time which is significant for operating room planning, MOHS algorithms were better than the NSGA-II.
Social implications: Using the proposed algorithms, hospital managers can reduce total costs, guarantee the quality of consumable operating room items, and increase the satisfaction of the surgeon, who is in charge of providing better services to the patients.
Originality/value: In this paper, two meta-heuristic algorithms were proposed for non-deterministic supply chain planning for consumable operating room items, considering surgeon satisfaction and cost, and their efficiencies were compared with each other. The two-mentioned algorithms have not been used in previous studies. Both academic researchers and hospital managers can benefit from applying the findings of this study.