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Supply chains face numerous disruptions in today's dynamic world, and achieving resilience is vital for healthcare systems, especially in the blood supply chain (BSC). However, there are several barriers hindering resilience, and... more
Supply chains face numerous disruptions in today's dynamic world, and achieving resilience is vital for healthcare systems, especially in the blood supply chain (BSC). However, there are several barriers hindering resilience, and identifying and prioritizing them is essential for developing effective strategies to improve resilience. This study proposes an integrated multi-criteria decision-making (MCDM) approach that combines the Best Worst Method (BWM), Delphi, and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) to recognize and prioritize SC resilience barriers in the BSC of Tehran, the largest BSC in Iran. The proposed approach provides real-time results for future improvements, and sensitivity analysis investigates the effects of criteria weights on decision-making. Additionally, the proposed method is compared with two existing methods, namely BWM- VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and BWM-Weighted Aggregated Sum Prod...
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Research Interests: Supply Chain and TOPSIS
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The nature of input materials is changed as long as the product reaches the consumer in many types of manufacturing processes. In designing and improving multi-stage systems, the study of the steps separately may not lead to the greatest... more
The nature of input materials is changed as long as the product reaches the consumer in many types of manufacturing processes. In designing and improving multi-stage systems, the study of the steps separately may not lead to the greatest possible improvement in the whole system, therefore the study of inputs and outputs of each stage can be effective in improving the output quality characteristics. In this study, the double sampling method is applied for inspection where decision variables are the sample size per sampling time and the maximum amount of defective items in the first and second samples in each stage. Furthermore, uncertainty in parameters such as production, inspection, and replacement costs are included in the objective function and handled by a Monte-Carlo based optimization method. In order to show the efficacies of the proposed method, a numerical example has been designed, and further analyses on solutions have been conducted.
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Objective: This paper seeks to find the optimal number of hubs and their location to keep the preparation time and congestion in the hubs and costs at a minimum. Also, this study considers the tardiness time for the conditions that the... more
Objective: This paper seeks to find the optimal number of hubs and their location to keep the preparation time and congestion in the hubs and costs at a minimum. Also, this study considers the tardiness time for the conditions that the customer's need is not answered in the specified time, which can help to make the problem conditions more realistic. Therefore, in this study, the scheduled time and the real-time system are considered. This paper considers the tardiness and congestion time for hub optimization problems with single, multiple, and multiple direct transport allocations. The decision variables in this model determine the number of hubs, the capacity of the hubs, and their location. Congestion and tardiness also affect service time, especially if the capacity and cost of hubs are limited. Methods: This paper uses a mathematical model to solve the hub problem of optimizing the allocationlocation of single, multiple, and multiple with direct transportation. GAMS software is used to find the optimal number of hubs and locations as two objective functions are optimized. The first objective function includes transportation costs, hub setup, and tardiness costs, and the second one consists of the handling time in the hubs and the congestion inside the hubs. The sensitive analysis is investigated for the service time based on the congestion and tardiness time. Results: This model is tested on AP (Australian Post) data for single, multiple, and multiple with direct shipping allocation models. This study also solves the exact model for 100 nodes allocated to all three models. The hubs' capacity, congestion, and tardiness determine the number of hubs. In this paper, hubs are considered small, medium, and large. Congestion levels are also considered changeable. In addition, a comparison is made between single and multiple allocations concerning cost and capacity limitation to investigate service time. The findings indicate that a hub with limited cost and capacity needs more service time. The lexicography method is also used to convert objective functions into one function. Conclusion: The more the number of hubs increases, the total costs, including the transportation and hub establishment costs will also increase. Therefore, considering the transportation costs and the establishment of the hub, it can be said that single and multiple allocations can be used in some situations. However, multiple allocations with direct transport have the lowest transportation costs because goods based on the costs are transported through the non-hub and hub nodes. In general, the results indicate that using the multiple allocation model with direct transport can reduce the total transport cost, and a single allocation has the highest transport costs. This paper is helpful for managers and business owners who first want to identify points for building their product or service warehouse. Secondly, they want to have the most optimal type of allocation for transportation from different cities.