Return material authorization (RMA) is a process in which a company decides to repair or replace customer's defect product during the warranty period. To execute RMA, both company and customer obliged to follow standard operating procedure (SOP) which usually consists of many business processes of a company well. As the business process could cause inefficiencies, a company should improve their business process regularly. The best way is using process discovery. This research proposes a new improved fuzzy miner algorithm to represent binary correlation between activities. This new algorithm utilizes binary significance and binary correlation equally to acquire fuzzy model. While the original fuzzy miner algorithm uses various binary correlation metrics, the improved fuzzy miner algorithm uses only one metric and could capture the fuzzy model, accurately based on the event logs to capture more accurate business process model. In this research, ProM fuzzy miner is used as a comparison to the proposed improved time-based fuzzy miner. The results showed that the improved algorithm has higher value on conformance checking and able to capture business process model based on time interval, by using only time-interval significance as a binary correlation metrics.
The emergence of the Covid-19 pandemic has caused several changes in healthcare services carried out by hospitals. Covid-19 has caused increasing waste generated from medical activities and operational service activities, in which the Standard Operating Procedure (SOP) has been adjusted due to new regulations to prevent cross-contamination during this pandemic. The increasing number of wastes generated and changes in SOP could have impacted on spending more costs for processing medical waste caused by Covid-19 operational services and causes longer service time. The purpose of this study was to find the optimum value of resource level based on operational costs and service time from the medical waste handling developed with hybrid Discrete Event Simulation -Agent-Based Modelling (DES-ABM) to capture real-time events. To find optimum value, optimization techniques such as Genetic Algorithm and Goal Programming are also used. Optimization from this research results in 17 alternatives of resource level from a total of 100 generations and 20 initial design point. The best design point found could reduce the waiting time by 26.87 minutes, reduce completion time by 506.82 minutes, and reduce cost IDR117,144 from the initial resource level used by the hospital.
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