Port Container Terminal (PCT) involves multi organizations which are Customer, PCT, Custom, and Quarantine. The business processes of those organizations interact asynchronously from container arrivals to the release of the containers. An organization can be handled by multi agents, which represent the actors involved to perform a sequence of activities (tasks) in the organization. Asynchronous Waiting Time (AWT) occurs when an agent assigns a task to another agent which is still working on another task. The previous study discovered that AWT contributes a significant amount of time. Therefore, this research proposes a method to reduce the AWT by parallelizing the agents of an organization and simulating the parallelized agents using agent based simulation. The simulation results time and cost are then optimized using three methods namely Stochastic Multi-Criteria Adaptability Analysis-2 (SMAA-2), Multi Objective Optimization on the Basis of Ratio Analysis (MOORA), and Complex Proportional Assessment (COPRAS). The three methods achieve the same reduced AWT to 9.4%. From those methods, MOORA achieves the highest accuracy of 80% and the sensitivity coefficient of 7. However, COPRAS results in 78% accuracy with lower sensitivity coefficient of 6. SMAA-2 results in the lowest accuracy of 40% and the highest sensitivity coefficient of 13.
A Port Container Terminal (PCT) involves complex business processes which are carried out by at least four organizations, namely PCT Operator, Customer, Quarantine and Customs. Each organization produces event log data from the activities. The event log data from the four organizations contain synchronous and asynchronous activities. In this research, the four organizations are represented by four agents. By simulating this log data using agent based simulation, we get the performance of the current business process. The performance indicators gathered are time and cost which are needed to do the activity (task). After the simulation is complete, we found Asynchronous Waiting Time (AWT). AWT is waiting time which happens because the agent in the simulation cannot do the newly assigned task because the agent is still working on the other task. Therefore, we parallelize the task performed by the agent so that the agent can do multiple tasks at a time. After we parallelize the task, we perform an optimization process using Stochastic Multicriteria Adaptability Analysis 2 (SMAA-2). Thus, the optimal amount of task an agent can do simultaneously is analyzed. This study result shows that parallelization can reduce AWT of the current system and the optimization process using SMAA-2 shows the most optimal number of multiple tasks an agent can do simultaneously.
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