Extra Credit: You Can Get Up To 2520 By Completing 7C

Extra Credityou Can Get Up To A 2520 By Completing 7c Even Though

This assignment involves creating a simulation of a bank's customer service system utilizing C++'s Standard Template Library (STL) priority_queue and queue. The core objective is to model the arrival and departure of bank customers, track their wait times, and manage event processing efficiently by leveraging the priority queue's sorting capabilities.

The key idea is to simulate events such as customers arriving and departing, each associated with a timestamp. These events need to be processed in chronological order, and because arrivals and departures can be interleaved, a priority queue sorted by event time is instrumental. Instead of traditional max-heap sorting, the priority queue here is manipulated to prioritize events with the smallest timestamp, which can be achieved by subtracting the event time from a large number such as 9999.

In this simulation, each event is characterized as either an arrival or a departure. The event structure should include relevant details like event type, timestamp, and customer identifier. When processing an arrival event, if the teller is free and there are no customers waiting, the customer's departure event is scheduled immediately based on their transaction time. If the teller is busy, the customer is added to a bank queue to wait. When handling departure events, the system determines whether there are waiting customers to serve next and calculates individual wait times accordingly.

The simulation aims to calculate the total wait time for all customers and the average wait time, providing insight into the efficiency of the bank's customer service process. Special handling ensures the first customer experiences no wait, and subsequent customers' wait times are computed based on their arrival and departure times.

Paper For Above instruction

The simulation of bank customer service operations is an excellent example of how data structures like priority queues and queues can be integrated to model real-world processes efficiently. The primary challenge in such simulations is maintaining the correct chronological order of varied events—arrivals and departures—while enabling dynamic updates based on customer transactions and service availability. Using the C++ STL priority_queue, which logically functions as a max-heap, we can manipulate it to serve as a min-heap by inverting the sorting metric, thus effectively prioritizing events with earlier timestamps.

To implement this system, an event structure must be defined to encapsulate necessary data: event type (arrival or departure), timestamp, and customer ID. Sorting within the priority queue is achieved by assigning priorities inversely proportional to the event time—subtracting the event's timestamp from a large constant like 9999—so that earlier events are processed first. This mechanism ensures that the event with the lowest time (or highest priority in the inverted scheme) is always at the queue's top.

The core of the simulation hinges on processing these events in order. On an arrival event, if the teller is unoccupied, the customer is served immediately, and a departure event is scheduled for that customer based on their transaction duration. If the teller is busy, the customer is added to a waiting queue, and their arrival time is recorded for wait time calculations later. When a departure event is processed, the system checks whether other customers are waiting in the queue; if so, it begins serving the next customer immediately, scheduling their departure accordingly. If no customers are waiting, the teller becomes available for the next arrival.

Calculating customer wait times involves noting the difference between the actual arrival time and the time service begins. For the first customer, this value is zero since they arrive when the teller is free. For subsequent customers, the wait is the difference between their arrival time and the time when the teller started serving them, which can be derived from their departure and arrival times, stored during process. By summing these individual wait times, the total wait time across all customers can be determined, and the average wait time is simply the total divided by the number of customers.

This modeling process underscores the importance of accurate event handling and data structure utilization in simulating real-life queueing systems. Such simulations are valuable in operational research, service industry optimization, and understanding customer flow dynamics. Additionally, by adjusting parameters like customer arrival rates and transaction durations, one can explore various scenarios and optimize the bank's staffing and service procedures for improved performance.

References

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