4. Customers arrive with EXPO(5) interarrival times at a service system with a single server. Service time is EXPO(4.25). All times are in minutes. Although the waiting queue has an infinite capacity, each arriving customer has a balking tolerance (with triangular distribution of parameters 3, 6 and 15). If the number in queue is greater than his balking tolerance, the customer will balk away from the system. Those customers that do not balk are only willing to wait for a limited period of time before they renege from the queue. The renege time tolerance is distributed ERLA(15,2). Customers who have reached their renege time but are within their stay zone (in front of the queue) will stay in the line anyway until they receive service. The stay zone can be modelled as POIS(0.75). Develop a model for this system, using ARENA-like modelling constructs. One should be able to directly translate your developed model into ARENA software and run it. The objective is to collect statistics on the number in queue at the server, server utilisation, and cycle times for customers who balk, customers who renege, and customers who complete their service. (20 marks) 4. Customers arrive with EXPO(5) interarrival times at a service system with a single server. Service time is EXPO(4.25). All times are in minutes. Although the waiting queue has an infinite capacity, each arriving customer has a balking tolerance (with triangular distribution of parameters 3, 6 and 15). If the number in queue is greater than his balking tolerance, the customer will balk away from the system. Those customers that do not balk are only willing to wait for a limited period of time before they renege from the queue. The renege time tolerance is distributed ERLA(15,2). Customers who have reached their renege time but are within their stay zone (in front of the queue) will stay in the line anyway until they receive service. The stay zone can be modelled as POIS(0.75). Develop a model for this system, using ARENA-like modelling constructs. One should be able to directly translate your developed model into ARENA software and run it. The objective is to collect statistics on the number in queue at the server, server utilisation, and cycle times for customers who balk, customers who renege, and customers who complete their service. (20 marks)