Why does pooling multiple queues typically reduce total average waiting time?

Question

BlackTom AI · Accepted Answer

When considering why pooling multiple queues often lowers overall average waiting time, it helps to look at how variability and resource sharing affect system performance.
Option 1: 'It reduces the total arrival rate.' Pooling does not change how many customers arrive to the system; arrivals in aggregate remain the same. The benefit comes from how those arrivals are served, not from fewer arrivals.
Option 2: 'It better manages variability in the system by sharing customers across servers.' This is the core idea: pooling buffers the impact of bursty or uneven arrivals by distributing workload among multiple servers, smoothing service times and reducing the likelihood that all servers are idle or all are busy at the same moment. By spreading work, the system can respond more steadily to fluctuations, lowering wait times.
Option 3: 'It increases flow time at each server.' In many pooling scenarios, flow time can actually decrease or stay similar due to shared load and fewer bottlenecks; claiming an automatic increase is incorrect since pooling often reduces wait and total time in the system.
Option 4: 'It creates specialized workers.' Pooling queues focuses on distributing customers, not on creating or requiring specialized labor; specialization is not a direct consequence of pooling and is not necessary for the observed waiting-time benefit.
Option 5: 'It doubles total capacity.' Pooling queues does not magically increase total server capacity; capacity is determined by the number of servers and their speeds, not by pooling alone. The benefit arises from improved utilization and variability reduction, not from a capacity doubling.

Why does pooling multiple queues typically reduce total average waiting time?

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Pooling systems increase waiting time because customers wait longer for a server to become available.

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