When do Decisions Need to Happen?
Decisions, Information, and Decisions Environment
When should decisions be made? The most compelling, obvious answer may be “when complete affecting information is known about the subject.” However, this is seldom, if ever, the correct answer. Complete information is unlikely to be known. Information known, at the time the problem arises, will likely change before the decision is made due to external factors that are constantly changing in response to their own environments. Decisions timing is extremely sensitive and extremely important.
Uncertainty in Decisions
When a decision is made it has some degree of uncertainty attached. Decisions are information driven and subject to the interpretation and biases of the decision maker as well as the implementers and receptors of the decision. The degree of uncertainty injected into the decision making by the influences of the decision environment must be determined for good decision-making. It is never perfect! Decisions are not made in immunity from other associated or opposing decisions. A method of organizing and evaluating the many affecting factors is extremely useful and highly important. There is a field of study devoted to such a method called “Management Science.” It might also be referred to as “Operations Research.” Other names may also be ascribed to this study field. They all make use of supporting areas of study such as Statistics, Queuing Theory, Economics, Mathematics, Calculus, Simulation, etc.
Queue is another name for a waiting line which is a very good and practical problem to explore in this article. Queueing Theory involves the use of Statistics and the other fields above described, in addition to others, according to the waiting line complexity. We can all easily envision many uses for this method of data evaluation as a basis for decisions. We encounter the waiting line in the super market, restaurant, airline terminal, at the motor vehicle license counter, at the voting station, etc.
Airline Trip Decisions Example
Consider a trip by airplane! You will be subject to many queues by the time the trip is over. Resolution of queueing decisions throughout your journey represent a very complex queueing system. First, you waited for a ticket agent to issue a ticket. Then, the agent joined a queue to determine if the flight you wanted has an available, appropriate seat. After buying your ticket, you had to wait in line to check in at the gate where the flight was boarding. Next, you waited to board; and once on board, your plane joined the queue of planes waiting to use the runway for takeoff. Eventually, the plane circled the destination airport, waiting for a runway and instruction to land. Upon landing, the plane may have had to wait for an unloading gate; and then, you had to wait to deplane. Finally, you joined the line to pick up your luggage, and then another line for transportation. The factors involved for each of these steps (queues) had to be determined using various methods – some simple, but most complex.
Queueing Theory Decisions
The intended point of the above example is that the occasions for applying queueing theory to common, everyday problems and decisions are numerous and varied. When systems are designed that involve queues, queueing theory or digital simulation are very often used to estimate expected waiting times, queue lengths, and so on. This results in members of the queues spending less time waiting in line. It is very easy to extend this problem treatment to restaurants, concert venues, ball stadiums, etc. It also has many more, more complex applications.