Optimizing Travel
Process optimization can initially seem overwhelming due to the many facets involved. However, if each aspect is broken down, it can actually become a simple
system. Below is a simple example of the use of process
optimization. The situation is a common commute from work to home
during peak driving hours. The current method is leaving work and
driving home at 5:00 in the afternoon. While this method wastes no
time between leaving work and driving home, it presents the issue of encountering traffic and all of the
troubles associated. Therefore, the method of process optimization
can be applied in order to determine the most productive situation.
The first step in process optimization is problem identification.
The actual problem must be identified before attempting to address it. Basically, it is necessary to know what is being looked for before attempting
to look. In the case of the commute, the situation is a rush hour
commute. The question involved is how the commute can be
improved. In order to address this question, possibilities must be
proposed and individually validated based on qualifications. This
will result in the identification of the problem. Below are several
suggestions and their supporting or opposing factors:
Can the traffic be changed?
-At 5:00, traffic will be present at all routes
Can an alternative transportation method be
used?
-Due to the distance and number of passengers, a family-size car is the only choice
Can an alternative route be used?
-All routes possible will have traffic
Can an alternative leaving time be
made?
-Cant leave earlier but can leave later
From these choices, it can be evaluated that the only
possible solution is to change the time of leave. Therefore, the
problem has been identified to be what the best time to leave is.
With the problem identified, important test factors have to be assigned to evaluate the best
time. The idea is to leave at the optimal time. However, it must be stipulated as to what the optimal time is in regards
to. Possible considerations are:
-time loss
-vehicle wear
-accident risk
-travel costs
-amount of productivity
While it would be ideal to consider all of these matters, it would be very difficult to focus on each one at the
same time. Therefore, they must be ranked based on importance and
manipulability. The most important three in this case will be time
loss, travel costs, and vehicle wear in that order. Also, factors
which will be affected in each case also need to be defined. While
time loss is almost completely governed with the leave time, travel costs and vehicle wear can be affected by
three factors: breaking, acceleration, and engine temperature. The
amount and intensity of each factor can be evaluated in order to assign values to travel cost and vehicle
wear. The definition of these factors outlines the testing
parameters required for scenario evaluation.
The next step in process optimization for this situation is data collection. Using the outlined parameters, a series of data points are taken in
correlation to varying leave times. This data is taken over a
specified amount of time which is determined statistically. The
data is then tabled and displayed graphically as well as evaluated using various statistical
approaches. In this case, a visual table will be used to evaluate
the times.
As seen in the figure, there are multiple points where peaks are present. Using the order of importance, a peak is identified which represents the
optimal leave time. This concludes the determination of leave
time. While this is only one aspect of the drive, many other
factors can be analyzed during the commute in order to perform a more thorough process
optimization. Factors include speed during the drive, weight
present in the car, and various internal conditions. In
realistic situations, more aspects of the process must be consulted in order to accomplish a full process
optimization.
See Travel Time Optimization Write-Up
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