This is my second post on politics this month (this month’s INFORMS blog challenge–my first post is about snow removal).  There are few political topics that invoke an emotional response as strongly as does K12 public education.  My daughter started attending the public school system this year, and I have been surprised at how, well, political school is.  But given the budget cuts over the past few years, some of that is understandable.

I learned the bus route that my daughter would take before the school year began.  My first reaction was to wonder if the bus routes were optimized (what else would my reaction be?).  Designing bus schedules isn’t rocket science, but it can be haphazard, leading to kids spending extra time on the bus, wasted gas, and late bus drivers.

A quick search on the web indicates that bus route scheduling is quite the political issue.

• A multiobjective optimization model that balances efficiency and equity.  The approach first creates routes by grouping students into clusters using a multi-objective districting algorithm, and then optimizes each group of students using set covering and traveling salesman problem algorithms.  The objectives include minimizing the number of buses, minimizing the fleet travel times, balancing the work loads among the buses, and minimizing student walking distance.
• A multiobjective optimization model for creating bus routes for kindergarten in Hong Kong
• An optimization model for generating bus routes for New York City schools with the objective of reducing the number of buses.
• A recent review in EJOR summarizes school bus route selection.  They write that routing school buses can address the following issues in various sub-problems: “data preparation, bus stop selection, bus route generation, school bell time adjustment, and bus scheduling.”

In my neck of the woods, I could probably identify near-optimal solutions without having to build a model (there are a number of small, isolated subdivisions with low road connectivity, which makes routing and bus stop selection a breeze).  But other bus routing scenarios are more complex, either due to the sheer size of the school system, the density and layout of neighborhoods, or not-so-simple school boundaries.

One feature that makes bus routing tough are “fair” policies for allocating students to schools that use lotteries to let parents select their schools: any child could attend any school so buses for every school could pass through a single neighborhood.  Good bus routes become much harder to identify (unless OR is used!), and regardless, students would have to spend more time on the bus as the distance to school increases.

Michael Johnson and and Karen Smilowitz’s excellent TutORial on Community Based Operations Research contains a brief overview about allocating public education resources.  In addition to school bus routing, operations research has been used to

• design recommendations for school closures in a region that reflect socio-economic characteristics of the students in different areas of the region.
• develop forecasting models for school attendance as input to optimization models for locating public-school buildings and setting attendance boundaries.
• use data envelopment analysis (DEA) that uses school performance observations to guide secondary schools for ways to improve their performance.

Have you seen OR used for public education?

Related posts:

• snow removal using a shovel, a plow, and operations research
  

This month’s INFORMS blog challenge has the topic of politics.  I’ve written about politics frequently before, mainly as it relates to elections.  I have also written about voting systems as they relate to politics, the Oscars, and the Olympics.  Politics is a broad topic, but I’ll write about something I haven’t before: snow removal.  After all, I am at home during yet another snow day (I live in Virginia:  1″ of snow is enough to paralyze the entire city.  My Midwestern sensibilities do not understand this).

Mike Trick wrote a wonderful post about snow removal last year.  I won’t duplicate his efforts, but I will note that James Campbell from University of Missouri-St. Louis has written several articles about OR and snow removal as has Gene Woolsey.  Snow removal can be formulated as an optimization model using generalized assignment and partitioning problems.

Another way to optimally remove snow is by using OR to influence urban planning.  In many states, including my state of Virginia, new laws limit the number of cul-de-sacs that can be built in new neighborhoods.  Cul-de-sacs and neighborhoods with few entrances and exits cause many problems: they create traffic bottlenecks and accidents, increase ambulance response times, and increase the time for snow removal.  Models that can relate neighborhood design to the cost of providing public services are valuable for removing snow more efficiently for decades to come.

The politics of snow removal are interesting to me.  As a Chicagoland native, I grew up used to seeing buckets of salt dumped on the road before every storm and plows quickly responding to blizzards.  When I was older, I was surprised to learn that most of the country does not have such great public services.  My parents told me why:  the blizzard of 1979–when more than 88″ of snow was dumped on the city–was so mismanaged that the mayor of Chicago lost his reelection bid.  A good snow removal plan that used OR would have reduced or eliminated the public backlash.  We still see political fallout after blizzards, such as the controversy surrounding the EMS response in New York during the December blizzard.  On the other hand, Newark’s major Cory Booker was praised by just about every news outlet for personally digging citizens out of the snow.  Of course, this is not a very efficient method for removing snow, but sometimes appearances matter more than efficiency.

Related posts:

• *vote* a post about election-oriented politics