Skip to main content

"Call Scheduling Algorithms" = Call Scheduling Algoritms + Staffing

We think about call scheduling algorithms as a set of rules about when cases should be called. However, staffing is the other half of the problem. For the rules to be implemented, the staff making the calls need to be there. And, there can also be issues if the staff is too large. The rules need to account for both of these situations.

Probably the more difficult problem is a staff that is too large. For example, imagine that all active cases have been called. There is an appointment in 45 minutes. The interviewer can wait, or call cases that have already been called on this shift. Calling cases again would be inefficient and a violation of a rule of the algorithm. Still, it seems bad to not make he calls.

I wrote a paper on a call scheduling algorithm. I assigned a preferred calling window to each case. These windows changed over time as calls were placed and the results of previous calls were used to inform the assignment of preferred window. I spent a lot of time analyzing data to see how often calls were placed in the preferred window.

This issue comes up in the paper by Greenberg and Stokes. Their algorithm recommended placing 30% of the calls on the first night of the study. This is likely infeasible as a scheduling problem.

In any event, call scheduling is a complicated problem. The complications, I think, stem in part from the difficulty of implementing staffing that will match the rules of the calling. And extending results across organizations with different approaches to staffing is difficult. In evaluating these algorithms, we need to know the rules, the violations of the rules, and the results.

Comments

Popular posts from this blog

Tailoring vs. Targeting

One of the chapters in a recent book on surveying hard-to-reach populations looks at "targeting and tailoring" survey designs. The chapter references this paper on the use of the terms among those who design health communication. I thought the article was an interesting one. They start by saying that "one way to classify message strategies like tailoring is by the level of specificity with which characteristics of the target audience are reflected in the the communication." That made sense. There is likely a continuum of specificity ranging from complete non-differentiation across units to nearly individualized. But then the authors break that continuum and try to define a "fundamental" difference between tailoring and targeting. They say targeting is for some subgroup while tailoring is to the characteristics of the individual. That sounds good, but at least for surveys, I'm not sure the distinction holds. In survey design, what would constitute ...

"Responsive Design" and "Adaptive Design"

My dissertation was entitled "Adaptive Survey Design to Reduce Nonresponse Bias." I had been working for several years on "responsive designs" before that. As I was preparing my dissertation, I really saw "adaptive" design as a subset of responsive design. Since then, I've seen both terms used in different places. As both terms are relatively new, there is likely to be confusion about the meanings. I thought I might offer my understanding of the terms, for what it's worth. The term "responsive design" was developed by Groves and Heeringa (2006) . They coined the term, so I think their definition is the one that should be used. They defined "responsive design" in the following way: 1. Preidentify a set of design features that affect cost and error tradeoffs. 2. Identify indicators for these costs and errors. Monitor these during data collection. 3. Alter the design features based on pre-identified decision rules based on ...

What is Data Quality, and How to Enhance it in Research

  We often talk about “data quality” or “data integrity” when we are discussing the collection or analysis of one type of data or another. Yet, the definition of these terms might be unclear, or they may vary across different contexts. In any event, the terms are somewhat abstract -- which can make it difficult, in practice, to improve. That is, we need to know what we are describing with those terms, before we can improve them. Over the last two years, we have been developing a course on   Total Data Quality , soon to be available on Coursera. We start from an error classification scheme adopted by survey methodology many years ago. Known as the “Total Survey Error” perspective, it focuses on the classification of errors into measurement and representation dimensions. One goal of our course is to expand this classification scheme from survey data to other types of data. The figure shows the classification scheme as we have modified it to include both survey data and organic f...