Tag Archives: python

Using column generation to minimize production waste

Minimizing production waste can result in significant cost savings. However, calculating just the right production configuration can be a tedious task.

Let’s take the example of a firm which delivers sheet metal. The sheets it delivers are all of the same width but can differ in length. The raw material used by the company are metal sheets of 3 meters wide by 20 meters long. Clients can order any length metal sheets (as long as it doesn’t exceed 20 meters).

Let’s say there is demand for the following lengths of sheet metal (all are 3 meters wide). The number of demands are denoted in the next line. E.g. for 40 items of length 6 have to be produced, 5 items of length 9 have to be produced and so on.

items_demands
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December 11, 2012Permalink Leave a comment

Estimating required hospital bed capacity

Estimating required hospital bed capacity requires a thorough analysis. There are a lot of ways of approaching a capacity requirement problem, but I think we can agree that a simple spreadsheet analysis just won't cut it.

The approach described in this post makes use of discrete-event simulation and, just to clarify, makes abstraction from a lot of variables which should be taken into consideration in a real-life analysis.

To explain the approach, the following case will be used:

An emergency department of a small regional hospital receives complains about its emergency admission capacity. After investigation of its admission data it becomes clear that their service level is not up to par with that of other hospitals. Therefore, plans for the redesign of the emergency department and an investment in its emergency bed capacity are presented. The plan proposes a new bed capacity of 12 beds (coming from a previous of 10 beds). The Chief of Medicine wants to know what the effect of this investment will be on their emergency admission service level.

The emergency department has recorded data on the interarrival times of patients that are admitted (or should be admitted) to an emergency bed. The following graph shows the interarrival distribution (triangular: mode=5, min=.1, max=12):

plot of chunk unnamed-chunk-1

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July 23, 2012Permalink Leave a comment

Using discrete-event simulation to simulate hospital processes

Discrete-event simulation is a very useful tool when it comes to simulating alternative scenario’s for current of future business operations. Let’s take the following case;

Patients of an outpatient diabetes clinic are complaining about long waiting times, this seems to have an adverse effect on patient satisfaction and patient retention. Today, one doctor runs the outpatient clinic. Hospital management has decided that in order to increase patient retention rates, a nurse will be hired to assist and take over some of the doctor’s tasks. This should result in decreased waiting times and, hopefully, increased patient retention rates.

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July 12, 2012Permalink Leave a comment