The Doctor Rostering Problem - Asser Fahrenholz

Chapter 4. Solving the DRP 30
Simulated Annealing
The Simulated Annealing implementation is based on algorithm 4.4 on page 26 and is
included in appendix A.4.
4.4.5 Implementation optimisations
As noted above, the two heuristics calculate the objective function value and feasibility
every time a neighborhood transformation has been applied. This presents an obvious
area for optimisation. One optimisation that did not make it into the implementation
is that of a delta function, which I describe in detail in chapter 8.
As one of the questions that spring to mind when developing an application could be:
What operations of this implementation are taking the most time?, I was fortunate to
be in possession of a tool to investigate this exact area. The Netbeans IDE 6.1 allows
profiling of the program, enabling the developer to investigate which methods are taking
the most time. When doing so, I found that the operation requiring the most time are
Hash Code checks. Hash Codes are 32 bit signed integers identifying objects by a single
value, found by the use of prime numbers and the values of the class-fields.
The new (optimised) hash code this lead to, can be seen in listing 4.1, where several
lines, now commented out, were previously calculated. Only the ID-number is really
needed to uniquely identify the doctor.
1 public class Doctor implements Comparable < Doctor >, Serializable {
2
.
3 @Override
4 public int hashCode () {
5 // final int prime = 31;
6 // int result = 1;
7 // result = prime * result + IDnumber ;
8 // result = prime * result + (( name == null ) ? 0 : name . hashCode ());
9 // result = prime * result + numberOfShifts ;
10 // result = prime * result + (( shifts == null ) ? 0 : shifts . hashCode ());
11 return IDnumber ;
12 }
13
.
14 }
Listing 4.1: The new hashcode method
The optimisation may not seem that big, but when the calculation of this hash code
is done once per doctor per shift per constraint, every time we need to calculate the
objective function value, it adds up!