Pywfplan is a Python library that helps planning the activity of a call center.

It is based on a C++ application I wrote a few years ago that has been used in production to plan call centers of sizes between 50 to 200 agents.

Pywfplan is a stripped down version of that application that retains the basic functionality and exports it as a Python module.

Ensure that you have a C++ compiler and the boost library installed, then run

python setup.py install

Or use the provided docker file:

docker build -t pywfplan .
docker run -p 8888:8888 -it pywfplan

Suppose you are given the following

• a list of agents
• a list of shift definitions
• a staffing curve that tells how many agents must be at work at a given time
• some more requirements (for example about specific contract types or about how rests are assigned)

and you are asked to produce an optimal plan for one week that obeys the requirements. That is you have to assign a shift to every agent for each day.

The plan should be optimal in the sense that at any given time there should be just enough agents at work to ensure that calls are being answered but no more than them and of course at no time there should be less than the minimum number of agents required.

In pywfplan the Shift class provides a simple notation to encode shift and requirements:

# 4 hours shifts
A1 = Shift.fromSpec("A1", "09:00-12:00")
A2 = Shift.fromSpec("A2", "09:30-12:30")
A3 = Shift.fromSpec("A3", "10:00-14:00")
...
# 8 hours shifts with a pause for lunch
M1 = Shift.fromSpec("M1", "09:00-12:00,13:00-17:00")
M2 = Shift.fromSpec("M2", "09:30-12:30,13:30-17:30")
M3 = Shift.fromSpec("M3", "10:00-13:00,14:00-18:00")
...
# a rest shift
R =  Shift.fromSpec("R")

then shifts can be composed with + and * to define complex assignments:

# assign either A1 or A2 or A3 on a single day
W = A1 + A2 + A3

# assign A1 on day 1, A2 on days 2-5 and R on day 7
W = A1 * A2 * A2 * A2 * A2 * A2 * R

# assign a 4 hours shift on days 1-5, a 8 hour shift on day 6 and rest on day 7
A = A1 + A2 + A3
M = M1 + M2 + M3
W = A * A * A * A * A * M * R

The StaffPlanner class is used to produce the optimal plan:

planner = StaffPlanner()

you set the target staffing curve

planner.setStaffingTarget(target, days=7)

for each agent you specify its assignment rule

# this agent works 4 hours from mon to fri
W = A1 + A2 + A3
rule = W * W * W * W * W * R * R

planner.addAgentRule(agent_code, rule)

the optimization is run with

planner.run()

and the optimal plan for each agent is retrieved with

agent_plan = planner.getAgentPlan(agent_code)

Internally pywfplan converts the assignment rule into a finite state machine that can be used to sample all the valid plans for that agent, for an example you can look here.

The planner then uses simulated annealing to minimize the average squared difference between the sampled plan staffing and the target staffing curves.