In [1] a simple optimization model is presented for the scheduling of patients receiving cancer treatments. [2] tried to use this model. This was not so easy: small bugs in [1] can make life difficult when replicating things.

We use the data set in [2]:

- There are \(T=40\) time slots of 15 minutes
- We have 23 chairs where patients receive their treatment
- We have 8 different types of patients
- Each patient type has a demand (number of actual patients) and a treatment length (expressed in 15 minute slots)
- There is a lunch break during which no patients can start their treatment
- We want at most 2 treatment sessions starting in each time slot.

Patient Data |

#### Main variables

A treatment session is encoded by two binary variables: \[\mathit{start}_{c,p,t} = \begin{cases} 1 & \text{if session for a patient of type $p$ starts in time slot $t$ in infusion chair $c$} \\ 0 & \text{otherwise} \end{cases}\] \[\mathit{next}_{c,p,t} = \begin{cases} 1 & \text{if session for a patient of type $p$ continues in time slot $t$ in infusion chair $c$} \\ 0 & \text{otherwise} \end{cases}\]

Start and Next variables |

**start**variables are colored orange and the

**next**variables are grey. Patient type 1 has a treatment session length of 1. This means a session has a

**start**variable turned on, but no

**next**variables. Patient type 2 has a length of 4. So each session has one

**start**variable and 3

**next**variables with values one.

Note that there are multiple patients of type 1 and 2.

#### Equations

A chair can be occupied by zero or one patients: \[\sum_p \left( \mathit{start}_{c,p,t} + \mathit{next}_{c,p,t}\right)\le 1 \>\forall c,t\]

When \(\mathit{start}_{c,p,t}=1\) we need that the next \(\mathit{length}(p)-1\) slots have \(\mathit{next}_{c,p,t'}=1\). Here the paper [1] makes a mistake. They propose to model this as: \[\sum_{t'=t+1}^{t+\mathit{length}(p)-1} \mathit{next}_{c,p,t'} = (\mathit{length}(p)-1)\mathit{start}_{c,p,t}\>\>\forall c,p,t\] This is not correct: this version would imply that we have \[\mathit{start}_{c,p,t}=0 \Rightarrow \sum_{t'=t+1}^{t+\mathit{length}(p)-1} \mathit{next}_{c,p,t'}= 0\] This would make a lot of slots just unavailable. (Your model will most likely be infeasible). The correct constraint is:\[\sum_{t'=t+1}^{t+\mathit{length}(p)-1} \mathit{next}_{c,p,t'} \ge (\mathit{length}(p)-1)\mathit{start}_{c,p,t}\>\>\forall c,p,t\] A dis-aggregated version is: \[\mathit{next}_{c,p,t'} \ge \mathit{start}_{c,p,t} \>\>\forall c,p,t,t'=t+1,\dots\,t+\mathit{length}(p)-1\] This may perform a little bit better in practice (although some solvers can do such a dis-aggregation automatically).

It is noted with this formulation, we only do \[\mathit{start}_{c,p,t}=1 \Rightarrow \mathit{next}_{c,p,t'}=1\] If \(\mathit{start}_{c,p,t}=0\), we leave \(\mathit{next}_{c,p,t'}\) unrestricted. This mean we have some \(\mathit{next}\) variables just floating. They can be zero or one. Only the important cases are bound to be one. This again means that the final solution is just \(\mathit{start}_{c,p,t}\), and we need to reconstruct the \(\mathit{next}\) variables afterwards. This concept of having variables just floating in case they do not matter, can be encountered in other MIP models.

To meet demand we can do:\[\sum_{c,t} \mathit{start}_{c,p,t} = \mathit{demand}_p\]

Finally, lunch is easily handled by fixing \[\mathit{start}_{c,p,t}=0\] when \(t\) is part of the lunch period.

There is one additional issue: we cannot start a session if there are not enough time slots left to finish the session. I.e. we have: \[\mathit{start}_{c,p,t}=0\>\>\text{if $t\ge T - \mathit{length}(p)+2$}\]

#### GAMS model

The data looks like:

setc 'chair' /chair1*chair23/p 'patient type' /patient1*patient8/t 'time slots' /t1*t40/lunch(t) 'lunch time' /t19*t22/; alias(t,tt);table patient_data(p,*)demand length patient1 24 1 patient2 10 4 patient3 13 8 patient4 9 12 patient5 7 16 patient6 6 20 patient7 2 24 patient8 1 28 ; scalar maxstart 'max starts in period' /2/ ;parameterdemand(p) length(p) ; demand(p) = patient_data(p,'demand'); length(p) = patient_data(p,'length'); |

We create some sets to help us make the equations simpler. This is often a good idea: sets are easier to debug than constraints. Constraints can only be verified when the whole model is finished and we can solve it. Sets can be debugged in advance. In general, I prefer constraints to be as simple as possible.

setstartok(p,t) 'allowed slots for start'after(p,t,tt) 'given start at
(p,t), tt are further slots needed (tt = t+1..t+length-1)'; startok(p,t) = ord(t)<=card(t)-length(p)+1;startok(p,lunch) = no;after(p,t,tt) = startok(p,t) and (ord(tt)>=ord(t)+1) and (ord(tt)<=ord(t)+length(p)-1); |

The set

**startok**looks like

Set startok |

The set

**after**is a bit more complicated:

set after |

**next**variable needs to be turned on. E.g. when patient type 5 starts a treatment session in period 1, the

**next**variables need to be one for periods 2 through 16. At the bottom we see again the effect of the lunch period.

The optimization model can now be expressed as:

binary variablesstart(c,p,t) 'start: begin treatment'next(c,p,t) 'continue treatment'; variable z 'objective variable';start.fx(c,p,t)$( not startok(p,t)) = 0;equationsobj 'dummy
objective: find feasible solution only'slots(c,p,t) 'start=1 =>
corresponding next = 0,1'slots2(c,p,t,tt) 'disaggregated version'chair(c,t) 'occupy once'patient(p) 'demand equation'starts(t) 'limit starts in each slot'; * dummy objectiveobj.. z =e= 0; * aggregated versionslots(c,startok(p,t)).. sum(after(p,t,tt), next(c,p,tt)) =g= (length(p)-1) * start(c,p,t);* disaggregated versionslots2(c,after(p,t,tt)).. next(c,p,tt) =g= start(c,p,t); * occupation of chairchair(c,t).. sum(p, start(c,p,t) + next(c,p,t)) =l= 1;* demand equationpatient(p).. sum((c,t),start(c,p,t)) =e= demand(p);* limit startsstarts(t).. sum((c,p),start(c,p,t)) =l= maxstart;model m1
/slots,chair,patient,starts,obj/;model m2
/slots2,chair,patient,starts,obj/;solve m1
minimizing z using mip;display
start.l; |

I try to make the results more meaningful:

parameter results(*,t) 'reporting';start.l(c,p,t) = round(start.l(c,p,t)); loop((c,p,t)$(start.l(c,p,t)=1),results(c,t) = ord(p);results(c,tt)$after(p,t,tt) = - ord(p);); results('starts',t) = sum((c,p),start.l(c,p,t)); |

We only use the

**start**variables. We know that some of the

**next**variables may have a value of one while not being part of the schedule. The results look like:

Results |

The colored cells with positive numbers correspond to a start of a session. The grey cells are patients occupying a chair for the remaining time after the start slot. We see that each period has two starts, except for lunch time, when no new patients are scheduled.

This model solves very quickly: about half a second.

#### Proper Next variables

In this model we only use the

**start**variables for reporting. The

**next**variables can show spurious values \(\mathit{next}_{c,p,t}=1\) which are not binding. Can we change the model so we only have valid

**next**variables?

There are two ways:

- Minimize the sum of
**next**variables: \[\min \sum_{c,p,t} \mathit{next}_{c,p,t}\] Surprisingly this made the model much more difficult to solve. - We know in advance how many
**next**variables should be turned on. So we can add the constraint:\[\sum_{c,p,t} \mathit{next}_{c,p,t} = \sum_p \mathit{demand}_p (\mathit{length}(p)-1) \] This will prevent these floating**next**variables.

#### A better formulation

We can remove the

**next**variables altogether and use the**start**variables directly in the constraint that checks the occupation of chairs: \[ \sum_p \sum_{t'=t-\mathit{length}_p+1}^t \mathit{start}_{c,p,t'} \le 1 \> \forall c,t\] In GAMS we can model this as follows. We just need to change the set**after**a little bit: we let**tt**in**after(p,t,tt)**include**t**itself. Let's call this set**cover**:setstartok(p,t) 'allowed slots for start'cover(p,t,tt) 'given start at (p,t), tt
are all slots needed (tt = t..t+length-1)'; startok(p,t) = ord(t)<=card(t)-length(p)+1;startok(p,lunch) = no;cover(p,t,tt) = startok(p,t) and (ord(tt)>=ord(t)) and (ord(tt)<=ord(t)+length(p)-1); |

Note again that the only difference with our earlier set

**after(p,t,tt)**is that

**after**has (

**ord**(tt)>=

**ord**(t)+1) while

**cover(p,t,tt)**has a condition: (

**ord**(tt)>=

**ord**(t)). One obvious difference between the sets

**after**and

**cover**is the handling of patient type 1.

**After**did not have entries for this patient type, while

**cover**shows:

Set cover has a diagonal structure for patient type 1 |

With this new set

**cover**we can easily form our updated constraint

**chair**:

* occupation of chairchair(c,t).. sum(cover(p,tt,t), start(c,p,tt)) =l= 1; |

This will find all variables

**start(c,p,tt)**that potentially cover the slot

**(c,t)**. Here we see how we can simplify equations a lot by using well-designed intermediate sets.

#### Minimize number of chairs

We can tighten up the schedule a little bit. There is enough slack in the schedule that we actually don't need all chairs to accommodate all patients. To find the minimum number of chairs we make the following changes to the model: first we introduce a new binary variable

**usechair(c)**. Next we change the equations:

* objectiveobj.. z =e= sum(c, usechair(c));* occupation of chairchair(c,t).. sum(cover(p,tt,t), start(c,p,tt)) =l= usechair(c);* chair orderingorder(c-1).. usechair(c) =l= usechair(c-1); |

The last constraint says \(\mathit{usechair}_c \le \mathit{usechair}_{c-1}\) for \(c\gt 1\) (this last condition is implemented by indexing the constraint as order(c-1)). The purpose of this constraint is two-fold: (1) reduce symmetry in the model which hopefully will speed up things, and (2) make the solution better looking: all the unused chairs are at the end. With this, an optimal schedule looks like:

Minimize number of chairs |

#### Multi-objective version

We can try to make the schedule more compact: try to get rid of empty chairs in the middle of the schedule. An example of such a "hole" is cell:

**(chair6, t12)**. We do this by essentially solving a bi-objective problem:

- Minimize number of chairs needed
- Minimize spread

- Solve a weighted sum objective \(w_1 z_1 + w_2 z_2\) with a large weight on \(z_1\) being the number of chairs used,
- Solve in two steps:
- Solve number of chairs problem
- Fix number of chairs to optimal value and then solve minimum spread model.

Bi-objective model results |

Update: I added paragraphs about the suggested formulations in the comments.

#### References

- Anali Huggins, David Claudio, Eduardo PĂ©rez, Improving Resource Utilization in a Cancer Clinic: An Optimization Model, Proceedings of the 2014 Industrial and Systems Engineering Research Conference, Y. Guan and H. Liao, eds., https://www.researchgate.net/publication/281843060_Improving_Resource_Utilization_in_a_Cancer_Clinic_An_Optimization_Model
- Python Mixed Integer Optimization, https://stackoverflow.com/questions/51482764/python-mixed-integer-optimization