#### The issue

In many models, we observe that an objective that**minimizes a sum**delivers undesirable results. Here are some examples:

__Assignment under preferences__

__In [1], we formulated a MIP model that tackles the problem of creating groups taking into account the preferences of people. When we just maximize the total sum of preferences that are honored by the assignment, we can see that some participants get a terrible assignment. I.e., taking one for the team. It requires some thought to develop a model that produces a more equitable solution. In a sense, we are looking at trading-off__

**overall efficiency**for

**fairness**.

__Job scheduling__

Typically in job scheduling models, we minimize (1) the total

**make-span**: do things as quickly as possible and (2)

**the sum of tardiness of jobs**. Sometimes we use a weighting scheme for tardy jobs: jobs from valuable clients may get a higher weight to make it less likely they are chosen to be tardy. There are some terms in the objective we can add, for instance:

- The number of jobs that are tardy. The sum may distribute the pain over many jobs. If we want to reduce the number of tardy jobs we can add a term that minimizes the number of tardy jobs.
- The maximum tardiness of a job. Adding such a term will try to prevent the situation where a single job will take the brunt of the pain.

These are examples where just

**minimizing the sum can lead to unpalatable solutions**. It may require some effort to design a more sophisticated objective that takes into account more aspects than just overall efficiency.

#### Example model

In [2] I discussed a MIP formulation for a matching problem:

Given \(N=2M\) points, find a matching that minimizes the sum of the cost (represented by lengths between the matched points).

### A. Single solution

In this section, I discuss how we can look at the problem in different ways:

- MIN SUM: Solve the problem minimizing the sum of the cost.
- MIN MAX: Solve the problem minimizing the maximum cost.
- MIN MAX followed by MIN SUM: this variant exploits that MIN MAX is not unique.
- MIN SUM followed by MIN MAX: due to uniqueness, this is not an interesting problem.
- A weighted sum of MIN SUM and MIN MAX.

#### A.1. MIN SUM Matching

The Min Sum problem can be stated as a MIP problem:

The optimal MIP solution looks like:

In the solution, we see a spike: the longest length is much larger than the rest.

MIP Model for Min Sum Matching |
---|

\[\begin{align}\min& \sum_{i,j|i \lt j} \color{darkblue}d_{i,j} \color{darkred}x_{i,j}\\ & \sum_{j|j \gt i} \color{darkred}x_{i,j} + \sum_{j|j \lt i} \color{darkred}x_{j,i} = 1 && \forall i\\ & \color{darkred}x_{i,j} \in \{0,1\} \end{align} \] |

The optimal MIP solution looks like:

Optimal MIN SUM solution |

In the solution, we see a spike: the longest length is much larger than the rest.

#### A.2. MIN MAX Problem

Instead of minimizing the sum, we can also minimize the length of the largest segment. Formally we want \[\min \max_{i\lt j} d_{i,j} x_{i,j}\] This can be linearized in a standard manner.

MIP Model for Min-Max Matching |
---|

\[\begin{align}\min\>& \color{darkred}z\\ & \color{darkred}z \ge \color{darkblue}d_{i,j} \color{darkred}x_{i,j} && \forall i \lt j\\ & \sum_{j|j \gt i} \color{darkred}x_{i,j} + \sum_{j|j \lt i} \color{darkred}x_{j,i} = 1 && \forall i\\ & \color{darkred}x_{i,j} \in \{0,1\} \end{align} \] |

Optimal MIN MAX solution |

We see we do much better than the maximum length of the MIN SUM problem.

One issue with the MIN MAX problem is that the solution is not unique. Any configuration with lengths less than the maximum length is optimal.

#### A.3. MIN MAX followed by MIN SUM

Instead of just using the MIN MAX objective, we can look at the problem as a multi-objective problem:

Multi-objective Model |
---|

\[\begin{align}\min \>& \color{darkred}z_1 = \max_{i,j|i \lt j} \color{darkblue}d_{i,j} \color{darkred}x_{i,j}\\ \min\>& \color{darkred}z_2 = \sum_{i,j|i \lt j} \color{darkblue}d_{i,j} \color{darkred}x_{i,j}\\ & \sum_{j|j \gt i} \color{darkred}x_{i,j} + \sum_{j|j \lt i} \color{darkred}x_{j,i} = 1 && \forall i\\ & \color{darkred}x_{i,j} \in \{0,1\} \end{align} \] |

Of course we need to reformulate the \(\max\) function to make it linear. The multi-objective structure was implemented as:

- Solve problem with the MIN MAX objective\[\begin{align} \min\>&z_1\\ &z_1 \ge d_{i,j} x_{i,j} &&\forall i\lt j \\ & \sum_{j|j \gt i} x_{i,j} + \sum_{j|j \lt i} x_{j,i} = 1 \\ &x_{i,j} \in \{0,1\} \end{align}\] Let \(z_1^*\) be the optimal objective.
- Solve problem with the MIN SUM, subject to MIN MAX objective is not deteriorating (i.e. the length of each segment is less than the maximum length found in the previous step). \[\begin{align}\min\>& z_2 = \sum_{i,j|i \lt j} d_{i,j} x_{i,j} \\ & d_{i,j} x_{i,j} \le z_1^* && \forall i\lt j\\ & \sum_{j|j \gt i} x_{i,j} + \sum_{j|j \lt i} x_{j,i} = 1 && \forall i\\ & x_{i,j} \in \{0,1\}\end{align}\]

This is typically called the

**lexicographic method**or

**preemptive method**.

The results look like:

Optimal multi-objective solution |

Here is a

**summary of the results**:

Model | Objective | Sum | Max |
---|---|---|---|

MIN SUM | 241.267 | 241.267 | 38.768 |

MIN MAX | 22.954 | 403.265 | 22.954 |

MULTI-OBJECTIVE (second solve) | 247.382 | 247.382 | 22.954 |

We can see that:

- The largest segment is very long for the MIN SUM model
- The sum is very large for the MIN MAX model.
- The MULTI model has a sum only a bit larger than from the MIN SUM model, and the largest length is identical to the MIN MAX model. It prevents the disadvantages of the MIN SUM and MIN MAX models.

#### A.4. MIN SUM followed by MIN MAX

The problem formed by first solving the MIN SUM problem followed by the MIN MAX model is not of much value: we get the same solution as the MIN-SUM problem. The reason is that the MIN-SUM solution is

**unique**. That means that running the second model does not yield a different solution.#### A.5. A weighted sum of MIN SUM and MIN MAX

An alternative to the preemptive or lexicographic method is to use a composite objective with some weights:

Multi-objective Model via Weighted Sum |
---|

\[\begin{align}\min \>& \color{darkblue}w_1 \color{darkred}z_1 + \color{darkblue}w_2 \color{darkred}z_2 \\ & \color{darkred}z_1 \ge \color{darkblue}d_{i,j} \color{darkred}x_{i,j} && \forall i,j| i \lt j \\ & \color{darkred}z_2 = \sum_{i,j|i \lt j} \color{darkblue}d_{i,j} \color{darkred}x_{i,j}\\ & \sum_{j|j \gt i} \color{darkred}x_{i,j} + \sum_{j|j \lt i} \color{darkred}x_{j,i} = 1 && \forall i\\ & \color{darkred}x_{i,j} \in \{0,1\} \end{align} \] |

The convention is we use \(w_i\gt 0\) and \(\sum_i w_i=1\). If we use\[\begin{cases} w_1 = 0.01 \\ w_2 = 0.99\end{cases}\] we get the same solution as in section

**A.3 MIN ABS followed by MIN SUM**.

### B. Finding All Efficient Solutions

In section A we looked at characterizing the "optimal" solution by a single point. When we have multiple objectives (min-sum and min-max), it can be insightful to looks at all "optimal" points or a representative subset. An optimal solution in this context can be defined as being non-dominated by another solution. Such a solution is called efficient. The collection of all efficient solutions is called the efficient frontier.

A first method is to vary the weights and solve for each weight. We don't want weights of zero (or one), so I generated the following step values:

#### B.1. Weighted sum loop

A first method is to vary the weights and solve for each weight. We don't want weights of zero (or one), so I generated the following step values:

---- 135 PARAMETER step step0 0.010, step1 0.050, step2 0.100, step3 0.150, step4 0.200, step5 0.250, step6 0.300 step7 0.350, step8 0.400, step9 0.450, step10 0.500, step11 0.550, step12 0.600, step13 0.650 step14 0.700, step15 0.750, step16 0.800, step17 0.850, step18 0.900, step19 0.950, step20 0.990

If we solve our

**weighted sum model**for these values, we get the following report:

---- 147 PARAMETER front w1 w2 sum max obj step0 0.010 0.990 247.382 22.954 25.199 step1 0.050 0.950 247.382 22.954 34.176 step2 0.100 0.900 247.382 22.954 45.397 step3 0.150 0.850 247.382 22.954 56.619 step4 0.200 0.800 247.382 22.954 67.840 step5 0.250 0.750 247.382 22.954 79.061 step6 0.300 0.700 247.382 22.954 90.283 step7 0.350 0.650 247.382 22.954 101.504 step8 0.400 0.600 247.382 22.954 112.726 step9 0.450 0.550 247.382 22.954 123.947 step10 0.500 0.500 247.382 22.954 135.168 step11 0.550 0.450 247.382 22.954 146.390 step12 0.600 0.400 247.382 22.954 157.611 step13 0.650 0.350 247.382 22.954 168.833 step14 0.700 0.300 247.382 22.954 180.054 step15 0.750 0.250 241.267 38.768 190.642 step16 0.800 0.200 241.267 38.768 200.767 step17 0.850 0.150 241.267 38.768 210.892 step18 0.900 0.100 241.267 38.768 221.017 step19 0.950 0.050 241.267 38.768 231.142 step20 0.990 0.010 241.267 38.768 239.242

This is a bit disappointing: we only observe two different solutions. And we saw these before.

We may miss some efficient (i.e., non-dominated) solutions. In general, the weighted sum method suffers from two problems:

- We may miss some efficient solutions. Although each point found with the weighted sum method is non-dominated, the other way around is not true: there may be efficient solutions we cannot see with this method.
- If this method finds several efficient points, these points may not be nicely distributed.

It is noted that in our simple loop we can miss efficient solutions for two reasons:

There is another method we can use.

We can actively look for non-dominated solutions as follows:

The model solved at each step \(K\) looks like:

- The inherent problem of the weighted sum model: it can miss solutions.
- The way we set up the loop: we only looked at 20 different weights. When we increase this, we may see more non-dominated solutions.

There is another method we can use.

#### B.2. Find non-dominated solutions

We can actively look for non-dominated solutions as follows:

- Start with arbitrary weights \(w_1, w_2 \in (0,1)\) to find a first solution on the efficient frontier. E.g. \(w_1 = w_2 = 0.5.\)
- Add to the model constraints on the objective values \(z_1, z_2\) such that a new solution is better in one of the objectives than any other found efficient solution
- Also, add a cut to the model that forbids the current solution.
- Solve. If infeasible, STOP. We are done.
- Go to step 2.

The model solved at each step \(K\) looks like:

MIP Model in search for non-dominated solutions |
---|

\[\begin{align}\min \>& \color{darkblue}w_1 \color{darkred}z_1 + \color{darkblue}w_2 \color{darkred}z_2 \\ & \color{darkred}z_1 \ge \color{darkblue}d_{i,j} \color{darkred}x_{i,j} && \forall i \lt j \\ & \color{darkred}z_2 = \sum_{i,j|i \lt j} \color{darkblue}d_{i,j} \color{darkred}x_{i,j}\\ & \sum_{j|j \gt i} \color{darkred}x_{i,j} + \sum_{j|j \lt i} \color{darkred}x_{j,i} = 1 && \forall i \\ & \color{darkred}z_1 \le \color{darkblue}z_1^{(k)} - \color{darkblue}\epsilon + \color{darkblue}M_1 \color{darkred}\delta && k=1,\dots,K-1\\ & \color{darkred}z_2 \le \color{darkblue}z_2^{(k)} - \color{darkblue}\epsilon + \color{darkblue}M_2 (1-\color{darkred}\delta) && k=1,\dots,K-1 \\ & \sum_{i,j|i\lt j} (2 \color{darkblue}x^{(k)}_{i,j}-1) \color{darkred}x_{i,j} \le \sum_{i,j|i\lt j} \color{darkblue}x^{(k)}_{i,j}-1 && k=1,\dots,K-1 \\ & \color{darkred}x_{i,j} \in \{0,1\} \\ & \color{darkred}\delta \in \{0,1\} \end{align} \] |

Here \(z^{(k)}\) and \(x^{(k)}\) indicate solutions found in earlier iterations. \(\epsilon\) is a small constant to make sure we are actually improving an objective. Good values for \(M\) can be found using our earlier optimization models. E.g.: \[\begin{align} &M_1 = 38.768 - 22.945 + \epsilon \\

& M_2 = 247.382 - 241.267 + \epsilon\end{align}\] The constraints \[\begin{align} &z_1 \le z_1^{(k)} - \epsilon + M_1 \delta\\ &z_2 \le z_2^{(k)} - \epsilon + M_2 (1-\delta)\end{align}\] say: one of the objectives should be (strictly) better compared to any other point we have found earlier. Finally, the constraint: \[\sum_{i,j|i\lt j} (2 x^{(k)}_{i,j}-1) x_{i,j} \le \sum_{i,j|i\lt j} x^{(k)}_{i,j}-1\] forbids any previously found point to be considered again.

When we run this algorithm, we find the following solutions:

---- 228 PARAMETER front2 sum max obj point1 247.38237 22.95441 135.16839 point2 243.76532 33.90486 138.83509 point3 241.26672 38.76785 140.01729

We see that

**point2**is a newly found efficient solution. In addition, we only need to solve 4 models (the last model, not shown in the results, is infeasible). This model is substantially more complicated than the previous loop with different weights. But we get something in return for this additional complexity: this algorithm produces all efficient points and depending on the number of non-dominated solutions, may need fewer models to solve.

#### Conclusion

In practical models, just what constitutes an optimal solution, is not always easy to define. An overall best solution may hurt some individuals disproportional. In our simple example, we saw a MIN SUM solution contained a few very bad matches. Trying to fix this with a multi-objective approach requires some thought. And this may give rise to new questions such as: give me all (or some subset) of the non-dominated solutions. This again is not an easy question.

#### References

- MIP vs Greedy Search, http://yetanothermathprogrammingconsultant.blogspot.com/2020/01/mip-vs-greedy-search.html
- Preferences in Assignments, http://yetanothermathprogrammingconsultant.blogspot.com/2020/01/preferences-in-assignments.html