Objectives assumptions

Each objective will be inserted into the optimisation model. Within the model, there are 2 options available:

1: Set goals as mandatory;

This can be done under the assumptions section of the Full SOA template

If the goals are set to mandatory, then the optimisation engine is forced to satisfy each goal within the model. For example, assume the following goal:

Under this scenario, an expense will be set up within the cash flow model for the year 2020.

The optimisation model will be forced to meet this goal. Therefore, if cashflow is not readily available the engine may be triggered to implement an alternative decision to raise cash (such as selling an existing asset). Alternatively, debt will need to be created in order to satisfy this objective and ensure the optimisation model remains feasible.

Problems arise with this approach when it is not possible to meet all of the stated objectives. As each goal is forced to occur in the model, there is no method to address competing goals. Consequently, goals that are an expense item such as buying a new car, going on holiday, paying for private school education, may jeopardise the ability to meet longer term goals such as preferred retirement age or meeting or living of a desired income in retirement.

Consequently, adopting this approach can result in the client/s running out of funds prior to expected life expectancy.

To avoid this issue, an adviser can adopt the second approach outlined below:

2: Set objectives as variables;

To set the goals as variables, the adviser must turn off the “Set objectives as mandatory” setting under assumptions.

If the goal is set to be a variable, each goal is set up as a variable within the optimisation problem. This means that the engine has the option of satisfying this goal but it is not required to satisfy the goal. This enables the engine to address competing goals. (see multiple objective optimisation.)

Using this method, there is no requirement to utilise the bad debt rounding account to maintain feasibility.

Alternatively, each goal is set up as a variable. If it is not possible to meet all of the specified goals, the optimisation engine can remain feasible and allocate resources to achieving the goals that are most important based on the client response to each goal:

Goals that are not achieved will receive a penalty on the objective function. For example, consider a scenario whereby we have resources of $55,000 in a given year and the following competing goals:

To achieve both goals in this scenario we would require $60,000 in resources. Given we only have $55,000 in resources the optimisation engine must make a decision on which goal will be satisfied.

We can see from our goals that Goal 1 is critically important, whereas Goal 2 is not important. Using the penalty system the model will apply a penalty factor of 8 for failing to achieve goal 1 and a penalty factor of 1 for failing to achieve goal 2 (review penalty assumptions for multi-objective optimisation here.)

In this scenario the engine will choose to satisfy goal 1, and partially satisfy goal 2. The penalty applied to the objective function from failing to satisfy goal 2 would be as follows:

To compare what would occur if the optimisation engine prioritised goal 2, the penalty applied to goal 1 would be as follows:

Therefore, in this scenario the engine will prioritise meeting goal 1 as it carries a penalty of $300,000 less ($800,000 – $500,000).

In practice, due to the significant penalties that apply for not meeting objectives the optimisation engine will strive to meet all objectives provided it is feasible to do so. In the event the optimisation problem is unable to achieve all goals, competing goals will be prioritised according to what is most important as per the above example.

To better understand the penalty system applied to objectives, (see multiple objective optimisation.)

It is important to note that some goals are considered binary, whereas some can be partially achieved. Advisers are able to specify whether they wish to set a goal as binary, otherwise PrimeSolve will use the following default settings: