Technically, if your model is an Integer Programming model then you need an algorithm for finding a feasible solution.

If this is the case then this paper might help

https://www.jstor.org/stable/3690518

A Simplicial Approach to the Determination of an Integer Point of a Simplex

Chuangyin Dang and Hans Van Maaren, Mathematics of Operations Research, Vol. 23, No. 2 (May, 1998), pp. 403-415

Good luck

I appreciate you taking the time to give me some advice.

Hello! I'd try to share my insight about the production scheduling of an open-pit mine by genetic algorithm, as well as how to convert infeasible sequencing to a feasible one.

First, let's start with some background on genetic algorithms. Genetic algorithms are a type of optimization algorithm that are inspired by the process of natural selection. They are often used to solve complex problems where traditional optimization techniques may not be effective. In the case of an open-pit mine production scheduling problem, a genetic algorithm can be used to find an optimal sequence of activities, such as drilling, blasting, and hauling, that maximizes production while minimizing costs.

One challenge with using a genetic algorithm is that the initial solution generated may not be feasible. This means that the sequence of activities may violate certain constraints, such as limited resources or production capacity. To convert an infeasible sequencing to a feasible one, there are several approaches that can be taken.

One approach is to create a normalized plan based on a completely random solution. This involves generating a completely random solution and then adjusting it to meet certain constraints. For example, if the random solution exceeds production capacity for certain activities, those activities can be rescheduled to a different time period to ensure that production capacity is not exceeded. This approach can be effective for small to medium-sized problems, but may not be feasible for larger problems due to the sheer number of possible solutions.

Another approach is to change the time periods that are assigned to genes of the chromosomes or all blocks of the ultimate pit. This involves adjusting the sequence of activities by shifting them forward or backward in time. For example, if a certain activity is scheduled to take place at a time when resources are limited, it may be possible to shift that activity to a different time period when resources are more plentiful. This approach can be effective for larger problems, but may require more computational resources and time.

Overall, the key to converting an infeasible sequencing to a feasible one is to carefully analyze the constraints and identify which activities are causing the violation. From there, it may be possible to adjust the sequencing to ensure that all constraints are met while still maximizing production and minimizing costs.