Hi everyone,
I am a Ph.D. student and I am working on the production scheduling of an open-pit mine by genetic algorithm, and I have a problem with converting infeasible sequencing to a feasible one, so I need someone to help me. how can I create a normalized plan based on a completely random solution? on the other hand, how can I change the time periods that are assigned to genes of the chromosomes or all blocks of the ultimate pit?
I don't know any normalization method for obtaining a feasible sequencing plan, and I studied some papers related to it, but the normalization methods the authors used, have not been stated in detail! because when it is assigned a time period to blocks, there is any rule to regard the condition of 45 degrees on slopes!!!
I want to explain more about the MATLAB code I wrote. Precedence constraints (These constraints ensure that a block can only be extracted during some time period t if all of its predecessor blocks have been mined completely before or during time period t) are not true, I think because when I import the results into Surpac software, the extraction of blocks and sequencing of them seems like strip mining regardless of 45 degrees on slopes and instead of formation of inverse cones! I don't know how can I implement the normalization process you mentioned above?!
I attach an output file of MATLAB so you can see.
Thank you in advance
To convert an infeasible sequencing to a feasible one, you can consider the following steps:
Here are some suggestions for implementing the normalization process:
- For addressing the condition of 45 degrees on slopes, you can introduce additional constraints or modifications in your genetic algorithm. This could involve evaluating the slope angles of the blocks in the sequencing and adjusting them if they exceed the allowed limit.
- To handle the precedence constraints, you can incorporate additional logic in your genetic algorithm to ensure that blocks are mined only after their predecessors have been fully mined. This can be achieved by checking the sequencing and adjusting it if necessary.
- You can consider penalizing or modifying the fitness function of your genetic algorithm to encourage feasible solutions. This can help guide the algorithm towards generating solutions that satisfy the constraints.
The following steps can be taken into consideration while creating a workable sequencing plan for open-pit mine production scheduling using a genetic algorithm:
Random Solution Generation, Priority Restrictions, Feasibility Evaluation, Normalisation Process, Fitness Function, Genetic Algorithm Optimisation, Iterative Improvement, Termination Criteria, and Last Validation
Keep in mind that the specifics of putting these procedures into practise may differ depending on your particular condition and restrictions. To obtain understanding of the normalisation techniques that have been applied in related studies, it is crucial to thoroughly review the literature and available research papers. For more advice, you can also speak with subject-matter specialists or accomplished researchers in the field of open-pit mine production scheduling.
Elham Lotfi
Working on the production scheduling of an open-pit mine using a genetic algorithm sounds like an interesting research topic. I understand that you are facing difficulties in converting infeasible sequencing to feasible solutions and need assistance in creating a normalized plan based on a random solution. Additionally, you would like to know how to modify the time periods assigned to genes or blocks in the ultimate pit, considering the condition of 45 degrees on slopes.
To address your concerns, I would suggest the following approaches:
1. Normalization of sequencing plan:
- Define the constraints: Identify the specific constraints and rules that need to be satisfied in your open-pit mine scenario, including the condition of 45 degrees on slopes. This information is crucial for developing a normalization method.
- Develop normalization rules: Based on the identified constraints, devise a set of rules or algorithms that can convert an infeasible sequencing plan into a feasible one. Consider factors such as block precedence and slope conditions during this process.
- Apply the normalization process: Implement the normalization rules in your MATLAB code to transform the infeasible sequencing plan into a feasible one. Ensure that the generated plan adheres to the constraints you defined.
2. Modifying time periods for genes or blocks:
- Analyze the existing approach: Evaluate the current method you have implemented for assigning time periods to genes or blocks. Understand its limitations in considering the 45-degree slope condition.
- Refine the assignment algorithm: Develop an algorithm that takes into account the slope condition while assigning time periods to genes or blocks. This may involve incorporating slope-related calculations or constraints in the assignment process.
- Test and validate: Apply the modified assignment algorithm to your MATLAB code and generate new solutions. Verify whether the assigned time periods align with the 45-degree slope condition and meet other relevant constraints.
3. Integration with Surpac software:
- Understand software requirements: Familiarize yourself with the specifications and requirements of Surpac software for importing and visualizing the mining results. Ensure that the output from your MATLAB code is compatible with Surpac.
- Validate the results: Import the sequencing results obtained from your MATLAB code into Surpac and verify if the generated mining sequence aligns with your expectations. Assess whether the 45-degree slope condition and the formation of inverse cones are appropriately reflected in the visualization.
It's important to note that the specific details of the normalization methods used in related papers may not be explicitly mentioned. This is common in research publications, as authors often focus on presenting the key aspects of their proposed techniques. Therefore, you may need to develop your own normalization method based on the principles and constraints specific to your open-pit mine scenario.
I hope these suggestions help you in addressing your challenges and advancing your research. If you have any further questions or need clarification on any particular aspect, please feel free to ask. Good luck with your Ph.D. studies!
Qamar Ul Islam , Shraddha jain Sharma
I greatly appreciate you for offering your valuable suggestions.
wishing you all the best!
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