# Optimizer Problem: Line Production Allocation by Production Plan

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edited April 15

Hi All

,

I am having issues using the optimizer for production allocation. The model is for capacity planning.

Problem statement

Need to allocate the SKU demand to compatible factory lines based on the following logic. Each SKU has a production priority and a factory line priority.

1. If the production priority for a SKU is 1 total demand for the SKU must be allocated before moving to the SKU with production priority 2.
2. For each SKU, each compatible factory line is given a priority value. For e.g., in the case of SKU-106 the maximum demand possible should be allotted to Line priority 1(Factory Line 3) and then the remaining demand can be allotted to Line priority 2(Factory Line 2)

Can anyone have idea ?

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Here's an approach to solve the production allocation problem using an optimizer for capacity planning, considering SKU and factory line priorities:
Data Structure:
SKUs: List containing information for each SKU, including:id: Unique identifier for the SKU (e.g., SKU-106)demand: Total demand for the SKUpriority: Production priority of the SKU (e.g., 1, 2, 3)compatible_lines: List of compatible factory lines with their priorities for this SKU with candy clicker. Each entry in the list should be a dictionary containing:line_id: Unique identifier for the factory line (e.g., Factory Line 2)priority: Priority of this line for producing this SKU (e.g., 1, 2)

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It's a complex optimization problem that may require a systematic approach, possibly involving algorithms or mathematical modeling.

One potential approach could be to develop a decision-making algorithm that takes into account the production priorities of the SKUs and the priority values of the factory lines to allocate the demand in an optimal way. This could involve writing a program or using specialized optimization software.

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@slope game: Another approach could involve formulating the allocation problem as a mathematical optimization model and solving it using mathematical optimization techniques such as linear programming or integer programming.

Ultimately, the best approach will depend on the specific details of your problem and the resources available to solve it.