Machine-State Production Planning for Dynamic Sub-Order Scheduling
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Solution Overview
Problem
Current production planning systems inadequately account for the current state of resources, such as machine failures and ad-hoc orders, leading to inflexible planning and inefficiencies in production environments with multiple heterogeneous machines.
Innovation Solution
A production optimization system with a distribution unit that continuously monitors machine status and an optimization unit that dynamically adjusts production plans by dividing orders into machine-specific sub-orders, allowing for real-time adaptation to resource changes and machine availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional production planning is used, then planning simplicity is maintained, but the system cannot adapt to current equipment conditions and machine failures
Solution Approach 1:
The system segments production planning into discrete sub-orders that can be independently assigned to different machines. Each sub-order represents a separable unit of work that can be dynamically reassigned based on machine availability, allowing the system to adapt to equipment conditions without complete replanning.
Solution Approach 2:
The system implements dynamic production planning where the production plan is continuously adjusted based on current machine status. The optimization unit recalculates sub-order assignments in real-time when machine conditions change, transforming static planning into a dynamic adaptive system.
2Manufacturing precision
If detailed production planning is implemented, then processing times are accurately determined, but the system becomes inflexible to ad-hoc orders and changes
Solution Approach 1:
By dividing orders into smaller sub-orders with individually calculated processing times, the system maintains precise time accounting while enabling flexible reassignment. Each sub-order's processing time is determined based on specific machine capabilities, allowing accurate scheduling that can adapt to changes.
Solution Approach 2:
The system continuously monitors machine status and feeds this information back to the optimization unit, which adjusts sub-order assignments accordingly. This feedback mechanism allows the system to maintain accurate processing times while adapting to ad-hoc orders and machine failures in real-time.
3Productivity
If production planning is decoupled from actual production, then planning efficiency is improved, but current equipment state cannot be considered
Solution Approach 1:
The distribution unit continuously monitors machine status and provides real-time feedback to the optimization unit. This feedback loop ensures that current equipment states are considered in production planning without significantly impacting planning efficiency, as the system uses automated status reporting rather than manual updates.
Solution Approach 2:
The system performs preliminary optimization of sub-order assignments based on current machine status before production begins. By pre-calculating optimal assignments based on real-time equipment conditions, the system maintains both planning efficiency and reliability of equipment state consideration.
4Speed
If multiple machines are used for parallel processing, then production speed increases, but planning complexity increases exponentially
Solution Approach 1:
The system segments production into sub-orders that can be independently assigned to multiple machines for parallel processing. This segmentation allows the use of heuristic algorithms that efficiently handle multi-machine scheduling without exponential complexity growth, as each sub-order can be evaluated and assigned relatively independently.
Data Source
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AI summary
The invention relates to a production planning system and method, in particular such a production planning system and method which take into consideration the state of the production means. A production optimization system is provided which has the following: a production environment (110) with at least one first machine (111), a second machine (112), a distribution unit (120) which is designed to be connected to the first machine (111) and the second machine (112) via a communication network and to ascertain the machine state thereof, and an optimization unit (130) that is designed to generate a production plan in which at least one task for producing a product is separated into machine-specific sub-tasks for the first machine (111) or the second machine (112), said sub-tasks being provided with a sequence for the first machine (111) or the second machine (112). The distribution unit (120) is designed to ascertain the next occurring sub-task for the first machine (111) or the second machine (112) from the optimization unit (130) while taking into consideration the respective machine state, to remove the sub-task from the production plan, and to assign the sub-task to the respective machine.