Reverse Scheduling Algorithm for Compact Order Lead Times
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Solution Overview
Problem
Conventional machine scheduling methods often result in increased order lead times, storage costs, and waiting times due to inefficiencies in resource allocation, particularly in industries like the semiconductor industry, where customers demand reduced lead times without requiring fundamental changes to existing scheduling routines.
Innovation Solution
A method that allocates resources by scheduling activities according to a just-in-time criterion, where the execution date of one activity is fixed, and subsequent activities are rescheduled in reverse, keeping the ultimate scheduled date fixed, to minimize lead times and compact schedules, using a genetic scheduling algorithm with predefined constraints to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional machine scheduling methods are used, then scheduling routines are simple and easy to implement, but order lead times increase and productivity decreases
Solution Approach 1:
The patent applies reverse scheduling by starting from the order due date and scheduling activities backwards to their earliest possible start times, rather than the conventional forward scheduling from order start. This inversion of the scheduling direction enables compact scheduling that minimizes order lead times while maintaining compatibility with existing scheduling systems through the use of standard genetic algorithms.
2Productivity
If forward scheduling is used, then activities are scheduled in chronological order, but order lead times are extended and storage costs increase
Solution Approach 1:
The patent implements backward scheduling from the order due date, scheduling each activity to start as late as possible without delaying subsequent activities. This reverse approach compresses the overall order lead time by eliminating unnecessary waiting periods and storage requirements that occur with forward scheduling, thereby improving order completion efficiency.
3Loss of time
If compact scheduling is implemented, then order lead times are reduced, but scheduling constraint satisfaction becomes more difficult
Solution Approach 1:
The patent employs a genetic algorithm that iteratively evaluates scheduling solutions against predefined constraints and optimizes the schedule accordingly. The feedback mechanism allows the system to adjust activity timing and resource allocation to satisfy constraints while maintaining compact scheduling, ensuring both reduced order lead times and reliable constraint satisfaction.
Solution Approach 2:
The patent uses dynamic scheduling where activity start and end times are continuously adjusted based on resource availability, constraint satisfaction, and optimization objectives. This dynamic approach allows the schedule to adapt and reconfigure itself to maintain compactness while satisfying all scheduling constraints throughout the optimization process.
Data Source
AI summary
Methods, systems and computer program products are provided for allocating resources in a plannable process, wherein a number of resources is used for executing an order comprising a chain of related activities to be executed on the number of resources. In one implementation, a method comprises, for each order, identifying a first activity execution due date for executing a predetermined first activity in the order, scheduling an execution date for each activity according to a just-in-time criterion in correspondence with an activity execution due date of a related activity, identifying a second activity execution date for executing a predetermined second activity in the order that is scheduled according to the scheduling routine, and reversely scheduling an execution date for each activity according to a just-in-time criterion in correspondence with an activity execution due date of a reverse related activity, wherein the second activity execution date is kept fixed as a second activity execution due date.