Genetic Algorithm Manufacturing Scheduling for Time-Dependent Energy Costs
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Solution Overview
Problem
Manufacturing processes are energy-intensive and face challenges in optimizing production schedules due to varying energy costs over time, deadlines, and dependencies between tasks, leading to increased costs and potential penalties for late production.
Innovation Solution
A method using genetic algorithms to generate and optimize manufacturing schedules by representing tasks as chromosomes, which include job sequence and idle time matrices, to minimize energy costs and penalties, involving processes like crossover, mutation, and selection based on fitness scores to determine an optimal schedule for multiple products across machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If production is ramped up to meet deadlines, then productivity is improved, but energy cost increases
Solution Approach 1:
The patent applies dynamics by making the production schedule flexible and adaptable to time-varying energy costs. The genetic algorithm dynamically adjusts task scheduling based on real-time or forecasted energy price signals, allowing the system to shift production activities from high-cost periods to low-cost periods while still meeting deadlines, thus resolving the contradiction between maintaining productivity and controlling energy costs.
Solution Approach 2:
The patent changes the parameter of task execution timing based on energy cost parameters. By using genetic algorithms to optimize scheduling parameters (start times, end times, task sequences), the system identifies optimal windows for production that minimize energy costs while maintaining required productivity levels. This involves changing when tasks are performed rather than how much is produced.
2Ease of manufacture
If energy cost varies by time of day, then cost optimization is improved, but scheduling complexity increases
Solution Approach 1:
The patent applies self-service by implementing an autonomous genetic algorithm-based scheduling system that automatically generates optimized production schedules without requiring manual intervention. The system self-adjusts to varying energy costs by evaluating multiple scheduling scenarios and selecting optimal ones, thereby simplifying the user's task while handling the scheduling complexity internally through automated optimization.
Solution Approach 2:
The patent uses feedback mechanisms where the genetic algorithm continuously evaluates schedule performance against energy cost data and deadline constraints. The system receives feedback on energy prices and production outcomes, then iteratively improves scheduling decisions by selecting chromosomes (schedules) with better fitness scores, resolving the complexity of time-dependent cost optimization through systematic feedback-driven refinement.
3Use of energy by moving object
If genetic algorithms are used to optimize schedules, then energy cost is reduced, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the production schedule into discrete tasks and representing each possible schedule as a separate chromosome in the genetic algorithm. This segmentation allows the complex optimization problem to be broken down into manageable genetic operations (selection, crossover, mutation) applied to individual task sequences, making the computational complexity tractable while still achieving comprehensive optimization across all tasks.
Data Source
AI summary
A method of manufacturing at least a first product and a second product with at least a first machine and a second machine at minimum cost in an environment in which a cost of energy used by the first machine and the second machine varies as a function of time may include generating multiple chromosomes, determining fitness scores of each of the chromosomes, randomly generating, with probabilities based on the fitness scores, new chromosomes, determining fitness scores of the new chromosomes, selecting one of the new chromosomes with an optimal fitness score, and manufacturing at least the first product and the second product with at least the first machine and the second machine according to a schedule based on the selected new chromosome.