Flexible Job-Shop Scheduling Using Iterative Machine Allocation
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Solution Overview
Problem
The flexible job-shop scheduling problem (FJSP) is challenging due to the need to determine both the sequence and machine allocation for each process, making it more complex than traditional job-shop scheduling problems (JSP), with a focus on optimizing production makespan.
Innovation Solution
A production scheduling method involving the generation of an initialized population of scheduling schemes, followed by iterative updating using target iterative algorithms like particle swarm or genetic algorithms to determine an optimal production scheduling scheme, optimizing production makespan and device utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional job-shop scheduling methods are used, then the scheduling process is simpler, but the production makespan is longer and device utilization is lower
Solution Approach 1:
The patent replaces traditional mechanical scheduling methods with intelligent algorithms (particle swarm optimization, genetic algorithms, simulated annealing) to solve the FJSP problem. These algorithms automatically search for optimal scheduling schemes by simulating natural processes, substituting manual or rule-based scheduling mechanisms with computational intelligence systems that can handle the complexity of flexible job-shop environments while minimizing makespan.
Solution Approach 2:
The patent changes the approach from fixed scheduling rules to dynamic parameter optimization. By treating scheduling decisions as adjustable parameters that can be optimized through iterative algorithms, the system adapts to the specific constraints and objectives of each FJSP instance, finding optimal or near-optimal solutions for makespan, device utilization, and other performance metrics.
2Adaptability or versatility
If more decision-making content is added to determine job paths, then the scheduling becomes more flexible, but the problem complexity increases
Solution Approach 1:
The patent applies dynamics by making the scheduling system adaptable and flexible through iterative optimization algorithms. The system dynamically adjusts scheduling decisions based on current system state and learned patterns, allowing it to handle the increased complexity of FJSP with multiple machines per operation while maintaining scheduling flexibility. The algorithms evolve solutions over time, adapting to the complex constraints and opportunities presented by flexible job-shop environments.
Data Source
AI summary
A flexible job-shop production scheduling method and apparatus, an electronic device, and a computer-readable storage medium. The method includes: generating an initialized population according to information of workpieces to be produced, the population including a plurality of production scheduling schemes, and each production scheduling scheme representing a scheduling sequence of said workpieces; and performing iterative update on the population on the basis of a preset target iterative algorithm, and determining a target production scheduling scheme according to the iterative process.