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More efficient meta-heuristic algorithms for integrated process planning and scheduling based on relative-priority encoding
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AI
DOI：10.1007/s10479-025-06966-1.png)
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Abstract
En 中文
Integrated process planning and scheduling (IPPS) is an important issue for the efficiency and resource utilization of industrial manufacturing systems, and meta-heuristic algorithms are commonly used for solving IPPS problems. Genetic Algorithms (GA) have, in particular, been demonstrated to generate efficient IPPS solutions. In this paper, a novel mechanism named relative-priority encoding is designed to express an IPPS problem solution. The novel mechanism is specifically designed for such IPPS problems that contain alternative operation paths represented by OR-nodes. The mechanism divides an encoded IPPS solution expression into two parts: one for path selection and the other for operation scheduling and mapping. By introducing the concept of a ready-operation set, each expression can be efficiently decoded into an IPPS solution. The unique encoding mechanism is applied not only to Genetic Algorithms, but also to several other meta-heuristic algorithms, including Grey Wolf Optimizer, Tabu Search, Particle Swarm Optimizer, and Simulated Annealing. Comprehensive comparisons are made, and the experimental results show that the relative-priority Genetic Algorithm (RPGA) performs better regarding solution quality and fast convergence. RPGA is also compared with a state-of-the-art GA, and the results demonstrate RPGA’s superiority in terms of quality and efficiency.
Keywords:
Genetic algorithms
Encoding
Meta-heuristic
Planning
Scheduling
Journal
IF:
4.5
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8.0K
Citations:
2.1W
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