An Improved Mayfly Optimization Algorithm for Type-2 Multi-Objective Integrated Process Planning and Scheduling

The type-2 multi-objective integrated process planning and scheduling problem, as an NP-hard problem, is required to deal with both process planning and job shop scheduling, and to generate optimal schedules while planning optimal machining paths for the workpieces. For the type-2 multi-objective in...

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Vydáno v:Mathematics (Basel) Ročník 11; číslo 20; s. 4384
Hlavní autoři: Yang, Ke, Pan, Dazhi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2023
Témata:
Algorithms
Analysis
Coding
Combinatorial analysis
Decoding
Genetic algorithms
Job shop scheduling
Machining
Manufacturing
Mathematical optimization
multi-objective optimization
Multiple objective analysis
neighborhood structure
Optimization
Optimization algorithms
Process planning
Scheduling
shop scheduling
Workpieces
ISSN:2227-7390, 2227-7390
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Shrnutí:The type-2 multi-objective integrated process planning and scheduling problem, as an NP-hard problem, is required to deal with both process planning and job shop scheduling, and to generate optimal schedules while planning optimal machining paths for the workpieces. For the type-2 multi-objective integrated process planning and scheduling problem, a mathematical model with the minimization objectives of makespan, total machine load, and critical machine load is developed. A multi-objective mayfly optimization algorithm with decomposition and adaptive neighborhood search is designed to solve this problem. The algorithm uses two forms of encoding, a transformation scheme designed to allow the two codes to switch between each other during evolution, and a hybrid population initialization strategy designed to improve the quality of the initial solution while taking into account diversity. In addition, an adaptive neighborhood search cycle based on the average distance of the Pareto optimal set to the ideal point is designed to improve the algorithm’s merit-seeking ability while maintaining the diversity of the population. The proposed encoding and decoding scheme can better transform the continuous optimization algorithm to apply to the combinatorial optimization problem. Finally, it is experimentally verified that the proposed algorithm achieves better experimental results and can effectively deal with type-2 MOIPPS.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math11204384