Archives > Volume 14 | Number 3 | September 2019 > pp 367–378
Advances in Production Engineering & Management
Volume 14 | Number 3 | September 2019 | pp 367–378
An integrated system for scheduling of processing and assembly operations with fuzzy operation time and fuzzy delivery time
Yang, M.S.; Ba, L.; Zheng, H.Y.; Liu, Y.; Wang, X.F.; He, J.Z.; Li, Y.
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A B S T R A C T
This paper integrates the processing scheduling with assembly scheduling, aiming to satisfy the requirements for just-in-time (JIT) production. Considering the uncertainty of time factors in actual production, the operation time of the jobs were represented as triangular fuzzy numbers and the delivery time of the final product as trapezoidal fuzzy numbers. An extended job-shop scheduling problem (JSP) considering above factors was proposed in this paper. A mathematical model was established for processing and assembly scheduling, aiming to achieve the mean satisfaction degree on delivery time. In light of the complexity of the problem, a genetic algorithm (GA) was designed to realize the fuzzy integrated optimization of processing and assembly under uncertainty. The proposed algorithm includes selection, crossover, mutation operations, and reflects the spirits of two-section real number encoding and elite protection strategy. Each part of the GA was designed in detail. Finally, the proposed model and algorithm were verified through a case study on processing and assembly scheduling. The model enjoys high practical value by taking the customer satisfaction of the delivery period as the main goal. The results show that our scheduling strategy mirrors the actual production situation and provides a good reference for JSP scheduling under multiple uncertainties. The best solution obtained by our model is more feasible than basic JSP in real production environment.
A R T I C L E I N F O
Keywords • Integrated scheduling; Uncertainty; Fuzzy operation time; Fuzzy delivery time; Genetic algorithm (GA)
Corresponding author • Yang, M.S.; Ba, L.
Article history • Received 18 April 2019, Revised 8 September 2019, Accepted 12 September 2019
Published on-line • 30 September 2019
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