A Scheduling Algorithm for Multi-Workshop Production Based on BOM and Process Route

For the scheduling problem of complex products in multi-workshop production, this paper studied the BOM (Bill of Materials) structure of complex products and the characteristics of the process route and developed the construction method of a multi-level process network diagram. Based on this, a comprehensive mathematical model for scheduling on multi-workshop production was proposed. An improved particle swarm algorithm (PSO) was proposed to solve the problem. By constructing the network subgraph, the invalid search path of the algorithm was avoided, and the efficiency of the algorithm was improved. In addition, for the scheduling problem with product time constraints, this paper presented a path search rescheduling strategy to ensure that the algorithm could obtain an effective search path. Finally, the model and algorithm were verified through a case study. This paper optimized the parameters of the algorithm by different tests and obtained the optimal range of the parameters. At the same time, through the analysis of the scheduling of complex products in a multi-workshop environment, the effectiveness and practicability of the above methods were verified.

[1]  Jun Zhao,et al.  Surrogate-assisted particle swarm optimization algorithm with Pareto active learning for expensive multi-objective optimization , 2019, IEEE/CAA Journal of Automatica Sinica.

[2]  P. Senthil Kumar,et al.  Hybrid Sorting Immune Simulated Annealing Algorithm For Flexible Job Shop Scheduling , 2014, Int. J. Comput. Intell. Syst..

[3]  Marcelo Seido Nagano,et al.  An evolutionary clustering search for the total tardiness blocking flow shop problem , 2017, Journal of Intelligent Manufacturing.

[4]  Jing J. Liang,et al.  Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution , 2019, Memetic Computing.

[5]  Mohsen Ziaee A heuristic algorithm for the distributed and flexible job-shop scheduling problem , 2013, The Journal of Supercomputing.

[6]  Sicheng Zhang,et al.  Integrated process planning and scheduling – multi-agent system with two-stage ant colony optimisation algorithm , 2012 .

[7]  Yabo Luo,et al.  Nested optimization method combining complex method and ant colony optimization to solve JSSP with complex associated processes , 2017, J. Intell. Manuf..

[8]  Tung-Kuan Liu,et al.  Solving Distributed and Flexible Job-Shop Scheduling Problems for a Real-World Fastener Manufacturer , 2014, IEEE Access.

[9]  Nadia Bhuiyan,et al.  Scheduling in aerospace composite manufacturing systems: a two-stage hybrid flow shop problem , 2018 .

[10]  Yilmaz Delice,et al.  A genetic algorithm approach for balancing two-sided assembly lines with setups , 2019, Assembly Automation.

[11]  Chuang Liu,et al.  Quantum-inspired ant colony optimisation algorithm for a two-stage permutation flow shop with batch processing machines , 2020, Int. J. Prod. Res..

[12]  Ahmed Azab,et al.  An improved model and novel simulated annealing for distributed job shop problems , 2015 .

[13]  Yanina Fumero,et al.  Optimization framework for the simultaneous batching and scheduling of multisite production environments , 2018 .

[14]  Congbo Li A Batch Splitting Flexible Job Shop Scheduling Model for Energy Saving under Alternative Process Plans , 2017 .

[15]  Seyed Hessameddin Zegordi,et al.  A multi-objective optimization approach to project scheduling with resiliency criteria under uncertain activity duration , 2020, Ann. Oper. Res..

[16]  MengChu Zhou,et al.  Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions , 2019, IEEE Transactions on Evolutionary Computation.

[17]  Zhenghua Chen,et al.  A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems , 2019, IEEE/CAA Journal of Automatica Sinica.

[18]  Maghsud Solimanpur,et al.  Multi-objective multi-model assembly line balancing problem: a quantitative study in engine manufacturing industry , 2019, OPSEARCH.

[19]  Lijun He,et al.  Many-Objective Evolutionary Algorithm With Reference Point-Based Fuzzy Correlation Entropy for Energy-Efficient Job Shop Scheduling With Limited Workers , 2021, IEEE Transactions on Cybernetics.

[20]  Hamid Reza Golmakani,et al.  A two-phase algorithm for multiple-route job shop scheduling problem subject to makespan , 2013 .

[21]  Stéphane Dauzère-Pérès,et al.  A batch-oblivious approach for Complex Job-Shop scheduling problems , 2017, Eur. J. Oper. Res..

[22]  Kun Chen,et al.  Two-generation Pareto ant colony algorithm for multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines , 2015, Journal of Intelligent Manufacturing.

[23]  Atabak Elmi,et al.  Multi-degree cyclic flow shop robotic cell scheduling problem: Ant colony optimization , 2016, Comput. Oper. Res..

[24]  Tian Wang,et al.  Solving distributed two-stage hybrid flowshop scheduling using a shuffled frog-leaping algorithm with memeplex grouping , 2020, Engineering Optimization.

[25]  Wenqiang Zhang,et al.  Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling , 2017, Comput. Ind. Eng..

[26]  Ibrahim Kucukkoc,et al.  Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines , 2016 .