MULTI-AGENT MODEL OF MULTI-NOMENCLATURE SMALL BATCH PRODUCTION
DOI:
https://doi.org/10.14529/ctcr210406Keywords:
small-scale production, make-to-order production, planning tools, methods of simulation optimizationAbstract
Production planning is a key aspect when optimizing production activities. Simulation is one of the most effective methods available for assessing production problems. The principles of adaptive planning consist of making day-to-day operational decisions at the shop floor, predicting equipment availability, assessing performance, and eliminating bottlenecks. Existing research to eliminate bottlenecks has focused on analyzing data from the physical shop, or vice versa, only on the use of simulated data. Convergence between real and simulated data allows, on the one hand, to obtain more information to predict the availability of each workplace, on the other hand, it allows performance assessment for replanning using a simulation model. Aim. Development of optimization tools for production planning using simulation approaches. Materials and methods. This article presents a multi-agent simulation model for each workplace in the workshop, examines the workload of the workshop, and evaluates the productivity of workplaces. Optimization is proposed for optimal utilization of production facilities. As an example illustrating the efficiency and advantage of the proposed model, we took the production process of electronic equipment in the assembly shop. Results. A planning problem and an approach to optimization are formulated. A multi-agent model of multinomenclature small-scale production has been developed. The model provides for the integration of simulation tools with operational planning systems at the data level. Conclusion. The model proposed in the study allows small-scale production to plan the number of jobs and identify bottlenecks in production. The use of a combination of simulation and planning tools ensures enterprise resource management, taking into account dynamic changes in the system.References
Salamati-Hormozi H., Zang Z.H., Zarei O., Ramezanian R. Trade-off between the costs and the fairness for a collaborative production planning problem in make-to-order manufacturing. Computers &Industrial Engineering, 2018, vol. 126, pp. 421–434. DOI: 10.1016/j.cie.2018.09.044
Denkena B., Dittrich M.A., Jacob S. Methodology for integrative production planning in highly dynamic environments. Production Engineering, 2019, vol. 13, pp. 317– 324. DOI: 10.1007/s11740-019-00889-0
Han J.H., Lee J.Y., Kim Y.D. Production planning in a two-level supply chain for production-time-dependent products with dynamic demands. Computers &Industrial Engineering, 2019, vol. 126, pp. 1–9. DOI: 10.1016/j.cie.2019.05.036
Bruni M.E., Puglia Pugliese L. Di, Beraldi P., Guerriero F. A computational study of exact approaches for the adjustable robust resource-constrained project scheduling problem. Computers and Operations Research, 2018, vol. 99, pp. 178–190. DOI: 10.1016/j.cor.2018.06.016
Garmdare H.S., Lotfi M.M., Honarvar M. Integrated model for pricing, delivery time setting, and scheduling in make-to-order environments. Journal of Industrial Engineering, 2018, vol. 14, pp. 55–64. DOI: 10.1007/s40092-017-0205-y
Alvarez-Gill N., Rosillo R., De la Fuente D., Pino R. A discrete firefly algorithm for solving the flexible job-shop scheduling problem in a make-to-order manufacturing. Central European Journal of Operations Research, 2020. DOI: 10.1007/s10100-020-00701-w
Goodall P., Sharpe R., West A. A date-driven simulation to support remanufacturing operations. Computers in Industry, 2019, vol. 105, pp. 48–60. DOI: 10.1016/j.compind.2018.11.001
Lofving M., Almstrom P., Jarebrant C., Wadman B., Widfeldt M. Evaluation of flexible automation for small batch production. Procedia Manufacturing, 2018, vol. 25, pp. 177–184. DOI: 10.1016/j.promfg.2018.06.072
Beemsterboer B., Teunter R., Land M., Bokhorst J. Integrating make-to-order and make-to-stock in job shop control. International Journal of Production Economics, 2017, vol. 185, pp. 1–10. DOI: 10.1016/j.ijpe.2016.12.015
Kapulin D.V., Russkikh P.A. Analisis and improvement of production planning within small-batch make-to-order production. Journal of Physics: Conference Series, 2020, vol. 1515, no. 2. DOI: 10.1088/1742-6596/1515/2/022072
Hermmati S., Ebadian M., Nahvi A. A new decision making structure for managing arriving orders in MTO environments. Expert Systems with Application, 2012, vol. 39, no. 3, pp. 2669–2676. DOI: 10.1016/j.eswa.2011.08.122
Jeon S.M., Gitae K. A survey of simulation modeling techniques in production planning and control (PPC). Production Planning & Control, 2016, vol. 27, iss. 5, pp. 360–377. DOI: 10.1080/09537287.2015.1128010
Barlas P., Heavey C. Automation of input data to discrete event simulation for manufacturing: A review. International Journal of Modeling, Simulation, and Scientific Computing, 2016, vol. 7, no. 1. DOI: 10.1142/S1793962316300016
Mokshin V.V., Kirpichnikov A.P., Soiko A.I. Simulation and optimization of the cargo terminal in the AnyLogic environment. Journal of Physics: Conference Series, 2019, vol. 1368. DOI: 10.1088/1742-6596 /1368/4/042082
Русских П.А., Капулин Д.В. Анализ решений для создания и реализации механизмов адаптивного планирования позаказного производства. Вестник МГТУ «Станкин». 2021. № 1 (56). С. 44–48. [Russkikh P.A., Kapulin D.V. [Analysis of solutions for the creation and implementation of adaptive planning mechanisms for make-to-order production]. Vestnik MSTU “STANKIN”, 2021, vol. 1, no. 56, pp. 44–48. (in Russ.)].
