Open AccessMulti-Agent Model of Multi-Nomenclature Small Batch Production
Author(s) -
P. A. Russkikh,
AUTHOR_ID,
Д. В. Капулин
Publication year - 2021
Publication title -
vestnik ûžno-uralʹskogo gosudarstvennogo universiteta. seriâ, kompʹûternye tehnologii, upravlenie, èlektronika
Language(s) - English
Resource type - Journals
eISSN - 2409-6571
pISSN - 1991-976X
DOI - 10.14529/ctcr210406
Subject(s) - production (economics) , production planning , computer science , process (computing) , industrial engineering , workload , plan (archaeology) , scale (ratio) , simulation modeling , key (lock) , batch production , operations research , resource (disambiguation) , resource allocation , manufacturing engineering , engineering , operations management , history , computer network , physics , computer security , archaeology , quantum mechanics , economics , macroeconomics , microeconomics , operating system
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.