Production Decision Optimization Based on a Multi-Agent Mixed Integer Programming Model
In the increasingly competitive manufacturing industry, optimizing production decision making and quality control is crucial for the strategic development of companies. To maximize cost effectiveness and enhance market competitiveness, scientific decision-making and effective quality inspection are...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/11/1827 |
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| author | Simiao Wang Yijun Li Jinghan Wang |
| author_facet | Simiao Wang Yijun Li Jinghan Wang |
| author_sort | Simiao Wang |
| collection | DOAJ |
| description | In the increasingly competitive manufacturing industry, optimizing production decision making and quality control is crucial for the strategic development of companies. To maximize cost effectiveness and enhance market competitiveness, scientific decision-making and effective quality inspection are particularly important. Among the various types of decision models for production processes, extensive research has been conducted in different fields to address diverse decision problems for production processes, resulting in the establishment of multiple models that aid in the analysis of factors that influence processes at various stages. In this paper, we propose a production decision optimization method based on a multi-agent mixed-integer programming model, which integrates multistage decision analysis and quality inspection. By incorporating Monte Carlo simulation, we can simulate the fluctuations in defect rates during actual production processes and optimize decision-making under multiple confidence levels. This model effectively balances production costs and product quality, achieving maximum cost-effectiveness through the optimization of decision pathways during the production stages. Experimental results show that our model can provide robust and efficient decision support in dynamic manufacturing environments. |
| format | Article |
| id | doaj-art-a7dd0e7948a848cf99c82ea9408b2332 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-a7dd0e7948a848cf99c82ea9408b23322025-08-20T02:32:57ZengMDPI AGMathematics2227-73902025-05-011311182710.3390/math13111827Production Decision Optimization Based on a Multi-Agent Mixed Integer Programming ModelSimiao Wang0Yijun Li1Jinghan Wang2Applied Mathematical Statistics, Anhui University, No. 3 Feixi Road, Hefei 230000, ChinaApplied Mathematical Statistics, Anhui University, No. 3 Feixi Road, Hefei 230000, ChinaData Science and Big Data Technology, Anhui University, No. 3 Feixi Road, Hefei 230000, ChinaIn the increasingly competitive manufacturing industry, optimizing production decision making and quality control is crucial for the strategic development of companies. To maximize cost effectiveness and enhance market competitiveness, scientific decision-making and effective quality inspection are particularly important. Among the various types of decision models for production processes, extensive research has been conducted in different fields to address diverse decision problems for production processes, resulting in the establishment of multiple models that aid in the analysis of factors that influence processes at various stages. In this paper, we propose a production decision optimization method based on a multi-agent mixed-integer programming model, which integrates multistage decision analysis and quality inspection. By incorporating Monte Carlo simulation, we can simulate the fluctuations in defect rates during actual production processes and optimize decision-making under multiple confidence levels. This model effectively balances production costs and product quality, achieving maximum cost-effectiveness through the optimization of decision pathways during the production stages. Experimental results show that our model can provide robust and efficient decision support in dynamic manufacturing environments.https://www.mdpi.com/2227-7390/13/11/1827multi-agent mixed-integer programmingproduction process decision-makingquality inspectionmultistage decision-makingMonte Carlo simulation |
| spellingShingle | Simiao Wang Yijun Li Jinghan Wang Production Decision Optimization Based on a Multi-Agent Mixed Integer Programming Model Mathematics multi-agent mixed-integer programming production process decision-making quality inspection multistage decision-making Monte Carlo simulation |
| title | Production Decision Optimization Based on a Multi-Agent Mixed Integer Programming Model |
| title_full | Production Decision Optimization Based on a Multi-Agent Mixed Integer Programming Model |
| title_fullStr | Production Decision Optimization Based on a Multi-Agent Mixed Integer Programming Model |
| title_full_unstemmed | Production Decision Optimization Based on a Multi-Agent Mixed Integer Programming Model |
| title_short | Production Decision Optimization Based on a Multi-Agent Mixed Integer Programming Model |
| title_sort | production decision optimization based on a multi agent mixed integer programming model |
| topic | multi-agent mixed-integer programming production process decision-making quality inspection multistage decision-making Monte Carlo simulation |
| url | https://www.mdpi.com/2227-7390/13/11/1827 |
| work_keys_str_mv | AT simiaowang productiondecisionoptimizationbasedonamultiagentmixedintegerprogrammingmodel AT yijunli productiondecisionoptimizationbasedonamultiagentmixedintegerprogrammingmodel AT jinghanwang productiondecisionoptimizationbasedonamultiagentmixedintegerprogrammingmodel |