Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

Numerous organizations are striving to maximize the profit of their businesses by the effective implementation of competitive advantages including cost reduction, quick delivery, and unique high-quality products. Effective production-scheduling techniques are methods that many firms use to attain these competitive advantages. Implementing scheduling techniques in high-mix low-volume (HMLV) manufacturing industries, especially in Industry 4.0 environments, remains a challenge, as the properties of both parts and processes are dynamically changing. As a reaction to these challenges in HMLV Industry 4.0 manufacturing, a newly advanced and effective real-time production-scheduling decision-support system model was developed. The developed model was implemented with the use of robotic process automation (RPA), and it comprises a hybrid of different advanced scheduling techniques obtained as the result of analytical-hierarchy-process (AHP) analysis. The aim of this research was to develop a method to minimize the total production process time (total make span) by considering the results of risk analysis of HMLV manufacturing in Industry 4.0 environments. The new method is the combination of multi-broker (MB) optimization and a genetics algorithm (GA) that uses general key process indicators (KPIs) that are easy to measure in any kind of production. The new MB–GA method is compatible with industry 4.0 environments, so it is easy to implement. Furthermore, MB–GA deals with potential risk during production, so it can provide more accurate results. On the basis of survey results, 16% of the asked companies could easily use the new scheduling method, and 43.2% of the companies could use it after a little modification of production.

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A decision support system of real time dispatching in semiconductor wafer fabrication with shortest process time in wet bench

2003 5th International Conference on ASIC. Proceedings (IEEE Cat. No.03TH8690) ◽

10.1109/issm.2003.1243287 ◽

2004 ◽

Cited By ~ 1

Author(s):

M.D. Hsieh ◽

Amos Lin ◽

Kenley Kuo ◽

H.L. Wang

Keyword(s):

Decision Support ◽

Decision Support System ◽

Real Time ◽

Support System ◽

Wafer Fabrication ◽

Process Time ◽

Semiconductor Wafer ◽

Semiconductor Wafer Fabrication

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Designing a scheduling decision support system for the skin pass line: A case study of the steel finishing line

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405420927578 ◽

2020 ◽

Vol 234 (13) ◽

pp. 1640-1655 ◽

Cited By ~ 1

Author(s):

Parinaz Vaez ◽

Armin Jabbarzadeh ◽

Nader Azad

Keyword(s):

Decision Support ◽

Decision Support System ◽

Support System ◽

Heuristic Algorithm ◽

Production Scheduling ◽

Mixed Integer ◽

Total Production ◽

Steel Company ◽

Manual Method ◽

Skin Pass

In this paper, we investigate the scheduling policies in the iron and steel industry, and in particular, we formulate and propose a solution to a complicated problem called skin pass production scheduling in this industry. The solution is to generate multiple production turns for the skin pass coils and, at the same time, determine the sequence of these turns so that productivity and product quality are maximized, while the total production scheduling cost, including the costs of tardiness, flow of material, and the changeover cost between adjacent and non-adjacent coils, is minimized. This study has been prompted by a practical problem in an international steel company in Iran. In this study, we present a new mixed integer programming model and develop a heuristic algorithm, as the commercial solvers would have difficulty in solving the problem. In our heuristic algorithm, initial solutions are obtained by a greedy constraint satisfaction algorithm, and then a local search method is developed to improve the initial solution. The experimental results tested on the data collected from the steel company show the efficiency of the proposed heuristic algorithm by solving a large-sized instance in a reasonable computation time. The average deviation between the manual method and the heuristic algorithm is 30%. Also, in all the components of the objective function, the algorithm performs better compared to the manual method. The improved values are greater than 15. In addition, we develop a commercial decision support system for the implementation of the proposed algorithm in the steel company.

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