Decision support system for continuous production
Author:
Bakhrankova Krystsina
Abstract
PurposeThe purpose of this paper is to develop energy optimizer (ENEO) – a model‐based decision support system (DSS) for an existing European chemical plant with a multi‐stage continuous production process. The system comprises two modules – energy cost minimization and joined energy cost minimization and output maximization. Following the description of the researched production, the paper presents a gist of the underlying formulations. Then, it tests the DSS on real data instances with a focus on its configuration, practical implications and implementation challenges.Design/methodology/approachThe design of the planning tool is consistent with that of the model‐based DSS and based on the existing information systems. The defined research problems are explored with the use of quantitative methods – the operations research methodology.FindingsThe findings show that ENEO reflects the essence of the researched production process and can provide benefits in practical business operations.Research limitations/implicationsBoth the proposed system configuration and the formulated models lay a foundation to further research within the described industrial setting.Practical implicationsThe system can be utilized in daily operations to provide substantial cost savings, improved capacity utilization and reactivity.Originality/valueThis paper contributes to research by bridging the gap between theory and practice. On the one hand, it describes an unexplored problem and its subsequent solution embodied in the DSS. On the other hand, it emphasizes the importance of applying the operations research methodology to the real‐world issues. Therefore, this work is valuable to both academics and practitioners.
Subject
Industrial and Manufacturing Engineering,Strategy and Management,Computer Science Applications,Industrial relations,Management Information Systems
Reference51 articles.
1. AMPL (2000), “New in AMPL: relational database access”, Modeling language for mathematical programming, available at: www.apml.com/NEW/tables.html (accessed January 27, 2009). 2. Artiba, A. and Riane, F. (1998), “An application of a planning and scheduling multi‐model approach in the chemical industry”, Computers in Industry, Vol. 36, pp. 209‐29. 3. Bakhrankova, K. (2009a), “Optimization of multi‐stage continuous production systems”, in Hertz, S. (Ed.), Proceedings of the 21st Annual Nofoma Conference, June 11‐12, 2009 Jönköping Sweden, Jönköping International Business School, Jönköping University, Jönköping, pp. 16‐31. 4. Bakhrankova, K. (2009b), “Planning, productivity and quality in continuous non‐discrete production”, International Journal of Management and Enterprise Development, Vol. 7 No. 1, pp. 44‐64. 5. Berning, G., Brandenburg, M., Gürsoy, K., Kussi, J.S., Mehta, V. and Tölle, F.J. (2004), “Integrating collaborative planning and supply chain optimization for the chemical process industry (I) – methodology”, Computers & Chemical Engineering, Vol. 28, pp. 913‐27.
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|