Abstract
We describe how a generic multi-period optimization-based decision support system (DSS) can be used for strategic planning in process industries. The DSS is built on five fundamental elements—materials, facilities, activities, storage areas, and time periods. It requires little direct knowledge of optimization techniques to be used effectively. Results based on real data from an aluminium company in India demonstrate significant potential for improvement in profits. We conclude with a comparison of similar studies in two other process industries.
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References
Adams D and Beckett D (1999). Programming in 4th Dimension, The Ultimate Guide. Automated solutions group: Huntington Beach, CA.
Dutta G and Fourer R (2001). A survey of mathematical programming applications in an integrated steel plant . Manuf Serv Opns Mngt 3(4): 387–400.
Dutta G and Fourer R (2004). An optimization based decision support system for strategic and operational planning in process industries . Optim Eng 5(3): 295–314.
Dutta G and Fourer R (2008). Database structure for a class of multi-period mathematical programming models . Decis Support Syst 45: 870–883.
Dutta G, Sinha GP, Roy PN and Mitter N (1994). A linear programming model for distribution of electrical energy in a steel plant . Int Trans Opl Res 1(1): 17–29.
Dutta G, Sinha GP and Roy PN (2000). A product mix optimizer for an integrated steel plant . IIMB Management Review December: 80–86.
Dutta G, Fourer R, Majumdar A and Dutta D (2007). An optimization-based decision support system for strategic planning in a process industry: The case of a pharmaceutical company in India . Int J Prod Econ 102: 92–103.
Fourer R (1997). Database structures for mathematical programming models . Decis Support Syst 20: 317–344.
Gravel M, Price WL and Gagné C (2000). Scheduling jobs in an alcan aluminium foundry using a genetic algorithm . Int J Prod Res 38: 3031–3041.
Gupta N (2008). A stochastic optimization based decision support system for strategic planning in process industries. PhD Dissertation, IIM Ahmedabad, India.
Marsten RE (1981). The design of the XMP linear programming library . ACM T Math Software 7: 481–497.
Nicholls MG (1994). Optimizing the operations of an ingot mill in an aluminium smelter . J Opl Res Soc 45: 987–998.
Nicholls MG (1995a). Aluminium production modelling—A nonlinear bilevel programming approach . Opns Res 43: 208–218.
Nicholls MG (1995b). Scheduling production in a heavily constrained plant—Anode manufacture in an aluminium smelter . J Opl Res Soc 46: 579–591.
Nicholls MG (1997). Developing an integrated model of an aluminium smelter incorporating sub-models with different time bases and levels of aggregation . Eur J Opl Res 99: 479–492.
Nicholls MG and Hedditch DJ (1993). The development of an integrated mathematical model of an aluminium smelter . J Opl Res Soc 44: 225–235.
Sinha GP, Chandrashekaran BS, Mitter N, Dutta G, Singh SB, Roy PN and Roy Choudhary A (1995). Strategic and operational management with optimization at Tata Steel . Interfaces 22(1): 6–19.
Warburton A and Marsi K (1998). Using optimization to improve the yield of an aluminium extrusion plant . J Opl Res Soc 49: 1111–1116.
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This work has been supported by grants from the Research and Publication Committee of the Indian Institute of Management, Ahmedabad, India.
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Dutta, G., Gupta, N. & Fourer, R. An optimization-based decision support system for strategic planning in a process industry: the case of aluminium company in India. J Oper Res Soc 62, 616–626 (2011). https://doi.org/10.1057/jors.2010.8
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DOI: https://doi.org/10.1057/jors.2010.8