DECISION

pp 1–18 | Cite as

Application of linear programming in optimizing the procurement and movement of coal for an Indian coal-fired power-generating company

Case Study
  • 24 Downloads

Abstract

In this paper, an application of linear programming in optimizing the procurement and movement of coal for an Indian coal-fired thermal power-generating company is presented. Results show that there is immense potential not only for significant cost savings but also for reduced logistics between different coal source–power plant pairs. The target plant load factor at each power plant can be achieved without the need of any imported coal which would not only save precious foreign exchange, but also reduce the logistics involved in the import of coal and transport to power plants. Sensitivity analyses have also been performed with varying coal supply and coal quality levels. The issue of greenhouse gas (GHG) emissions from coal-fired power plants has also been addressed. The trade-off between the optimal total cost and GHG emission targets has been explored. Results show that it is possible to significantly reduce carbon footprints with a marginal increase in the optimal total cost and without the need of import of coal. However, if it is desired to further reduce GHG emission targets, optimal total costs rise substantially with imported coal gradually substituting domestic coal. It is believed that the results presented in this paper would provide a fresh perspective with regard to the allocation and movement of coal. Finally, recommendations and concluding remarks are presented.

Keywords

Linear programming Coal-fired power plant Coal allocation Coal movement Greenhouse gas emission 

Notes

Acknowledgements

The authors are thankful to the Company for entrusting with the project and providing all the necessary data and information for this purpose.

References

  1. Amaya CA, Carvajal J, Castaño F (2013) A heuristic framework based on linear programming to solve the constrained joint replenishment problem (C-JRP). Int J Prod Econ 144(1):243–247CrossRefGoogle Scholar
  2. Arriaga H, Viguria M, López DM, Merino P (2017) Ammonia and greenhouse gases losses from mechanically turned cattle manure windrows: a regional composting network. J Environ Manage 203(1):557–563CrossRefGoogle Scholar
  3. Bento CB, Filoso S, Pitombo LM, Cantarella H, Rossetto R, Martinelli LA, do Carmo JB (2018) Impacts of sugarcane agriculture expansion over low-intensity cattle ranch pasture in Brazil on greenhouse gases. J Environ Manage 206:980–988CrossRefGoogle Scholar
  4. Bentz C, Cornaz D, Ries B (2013) Packing and covering with linear programming: a survey. Eur J Oper Res 227(3):409–422CrossRefGoogle Scholar
  5. Borgonovo E, Buzzard GT, Wendell RE (2018) A global tolerance approach to sensitivity analysis in linear programming. Eur J Oper Res 267(1):321–337CrossRefGoogle Scholar
  6. Capitanescu F, Marvuglia A, Benetto E, Ahmadi A, Tiruta-Barna L (2017) Linear programming-based directed local search for expensive multi-objective optimization problems: application to drinking water production plants. Eur J Oper Res 262(1):322–334CrossRefGoogle Scholar
  7. Chandra A, Chandra H (2004) Impact of Indian and imported coal on indian thermal power plants. J Sci Ind Res 63:156–162Google Scholar
  8. Charkhgard H, Savelsbergh M, Talebian M (2018) A linear programming based algorithm to solve a class of optimization problems with a multi-linear objective function and affine constraints. Comput Oper Res 89:17–30CrossRefGoogle Scholar
  9. Choobineh FF, Asef-Vaziri A, Huang X (2012) fleet sizing of automated guided vehicles: a linear programming approach based on closed queuing networks. Int J Prod Res 50(12):3222–3235CrossRefGoogle Scholar
  10. Daham H, Yang X, Warnes M (2017) An efficient mixed integer programming model for pairing containers in inland transportation based on the assignment of orders. J Oper Res Soc 68(6):678–694CrossRefGoogle Scholar
  11. de Almeida PN, Dias LC (2012) Value-based DEA models: application-driven developments. J Oper Res Soc 63(1):16–27CrossRefGoogle Scholar
  12. Dountio EG, Meukam P, Tchaptchet DLP, Ango LEO, Simo A (2016) Electricity generation technology options under the greenhouse gases mitigation scenario: case study of Cameroon. Energy Strategy Reviews 13–14:191–211CrossRefGoogle Scholar
  13. Falsini D, Fondi F, Schiraldi MM (2012) A logistics provider evaluation and selection methodology based on AHP, DEA and linear programming integration. Int J Prod Res 50(17):4822–4829CrossRefGoogle Scholar
  14. Färe R, Grosskopf S, Karagiannis G, Margaritis D (2017) Data envelopment analysis and its related linear programming models. Ann Oper Res 250(1):37–43CrossRefGoogle Scholar
  15. García J, Florez JE, Torralba Á, Borrajo D, López CL, García-Olaya Á, Sáenz J (2013) Combining linear programming and automated planning to solve intermodal transportation problems. Eur J Oper Res 227(1):216–226CrossRefGoogle Scholar
  16. Gendron B, Hanafi S, Todosijević R (2018) Metaheuristics based on iterative linear programming and slope scaling for multicommodity fixed charge network design. Eur J Oper Res.  https://doi.org/10.1016/j.ejor.2018.01.022 Google Scholar
  17. Ghaderi M, Ruiz F, Agell N (2017) A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding. Eur J Oper Res 259(3):1073–1084CrossRefGoogle Scholar
  18. Ghodke S, Kumar R, Singh N, Khandelwal H (2012) Estimation of greenhouse gas emission from indian coal based thermal power plants. IOSR Journal of Engineering 2(4):591–597CrossRefGoogle Scholar
  19. Govindan K, Sivakumar R (2016) Green supplier selection and order allocation in a low-carbon paper industry: integrated multi-criteria heterogeneous decision-making and multi-objective linear programming approaches. Ann Oper Res 238(1/2):243–276CrossRefGoogle Scholar
  20. Henderson B, Golub A, Pambudi D, Hertel T, Godde C, Herrero M, Cacho O, Gerber P (2018) The power and pain of market-based carbon policies: a global application to greenhouse gases from ruminant livestock production. Mitig Adapt Strat Glob Change 23(3):349–369CrossRefGoogle Scholar
  21. Hwang K-L, Choi S-M, Kim M-K, Heo J-B, Zoh K-D (2017) Emission of greenhouse gases from waste incineration in Korea. J Environ Manage 196:710–718CrossRefGoogle Scholar
  22. Kim D-G, Kirschbaum MUF (2015) The effect of land-use change on the net exchange rates of greenhouse gases: a compilation of estimates. Agr Ecosyst Environ 208:114–126CrossRefGoogle Scholar
  23. Koçyiğit Ç, Bayrak H, Pınar M (2018) Robust auction design under multiple priors by linear and integer programming. Ann Oper Res 260(1/2):233–253CrossRefGoogle Scholar
  24. Kulturel-Konak S, Konak A (2013) Linear programming based genetic algorithm for the unequal area facility layout problem. Int J Prod Res 51(3):4302–4324CrossRefGoogle Scholar
  25. La Notte A, Tonin S, Lucaroni G (2018) Assessing direct and indirect emissions of greenhouse gases in road transportation, taking into account the role of uncertainty in the emissions inventory. Environ Impact Assess Rev 69:82–93CrossRefGoogle Scholar
  26. Luathep P, Sumalee A, Lam WHK, Li Z-C, Lo HK (2011) Global optimization method for mixed transportation network design problem: a mixed-integer linear programming approach. Transportation Research Part B: Methodological 45(5):808–827CrossRefGoogle Scholar
  27. Mansini R, Ogryczak W, Speranza MG (2014) Twenty years of linear programming based portfolio optimization. Eur J Oper Res 234(2):518–535CrossRefGoogle Scholar
  28. Mantoam EJ, Romanelli TL, Gimenez LM (2016) Energy demand and greenhouse gases emissions in the life cycle of tractors. Biosys Eng 151:158–170CrossRefGoogle Scholar
  29. Massé P, Gibrat R (1957) Application of linear programming to investments in the electric power industry. Manage Sci 3(2):149–166CrossRefGoogle Scholar
  30. Ministry of Environment and Forests, Government of India (2010) India: Greenhouse Gas Emissions 2007. http://www.moef.nic.in/downloads/public-information/Report_INCCA.pdf, Accessed 9 September, 2017
  31. Mittal, M.L., Sharma, C. and Singh, R. (2012) Estimates of Emissions from Coal Fired Thermal Power Plants in India. Available at https://www3.epa.gov/ttnchie1/conference/ei20/session5/mmittal.pdf. Accessed 9 September, 2017
  32. Molina F, Morabito R, de Araujo SA (2016) MIP models for production lot sizing problems with distribution costs and cargo arrangement. J Oper Res Soc 67(11):1395–1407CrossRefGoogle Scholar
  33. Nazari A, Thiruvady D, Aleti A, Moser I (2016) A mixed integer linear programming model for reliability optimization in the component deployment problem. J Oper Res Soc 67(8):1050–1060CrossRefGoogle Scholar
  34. Niu H, Zhao X, Gao R (2015) train scheduling for minimizing passenger waiting time with time-dependent demand and skip-stop patterns: nonlinear integer programming models with linear constraints. Transportation Research Part B: Methodological 76:117–135CrossRefGoogle Scholar
  35. Ozceylan E, Paksoy T (2013) Fuzzy multi-objective linear programming approach for optimizing a closed-loop supply chain network. Int J Prod Res 51(8):2443–2461CrossRefGoogle Scholar
  36. Pradhan BB, Shrestha RM, Hoa NT, Matsuoka Y (2017) Carbon prices and greenhouse gases abatement from agriculture, forestry and land use in Nepal. Glob Environ Change 43:26–36CrossRefGoogle Scholar
  37. Tan RR, Aviso KB, Cayamanda CD, Chiu ASF, Promentilla MAB, Ubando AT, Yu KDS (2016) A fuzzy linear programming enterprise input–output model for optimal crisis operations in industrial complexes. Int J Prod Econ 181(Part B):410–418CrossRefGoogle Scholar
  38. Tempelmeier H, Hilger T (2015) Linear programming models for a stochastic dynamic capacitated lot sizing problem. Comput Oper Res 59:119–125CrossRefGoogle Scholar
  39. Tempelmeier H, Hilger T (2018) Corrigendum: linear programming models for a stochastic dynamic capacitated lot sizing problem. Comput Oper Res 91:258–259CrossRefGoogle Scholar
  40. Temple J (2018) Sending heat into space: skycool’s advanced materials could reinvent air conditioning and refrigeration-cutting costs and greenhouse gases in the process. MIT Technology Review 121(1):84–91Google Scholar
  41. Umetani S, Fukushima Y, Morita H (2017) A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system. Omega 67:115–122CrossRefGoogle Scholar
  42. van Pelt TD, Fransoo JC (2018) A note on linear programming models for a stochastic dynamic capacitated lot sizing problem. Comput Oper Res 89:13–16CrossRefGoogle Scholar
  43. Wang H-F, Zheng K-W (2013) Application of fuzzy linear programming to aggregate production plan of a refinery industry in Taiwan. J Oper Res Soc 64(2):169–184CrossRefGoogle Scholar
  44. Wang EJ, Chen YC, Wang WS, Su TS (2010) Analysis of outsourcing cost-effectiveness using a linear programming model with fuzzy multiple goals. Int J Prod Res 48(2):501–523CrossRefGoogle Scholar
  45. Wu Y, Zhang L (2017) Can the development of electric vehicles reduce the emission of air pollutants and greenhouse gases in developing countries? Transp Res Part D 51:129–145CrossRefGoogle Scholar
  46. Yang L, Zhou X (2017) Optimizing on-time arrival probability and percentile travel time for elementary path finding in time-dependent transportation networks. Transportation Research Part B: Methodological 96:68–91CrossRefGoogle Scholar
  47. Yin J, Yang L, Tang T, Gao Z, Ran B (2017) Dynamic passenger demand oriented metro train scheduling with energy efficiency and waiting time minimization: mixed-integer linear programming approaches. Transportation Research Part B: Methodological 97:182–213CrossRefGoogle Scholar

Copyright information

© Indian Institute of Management Calcutta 2018

Authors and Affiliations

  1. 1.Indian Institute of Management CalcuttaJoka, KolkataIndia

Personalised recommendations