The Coupled Operational Systems: A Linear Optimisation Review
Source: By:Said El Noshokaty
DOI: https://doi.org/10.30564/jesr.v2i2.428
Abstract:The purpose of this review is to summarise the existing literature on the operational systems as to explain the current state of understanding on the coupled operational systems. The review only considers the linear optimisation of the operational systems. Traditionally, the operational systems are classified as decoupled, tightly coupled, and loosely coupled. Lately, the coupled operational systems were classified as systems of time-sensitive and time-insensitive operational cycle, systems employing one mix and different mixes of factors of production, and systems of single-linear, single-linear-fractional, and multi-linear objective. These new classifications extend the knowledge about the linear optimisation of the coupled operational systems and reveal new objective-improving models and new state-of-the-art methodologies never discussed before. Business areas affected by these extensions include product assembly lines, cooperative farming, gas/oil reservoir development, maintenance service throughout multiple facilities, construction via different locations, flights traffic control in aviation, game reserves, and tramp shipping in maritime cargo transport.
References:[1] Baker, K. Models for Tightly-Coupled Production Systems, In: Sarin R.K. (Eds) Perspectives in Operations Management. Springer, Boston, MA, 1993: 321-339. [2] Bowman, E. Assembly-Line Balancing by Linear Programming. Operations Research, 1960: 385-389 DOI: org/10.1287/opre.8.3.385 [3] Lau, H. A Directly-Coupled Two-Stage Unpaced Line. IIE Transactions, 1986, 18(3): 304-312. [4] Balogun, O., Jolayemi, E., Akingbade, T., and Muazu, H. Use Of Linear Programming For Optimal Production In A Production Line In Coca –Cola Bottling Company, Ilorin. International Journal of Engineering Research and Applications, 2012, 2(5): 2004-2007. [5] Akpinar, S. and Baykasoğlu A. Modeling and solving mixed-model assembly line balancing problem with setups. Part I: A mixed integer linear programming model. Journal of Manufacturing Systems, 2014, 33(1): 177-187. [6] Barathwaj, N., Raja, P., and Gokulraj, S. Optimization of assembly line balancing using genetic algorithm. J. Cent. South Univ., 2015: 3957−3969 DOI: 10.1007/s11771-015-2940-9 [7] Ritt, M. and Costa, A. Improved integer programming models for simple assembly line balancing and related problems. Intl. Trans. in Op. Res., 2015: 1-15 DOI: 10.1111/itor.12206 [8] Mohebalizadehgashti, F. Balancing, Sequencing and Determining the Number and Length of Workstations in a Mixed Model Assembly Line. a master thesis presented to the University of Guelph, Ontario, Canada, 2016. [9] Alghazi, A., Balancing and Sequencing Mixed Model Assembly Line, a Ph. D. thesis in industrial engineering presented to the Clemson University, South Carolina, USA, 2017. [10] Hillier, F., Introduction to Operations Research, 6th edition, McGraw-Hill, UK, 1995. [11] Owen, S. and Daskin, M. Strategic facility location: A review. European Journal of Operational Research, 1998, 111(3): 423-447. [12] Sarmiento, A. and Nagi, R. A review of integrated analysis of production-distribution systems. IIE Transactions, 1999, 31(11): 1061-1074. [13] Humayd, A. Distribution System Planning with Distributed Generation: Optimal versus Heuristic Approach. A master’s thesis presented to the University of Waterloo, Waterloo, Ontario, Canada, 2011. [14] Omu, A., Choudhary, R., and Boies, A. Distributed energy resource system optimisation using mixed integer linear programming. Energy Policy, 2013, 61(1): 249-266. [15] Gupta, D. Strategic Allocation of Resources Using Linear Programming Model with Parametric, a master’s thesis presented to the Ludwig Maximilian University of Munich, Munich, Germany, 2013. [16] Sofi, N., Ahmed, A., Ahmed, M., and Behat, B. Decision Making in Agriculture: A Linear Programming Approach. International Journal of Modern Mathematical Sciences, 2015, 13(2): 160-169. [17] Khor, C., Elkamel, A., and Shah, N. Optimization methods for petroleum fields development and production systems: a review. Optimization and Engineering. DOI: 10.1007/s11081-017-9365-2 [18] Rios, J. and Ross, K. ‘Massively parallel dantzig-wolfe decomposition applied to traffic flow scheduling’, Journal of Aerospace Computing, Information, and Communication, 2010, 7, (1): 32-45. [19] Kondoh, S., Komoto, H., and Salmi, T., Resource sharing method among multiple production systems to reduce initial investment for inverse manufacturing, In: Matsumoto M., Umeda Y., Masui K., Fukushige S. (Eds) Design for Innovative Value Towards a Sustainable Society. Springer, Dordrecht, 2011: 629-634. [20] Torgnes, E., Gunnerud, V., Hagem, E., Ronnqvist, M., and Foss, B. Parallel Dantzig–Wolfe decomposition of petroleum production allocation problems. Journal of the Operational Research Society, 2012, 63(7): 950–968. [21] Abrache, J., Crainic, T., Gendreau, M., and Aouam, T. A Dantzig-Wolfe Auction Mechanism for Multilateral Procurement, CIRRELT-2014-70, Montreal, Canada, 2014. [22] El Noshokaty, S. Linear optimization of operational systems: New extensions to the coupled systems. Create Space, Amazon, USA, 2017. [23] Dantzig G. and Wolfe P. Decomposition Principle for Linear Programs. Operations Research, 1960, 8(1): 101-111. [24] El Noshokaty S. ‘Global optimization in multiple production systems of time-sensitive production cycle’, Journal of Advanced Research in Economics & Business Management, 2018, 5(1 & 2): 1-4. [25] El Noshokaty, S. Shipping Optimisation Systems (SOS): Liner Optimisation Perspective. International Journal of Shipping and Transport Logistics, 2013, 5(3): 237-256. [26] El Noshokaty, S. Shipping Optimisation Systems (SOS): Tramp Optimisation Perspective. Journal of Shipping and Trade, 2017: 1-36. DOI 10.1186/s41072-017-0021-y [27] El Noshokaty, S. Block-Angular Linear Ratio Programmes. International Journal of Operational Research, 2014, 19(3): 338-357. [28] El Noshokaty S. Global optimization in multiple production systems employing different mixes of production factors in the production cycle. Journal of Advanced Research in Economics & Business Management, 2018, 5(3): 15-18.