New Fuzzy Parametric Linear Programming Approaches For Optimising The Performance Of Crude Oil Companies In Iraq
Refineries and crude oil industries are amongst the most important revenue generators of oil-producing countries. Iraq is an oil-exporting country. Although it has many refineries to process and refine crude oil, it suffers from severe production crises that ravage it from time to time. This study a...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.usm.my/60422/1/AL%20SAEEDI%20RAFID%20ABDULMAHDI%20JASIM%20-%20TESIS%20cut.pdf |
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| Summary: | Refineries and crude oil industries are amongst the most important revenue generators of oil-producing countries. Iraq is an oil-exporting country. Although it has many refineries to process and refine crude oil, it suffers from severe production crises that ravage it from time to time. This study aims to optimise the scheduling, production planning and inventory control of multisite refineries located in Iraq. The study sample was taken from the Iraqi Midland Refineries (IMR) of the Iraqi Petroleum Company (IPC) company, which includes four refineries in four different sites in Iraq. In this research, different approaches based on fuzzy set theory are considered as demand and cost are explained as fuzzy. The parametric fuzzy linear programming (PFLP) problem is suggested on the basis of symmetric triangular fuzzy numbers (STFNs). Technically, this model involves three methods. The first method utilises the concept of the Spread Function (SF). We utilise two different functions of SFs to find the parameter values of λ and maximise the PFLP model. The second method is based on fractal analysis, where the fractional power λ of the fractal is the parameter used in our study. Lastly, the third method is based on fractional Tsallis entropy of order λ. Results indicate that the established models can deliver valuable information to the maximal profit production approach to evaluate oil refineries in a parametric fuzzy setting. |
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