Random matrix generators for optimizing a fuzzy biofuel supply chain system

Complex industrial systems often contain various uncertainties. Hence sophisticated fuzzy optimization (metaheuristics) techniques have become commonplace; and are currently indispensable for e ective design, maintenance and operations of such systems. Unfortunately, such state-of-the-art techniques suffer several drawbacks when applied to largescale problems. In line of improving the performance of metaheuristics in those, this work proposes the fuzzy random matrix theory (RMT) as an add-on to the cuckoo search (CS) technique for solving the fuzzy large-scale multiobjective (MO) optimization problem; biofuel supply chain. The fuzzy biofuel supply chain problem accounts for uncertainties resulting from uctuations in the annual electricity generation output of the biomass power plant [kWh/year]. The details of these investigations are presented and analyzed.
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Random matrix generators for optimizing a fuzzy biofuel supply chain system VOLUME: 4 | ISSUE: 1 | 2020 | March Random Matrix Generators for Optimizing a Fuzzy Biofuel Supply Chain System 1 2,∗ 1 Timothy GANESAN , Pandian VASANT , Pratik SANGHVI , 3 4 Joshua THOMAS , Igor LITVINCHEV Royal Bank of Canada, Canada 1 Universiti Teknologi Petronas, Malaysia 2 3 UOW Malaysia, KDU Penang University College, Malaysia 4 Nuevo Leon State University, Mexico *Corresponding Author:Pandian VASANT (Email: pvasant@gmail.com) (Received: 12-Nov-2019; accepted: 3-Feb-2020; published: 31-Mar-2020) DOI: http://dx.doi.org/10.25073/jaec.202041.268 1. Introduction Abstract. Complex industrial systems of-ten contain various uncertainties. Hence sophis- Industrial optimization often involves systemsticated fuzzy optimization (metaheuristics) tech- containing various complexities and uncertain-niques have become commonplace; and are cur- ties - thus requiring heavy computational eortrently indispensable for eective design, main- when performing optimization. In such scenar-tenance and operations of such systems. Un- ios metaheuristics play a prominent role (Gane-fortunately, such state-of-the-art techniques suf- san et al.[25]; Ganesan et al.[26]; Yang [66];fer several drawbacks when applied to large- Ganesan et al. [24]; Ganesan et al. [27]; Hongscale problems. In line of improving the per- et al.[32]; Dong et al. [21]). Decision mak-formance of metaheuristics in those, this work ers are globally facing various optimization chal-proposes the fuzzy random matrix theory (RMT) lenges when optimizing supply chains - this isas an add-on to the cuckoo search (CS) tech- attributed to its large-scale and complex struc-nique for solving the fuzzy large-scale multiobjec- ture. Currently various state-of-the-art toolstive (MO) optimization problem; biofuel supply have been developed to overcome these chal-chain. The fuzzy biofuel supply chain problem lenges where they have been used to:accounts for uncertainties resulting from uctu-ations in the annual electricity generation out- • Model these supply chains (Seuring [55];put of the biomass power plant [kWh/year]. The Brandenburg et al. [12]; Ahi and Searcydetails of these investigations are presented and [3])analyzed. • ciently optimize the decision making pro- cess (Ogunbanwo et al. [47]; Mastrocinque et al. [43])Keywords Fuel supply chains have broad applications span-Random matrix theory, fuzzy framework, ning across diverse industrial sectors. For in-cuckoo search, biofuel supply chain, stance in Lin et al. [38], the annual biomass-multiobjective (MO), large-scale opti- ethanol production cost in a fuel supply chainmization. was minimized. In that work, the large-scalec 2020 Journal of Advanced Engineering and Computation (JAEC) 33 VOLUME: 4 | ISSUE: 1 | 2020 | Marchsupply chain model consisted of: stacking, ues (using appropriate fuzzy methods) into crispin-eld preprocessing, transportation, trans- values of research variable dimensions. A moreportation, biomass harvesting, packing/stor- practical work could be seen in Babazadeh [6].age, ethanol production and ethanol distribu- In that work, the author developed a novel fuzzytion. Aiming to reduce the cost of produc- framework for a bioenergy supply chain: thetion in a biorenery (to approximately 62%), possibilistic programming model based on pos-the researchers used the mixed integer program- sibilistic mean and absolute deviation of fuzzyming technique. Another interesting work on a numbers. The model performance was evalu-switchgrass-based bioethanol supply chain (lo- ated by using data from a real-world case studycated in North Dakota, U.S) was presented in and it was shown that the proposed method per-the work of Zhang et al. [71]. In that work formed better than a pure possibilistic program-the supply chain system was modeled and op- ming model. A similar work can be seen in Lintimized using mixed integer linear programming et al. [37]. In that work the uncertain factorsto attain the optimal utilization of marginal land considered were the demand of biomass energyfor switchgrass production. The end goal for (due to unstable price of fossil fuels) and thethat work was to establish an economical and number of job oer opportunities springing upsustainable harvest of bioethanol. In Osmani from the energy facilities. To account for theseand Zhang [49], a sustainable dual feedstock uncertainties the authors employed a ...