Abstract
Complex industrial systems often contain various uncertainties. Hence sophisticated fuzzy optimization (metaheuristics) techniques have become commonplace; and are currently indispensable for effective 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 fluctuations in the annual electricity generation output of the biomass power plant [kWh/year]. The details of these investigations are presented and analyzed.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
Highlights
The following frameworks have been introduced in the past for tackling MO optimization problems: Strength Pareto Evolutionary Algorithm (SPEA-2) (Zhao et al.[69]), Weighted sum approach (Naidu et al [46]), Normal-Boundary Intersection (NBI) (Ahmadi et al.[2]; Ganesan et al.[23]) and Non-Dominated Sorting Genetic Algorithm (NSGA-II) (Mousavi et al.[45])
Scalarization and NBI approaches involve the aggregation of multiple target objectives
The objective functions of the fuzzy MO biofuel supply chain problem was combined into a single function using the weighted sum approach (Kalita et al.[34])
Summary
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. To account for these uncertainties the authors employed a fuzzy multiple objective linear programming to solve the problem Another eective implementation of fuzzy framework for biofuel supply chains could be seen in the work of Balaman et al [9]. In the work of Balaman et al [10], the authors developed a novel decision model to obtain the optimal supply chain conguration and district heating system to meet the thermal demand of a certain locality To this end, the authors formulated and validated a Fuzzy Mixed Integer Linear Programming (MILP) which consists of multiple types of biomass and systemic uncertainties.
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