Lévy-Enhanced Swarm Intelligence for Optimizing a Multiobjective Biofuel Supply Chain

2020 ◽  
pp. 287-309 ◽  
Author(s):  
T. Ganesan ◽  
Pandian Vasant
Keyword(s):  
Supply Chain ◽  
Swarm Intelligence ◽  
Large Scale ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cuckoo Search ◽  
Biofuel Supply Chain ◽  
Solution Methods ◽  
The Face ◽  
Chain Problem

Engineering systems are currently plagued by various complexities and uncertainties. Metaheuristics have emerged as an essential tool for effective engineering design and operations. Nevertheless, conventional metaheuristics still struggle to reach optimality in the face of highly complex engineering problems. Aiming to further boost the performance of conventional metaheuristics, strategies such as hybridization and various enhancements have been added into the existing solution methods. In this work, swarm intelligence techniques were employed to solve the real-world, large-scale biofuel supply chain problem. Additionally, the supply chain problem considered in this chapter is multiobjective (MO) in nature. Comparative analysis was then performed on the swarm techniques. To further enhance the search capability of the best solution method (GSA), the Lévy flight component from the Cuckoo Search (CS) algorithm was incorporated into the Gravitational Search Algorithm (GSA) technique; developing the novel Lévy-GSA technique. Measurement metrics were then utilized to analyze the results.

scholarly journals Cuckoo search algorithm for multi-objective supply chain problem

2018 ◽  
Vol 189 ◽  
pp. 06001 ◽  
Author(s):  
Fathy Elkazzaz ◽  
Abdelmageed Mahmoud ◽  
Ali Maher
Keyword(s):  
Supply Chain ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Chain Model ◽  
Multi Objective ◽  
Optimum Configuration ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Chain Problem

A meta-heuristic algorithm called, the cuckoo search algorithm is proposed in dealing with the multi-objective supply chain model to find the optimum configuration of a given supply chain problem which minimizes the total cost and the total lead-time. The supply chain problem utilized in this study is taken from literature to show the performance of the proposed model; in addition, the results have been compared to those achieved by the bee colony optimization algorithm and genetic algorithm. Those obtained results indicate that the proposed cuckoo search algorithm is able to get better Pareto solutions (non-dominated set) for the supply chain problem.

Multiobjective Optimization of a Biofuel Supply Chain Using Random Matrix Generators

2020 ◽  
pp. 206-232 ◽  
Author(s):  
Timothy Ganesan ◽  
Pandian Vasant ◽  
Igor Litvinchev
Keyword(s):  
Supply Chain ◽  
Random Matrix ◽  
Large Scale ◽  
Cuckoo Search ◽  
Optimization Techniques ◽  
Computational Results ◽  
Matrix Approach ◽  
Industrial Systems ◽  
Biofuel Supply Chain ◽  
Effective Design

As industrial systems become more complex, various complexities and uncertainties come into play. Metaheuristic-type optimization techniques have become crucial for effective design, maintenance, and operations of such systems. However, in highly complex industrial systems, conventional metaheuristics are still plagued by various drawbacks. Strategies such as hybridization and algorithmic modifications have been the focus of previous efforts to improve the performance of conventional metaheuristics. This work tackles a large-scale multi-objective (MO) optimization problem: biofuel supply chain. Due to the scale and complexity of the problem, the random matrix approach was employed to modify the stochastic generator segment of the cuckoo search (CS) technique. Comparative analysis was then performed on the computational results produced by the conventional CS technique and the improved CS variants.

scholarly journals Random Matrix Generators for Optimizing a Fuzzy Biofuel Supply Chain System

2020 ◽  
Vol 4 (1) ◽  
pp. 33 ◽  
Author(s):  
Timothy Ganesan ◽  
Pandian Vasant ◽  
Pratik Sanghvi ◽  
Joshua Thomas ◽  
Igor Litvinchev
Keyword(s):  
Supply Chain ◽  
Random Matrix ◽  
Large Scale ◽  
Matrix Theory ◽  
Fuzzy Optimization ◽  
Cuckoo Search ◽  
Supply Chain System ◽  
Creative Commons ◽  
Biofuel Supply Chain ◽  
Biomass Power Plant

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.

Biofuel Supply Chain Optimization Using Lévy-Enhanced Swarm Intelligence

2020 ◽  
pp. 169-197
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Optimization Techniques ◽  
Optimal Decision ◽  
Supply Chain Optimization ◽  
Industrial Systems ◽  
Planning Optimization ◽  
Biofuel Supply Chain ◽  
Solution Methods ◽  
Optimal Decision Making

Supply chain planning/optimization presents various challenges to decision makers globally due to its highly complicated nature as well as its large-scale structure. Over the years various state-of-the-art methods have been employed to model supply chains. Optimization techniques are then applied to such models to help with optimal decision making. However, with highly complex industrial systems such as these, conventional metaheuristics are still plagued by various drawbacks. Strategies such as hybridization and algorithmic modifications have been the focus of previous efforts to improve the performance of conventional metaheuristics. In light of these developments, this chapter presents two solution methods for tackling the biofuel supply chain problem.

scholarly journals Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

2021 ◽  
Vol 11 (10) ◽  
pp. 4438
Author(s):  
Satyendra Singh ◽  
Manoj Fozdar ◽  
Hasmat Malik ◽  
Maria del Valle Fernández Moreno ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Methods ◽  
Bidding Strategy ◽  
Wind Speed Profile ◽  
Gravitational Search ◽  
Modified Gravitational Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

scholarly journals Improved Compact Cuckoo Search Algorithm Applied to Location of Drone Logistics Hub

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 333 ◽  
Author(s):  
Jeng-Shyang Pan ◽  
Pei-Cheng Song ◽  
Shu-Chuan Chu ◽  
Yan-Jun Peng
Keyword(s):  
Rural Areas ◽  
Large Scale ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Development Stage ◽  
Cuckoo Search Algorithm ◽  
Initial Development ◽  
Logistics Systems ◽  
The Cost ◽  
The Village

Drone logistics can play an important role in logistics at the end of the supply chain and special environmental logistics. At present, drone logistics is in the initial development stage, and the location of drone logistics hubs is an important issue in the optimization of logistics systems. This paper implements a compact cuckoo search algorithm with mixed uniform sampling technology, and, for the problem of weak search ability of the algorithm, this paper combines the method of recording the key positions of the search process and increasing the number of generated solutions to achieve further improvements, as well as implements the improved compact cuckoo search algorithm. Then, this paper uses 28 test functions to verify the algorithm. Aiming at the problem of the location of drone logistics hubs in remote areas or rural areas, this paper establishes a simple model that considers the traffic around the village, the size of the village, and other factors. It is suitable for selecting the location of the logistics hub in advance, reducing the cost of drone logistics, and accelerating the large-scale application of drone logistics. This paper uses the proposed algorithm for testing, and the test results indicate that the proposed algorithm has strong competitiveness in the proposed model.

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