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.

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.

Biofuel Supply Chain Optimization Using Random Matrix Generators

2020 ◽  
pp. 198-215
Keyword(s):  
Supply Chain ◽  
Random Matrix ◽  
Large Scale ◽  
Supply Chain Optimization ◽  
Heavy Atoms ◽  
Large Scale Problems ◽  
Biofuel Supply Chain

Supply chain problems are large-scale problems with complex interlinked variables. This sort of characteristic closely resembles structures often encountered in the nuclei of heavy atoms (e.g., platinum, gold or rhodium). Such structures are said to have the property of universality.

scholarly journals Cuckoo Search Algorithm for Optimal Placement and Sizing of Static Var Compensator in Large-Scale Power Systems

2016 ◽  
Vol 6 (2) ◽  
pp. 59-68 ◽  
Author(s):  
Khai Phuc Nguyen ◽  
Goro Fujita ◽  
Vo Ngoc Dieu
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Search Algorithm ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Harmony Search Algorithm ◽  
Optimization Techniques ◽  
Optimal Placement ◽  
Static Var Compensator

Abstract This paper presents an application of Cuckoo search algorithm to determine optimal location and sizing of Static VAR Compensator. Cuckoo search algorithm is a modern heuristic technique basing Cuckoo species’ parasitic strategy. The Lévy flight has been employed to generate random Cuckoo eggs. Moreover, the objective function is a multiobjective problem, which minimizes loss power, voltage deviation and investment cost of Static VAR Compensator while satisfying other operating constraints in power system. Cuckoo search algorithm is evaluated on three case studies and compared with the Teaching-learning-based optimization, Particle Swarm optimization and Improved Harmony search algorithm. The results show that Cuckoo search algorithm is better than other optimization techniques and its performance is also better.

scholarly journals A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
José García ◽  
Francisco Altimiras ◽  
Alvaro Peña ◽  
Gino Astorga ◽  
Oscar Peredo
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Cuckoo Search ◽  
Crew Scheduling ◽  
Scheduling Problems ◽  
Industrial Systems ◽  
Performance Improvements ◽  
Metaheuristic Techniques ◽  
The Internet Of Things

The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

scholarly journals How does the position of firms in the supply chain affect their performance? An empirical study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Siddharth Arora ◽  
Alexandra Brintrup
Keyword(s):  
Supply Chain ◽  
Empirical Data ◽  
Large Scale ◽  
Individual Performance ◽  
Supply Network ◽  
Supply Chain Networks ◽  
Focal Firm ◽  
Cooperative Competition ◽  
Large Scale Networks ◽  
The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

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