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.

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.

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 Decision-Making and Performance Analysis of Closed-Loop Supply Chain under Different Recycling Modes and Channel Power Structures

2019 ◽  
Vol 11 (22) ◽  
pp. 6413 ◽  
Author(s):  
Gong ◽  
Chen ◽  
Zhuang
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Large Scale ◽  
Closed Loop ◽  
System Efficiency ◽  
Power Structures ◽  
Supply Chain System ◽  
Closed Loop Supply Chain ◽  
Channel Power ◽  
And Performance

The recycling and remanufacturing of e-waste is linked to a worldwide emphasis on the establishment and implementation of Extended Producer Responsibility system (ERP), which has become an important problem in the process of cycling economy. Meanwhile, with the development and expansion of large-scale retail enterprises, the power structure of supply chain channels is showing a tendency towards diversity as well. However, few studies on closed-loop supply chains (CLSC) have considered both recycling modes and channel power structures. We aim to explore the influence of different recycling modes and channel power structures on the optimal decisions and performance of a closed-loop supply chain (CLSC), considering three recycling channels including manufacturer recycling, retailer recycling and hybrid recycling of retailer and manufacturer and two dominant modes including manufacturer-led and retailer-led. We construct six closed-loop supply chain models under different combinations of three recycling channels and two dominant modes. We analyze the effect of different recycling channels on company decision-making under the same dominant mode, whether participating in recycling has an impact on company decision-making under different dominant modes, and the effect on supply chain members and supply chain system under different dominant modes and recycling channels. The results show that the hybrid recycling strategy is always optimal for both supply chain members; the sub-optimal recycling strategies are both recycled by the subordinate enterprise, and the worst recycling strategies are both recycled by the leading enterprise. Moreover, it is always the worst strategy for manufacturer to participate in a closed-loop supply chain dominated by retailer and recycled by retailer; participating in a closed-loop supply chain dominated by manufacturer and recycled by manufacturer is always the worst strategy for retailer. From a system point of view, system efficiency is the highest under hybrid recycling, and system efficiency is the lowest if leading company recycles separately.

End-to-End Tracing and Congestion in a Blockchain

2021 ◽  
pp. 68-91
Author(s):  
Kosala Yapa Bandara ◽  
Subhasis Thakur ◽  
John G. Breslin
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Use Case ◽  
Item Recall ◽  
Architectural Style ◽  
Supply Chain System ◽  
Chain Management ◽  
Blockchain Technology ◽  
End To End ◽  
The Uk

Modern supply chain applications are complex systems that play an important role in many different sectors. Supply chain management systems are implemented to handle increasing complexity and flows of goods. However, most of these systems are also increasing the complexity of providing trust and a global view of transactions in a distributed supply chain system. Blockchain technology introduces a new architectural style to support the traceability and trust of transactions performed by participants in a network. This chapter uses this emerging technology to realize a supply chain use case from JLP Meats in the UK with improved transparency, trust, and end-to-end querying while discussing potential challenges of realizing large-scale enterprise blockchain applications. The process of farm-to-fork is implemented and tested for traceability, item recall, block analysis, congestion enabling food safety, and sustainable agriculture. Potential challenges are highlighted in complex supply chains that need heterogeneous trade compliance and scalability.

scholarly journals Toward Large-Scale Continuous EDA: A Random Matrix Theory Perspective

2016 ◽  
Vol 24 (2) ◽  
pp. 255-291 ◽  
Author(s):  
A. Kabán ◽  
J. Bootkrajang ◽  
R. J. Durrant
Keyword(s):  
Random Matrix Theory ◽  
Random Matrix ◽  
Large Scale ◽  
Model Building ◽  
Matrix Theory ◽  
Theoretical Computer Science ◽  
Global Optimisation ◽  
Divide And Conquer ◽  
Random Projections ◽  
Large Scale Problems

Estimations of distribution algorithms (EDAs) are a major branch of evolutionary algorithms (EA) with some unique advantages in principle. They are able to take advantage of correlation structure to drive the search more efficiently, and they are able to provide insights about the structure of the search space. However, model building in high dimensions is extremely challenging, and as a result existing EDAs may become less attractive in large-scale problems because of the associated large computational requirements. Large-scale continuous global optimisation is key to many modern-day real-world problems. Scaling up EAs to large-scale problems has become one of the biggest challenges of the field. This paper pins down some fundamental roots of the problem and makes a start at developing a new and generic framework to yield effective and efficient EDA-type algorithms for large-scale continuous global optimisation problems. Our concept is to introduce an ensemble of random projections to low dimensions of the set of fittest search points as a basis for developing a new and generic divide-and-conquer methodology. Our ideas are rooted in the theory of random projections developed in theoretical computer science, and in developing and analysing our framework we exploit some recent results in nonasymptotic random matrix theory.

scholarly journals Modeling Relief Demands in an Emergency Supply Chain System under Large-Scale Disasters Based on a Queuing Network

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xinhua He ◽  
Wenfa Hu
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Mathematical Problem ◽  
Distribution Centers ◽  
Supply Chain System ◽  
Time Model ◽  
Chain System ◽  
Emergency Rescue ◽  
Rescue Center ◽  
Emergency Supply

This paper presents a multiple-rescue model for an emergency supply chain system under uncertainties in large-scale affected area of disasters. The proposed methodology takes into consideration that the rescue demands caused by a large-scale disaster are scattered in several locations; the servers are arranged in multiple echelons (resource depots, distribution centers, and rescue center sites) located in different places but are coordinated within one emergency supply chain system; depending on the types of rescue demands, one or more distinct servers dispatch emergency resources in different vehicle routes, and emergency rescue services queue in multiple rescue-demand locations. This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. Finally, a case study of an emergency supply chain system operating in Shanghai is discussed. The results demonstrate the robustness and applicability of the proposed model.

Bilevel Optimization of Taxing Strategies for Carbon Emissions Using Fuzzy Random Matrix Generators

2022 ◽  
pp. 210-234
Author(s):  
Timothy Ganesan ◽  
Irraivan Elamvazuthi
Keyword(s):  
Power Generation ◽  
Carbon Emissions ◽  
Random Matrix ◽  
Matrix Theory ◽  
Stackelberg Game ◽  
Cuckoo Search ◽  
Bilevel Optimization ◽  
Tax Rates ◽  
The Novel ◽  
Fuzzy Random

Bilevel (BL) optimization of taxing strategies in consideration of carbon emissions was carried out in this work. The BL optimization problem was considered with two primary targets: (1) designing an optimal taxing strategy (imposed on power generation companies) and (2) developing optimal economic dispatch (ED) schema (by power generation companies) in response to tax rates. The resulting interaction was represented using Stackelberg game theory – where the novel fuzzy random matrix generators were used in tandem with the cuckoo search (CS) technique. Fuzzy random matrices were developed by modifying certain aspects of the original random matrix theory. The novel methodology was tailored for tackling complex optimization systems with intermediate complexity such as the application problem tackled in this work. Detailed performance and comparative analysis are also presented in this chapter.

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