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Review on recent implementations of multiobjective and multilevel optimization in sustainable energy economics
(2022)
Ganesan, Timothy
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Litvinchev, Igor
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Marmolejo Saucedo, José Antonio
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Thomas, J. Joshua
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Vasant, Pandian
Rapid progress is currently being made globally in the sustainable energy industry. This trend has been seen to concentrate on specific focus areas in the global energy ecosystem. The integration of sustainability ideas into the existing energy ecosystem has given rise to various complexities, e.g., multilevel and multiobjective (MO) scenarios. This in return has generated various avenues for the implementation of mathematical optimization as well as state-of-the-art operations research methodologies on such real-world systems. This chapter aims to provide a concise review on recent implementations of MO and multilevel optimization on sustainable energy economic systems. Three key industrial areas are given emphasis—economic load/emission dispatch, bioenergy supply chains, and sustainable capacity planning. © 2022 Elsevier Inc. All rights reserved.
Scopus© Citations 1 18 1
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A new heuristic algorithm to solve Circle Packing problem inspired by nanoscale electromagnetic fields and gravitational effects
(2018)
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Marmolejo Saucedo, José Antonio
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Murillo-Suarez, Alfonso
In this paper, we present a new algorithm for the fast and efficient solution of the Packing problem in two dimensions. The packing problem consists in finding the best arrangement of objects (many geometrical forms) in a specific space called container.This new algorithm is inspired by the observations of nanometric scale electromagnetic fields. We use the electromagnetic theory of the electric field to calculate the best position to place a circular object in a configuration of other circular objects previously packing. Also, in this new algorithm we simulate two processes called »gravity» and »shaken» that compact the distribution of the objects placed in the container and allow to minimize the unoccupied space. © 2018 IEEE.
Scopus© Citations 3 11 1
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Binary monkey algorithm for approximate packing non-congruent circles in a rectangular container
(2018)
Torres-Escobar, Rafael
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Marmolejo Saucedo, José Antonio
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Litvinchev, Igor
A Packing problem consists in the best arrangement of several objects inside a bounded area named as the container. This arrangement must fulfill with technological constraints, for example, objects should not be overlapping. Some packing models for circular objects are typically formulated as non-convex optimization problems; where the continuous variables are the coordinates of the objects, so they are limited to not finding optimal solutions. Due to the combinatorial nature in the arrangement of such objects, heuristic methods are being used extensively which combine methods of global search and methods of local exhaustive search of local minima or their approximations. In this paper, we will address the packing problem for non-congruent (different size) circles with the binary version of the monkey algorithm which incorporates a cooperation process and a greedy strategy. We use a rectangular grid for covering the container. Every node in the grid represent potential positions for a circle. In this sense, binary monkey algorithm for the knapsack problem, can be used to solve de 0–1 approximate packing problem for non-congruet circles. The binary monkey problem uses two additional processes of the original monkey algorithm, these two processes are a greedy process and a cooperation processes. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
Scopus© Citations 23 12 1
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A new algorithm for optimization of quality of service in peer to peer wireless mesh networks
(2019)
Gheisari, Mehdi
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Alzubi, Jafar
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Xiaobo, Zhang
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Kose, Utku
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Marmolejo Saucedo, José Antonio
Nowadays, wireless mesh networks are known as important parts of different commercial, scientific, and industrial processes. Their prevalence increases day-by-day and the future of the world is associated with such technologies for better communication. However, the issue of improving quality of service for dealing with more complex and intense flow of data has been always a remarkable research problem, as a result of improved wireless communication systems. In this sense, objective of this study is to provide a new algorithm for contributing to the associated literature. In the study, peer to peer wireless mesh networks and the concept of service quality were examined first and then an approach for improving service quality in such networks has been proposed accordingly. In detail, the proposed an approach allows profiting data transfer capability by data packet and using this information for routing and preventing overcrowd in network nodes and finally, distributing the load over it. When middle nodes overcrowd, they withhold to send control messages of route creating or do that by delay. The proposed approach has been evaluated and the findings revealed that at least 10% of undue delays through network can be prevented while permittivity does not reduce, thanks to the approach. Also energy consumption within network nodes partially increases due to adding table and the search which can be overlooked. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Scopus© Citations 38 32 1
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μ𝜃-EGF: A New Multi-Thread and Nature-Inspired Algorithm for the Packing Problem
(2020)
García-Jacas, César R.
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Marmolejo Saucedo, José Antonio
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Murillo-Suarez, Alfonso
In this paper, the authors present a new algorithm efficient solution to the packing problem in two dimensions. The authors propose a new heuristic using the value of the electromagnetic field to determine the best position to place a circular object in a configuration of other circular objects previously packed. Also, this algorithm simulates two processes to compact objects already placed, inspired by gravitational forces, to minimize the empty space in the container and maximizing the number of objects in the container. To determine the efficacy of this algorithm, the authors carried out experiments with twenty-four instances. Parallel computing can contribute to making decision processes such as optimization and prediction more agile and faster. Real-time decision making involves the use of solution methodologies and algorithms. For this reason the present manuscript shows an alternative for the solution of a classic industry problem that must be solved quickly. Packaging optimization can help reduce waste of container material. The material used to transport the products can reduce its environmental impact due to an efficient packaging process. Light-weighting can also be accomplished by reducing the amount of packaging material used. © Springer Nature
36 1
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Digital twin framework for large-scale optimization problems in supply chains: a case of packing problem
(Springer, 2021)
Marmolejo Saucedo, José Antonio
The development of new information technologies at the beginning of the 21st century allows the integration between the physical and the virtual world. In Engineering, an emerging technology called digital twins is presented as the mechanism to virtualize the operation of devices, machines and processes. In industrial engineering and specifically in supply chains there is a growing interest in the development of digital twins. For this reason, this paper proposes the integration of large-scale optimization problems in a digital platform that allows the solution of these problems for decision-making in real time. Bin-Packing and Vehicle Routing problems are addressed through the interface of a commercial supply chain management platform and heuristic optimization algorithms. We use technology based on simulation of discrete events to achieve the periodic decisions that make up the Digital Supply ChainTwin engine. A hypothetical case solution is presented to verify the performance of the proposed development. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Scopus© Citations 16 30 2
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Quantum-behaved bat algorithm for solving the economic load dispatch problem considering a valve-point effect
(2020)
Vasant, Pandian
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Parvez Mahdi, Fahad
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Marmolejo Saucedo, José Antonio
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Litvinchev, Igor
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Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in order to minimize total fuel cost of the system, while satisfying all other constraints. To make the system more applicable to the real-world problem, a valve-point effect is considered here with the ELD problem. QBA is applied in 3-unit, 10-unit, and 40-unit power generation systems for different load demands. The obtained result is then presented and compared with some well-known methods from the literature such as different versions of evolutionary programming (EP) and particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA) and hybrid ABC_PSO. The comparison of results shows that QBA performs better than the above-mentioned methods in terms of solution quality, convergence characteristics and computational efficiency. Thus, QBA proves to be an effective and a robust technique to solve such nonlinear optimization problem.
Scopus© Citations 7 16 2

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