Browsing by Author "Cevallos Tapia, Carlos Patricio"

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A hybrid algorithm for supply chain optimization of assembly companies
(IEEE, 2019) Cevallos Tapia, Carlos Patricio; Sigüenza Guzmán, Lorena Catalina; Peña Ortega, Mario Patricio; Peña Ortega, Mario Patricio
A fundamental goal of any system is to get an optimal state. These optimal states can be found in different areas, such as medicine, engineering, or architecture. In the field of industrial engineering, one of its objectives is improving or optimizing company processes in order to increase benefits while reducing costs. In this context, an essential component is the supply chain, which is a network in that different entities, such as manufacturers, suppliers, distributors, retailers, transporters, and customers or end-users, are associated. Several optimization algorithms with different approaches have been developed to optimize the supply chain. Nevertheless, they still have problems to fulfill some requirements at once. This research aims to develop a hybrid optimization algorithm that leverages the capabilities of different approaches. This algorithm, which presents a multi-objective optimization schema, meets a tradeoff between the optimization results quality and the runtime. To this end, a manufacturing and assembly company is used as a case study to prove the algorithm. The results are also compared with other state-of-the-art algorithms using the same execution environment and general settings. Findings indicate that the hybrid algorithm converges in less time and in most cases, it could reach the global optimal.
Desarrollo de un algoritmo evolutivo híbrido para la optimización de una cadena de suministro de dos empresas de ensamblaje
(Universidad de Cuenca, 2020-06-17) Cevallos Tapia, Carlos Patricio; Sigüenza Guzmán, Lorena Catalina
A fundamental goal in the world is to obtain an optimal state, or rather, an accurate, precise and perfect solution for a specific problem. These optimal states can be found in different areas such as medicine, engineering or architecture. For example, Industrial Engineering has as one of its objectives to improve or optimize the processes of a company in order to obtain more benefits with lower costs. There are a lot of optimization algorithms, such as genetic, particle swarm optimization, micro-algorithms or memetic. Therefore, an optimal solution would be to take advantage of their benefits and then build a hybrid algorithm that incorporates their best features. In this manner, it is possible to find solutions to the problem much faster, in terms of runtime and convergence warranty. In this context, this research aims to find an optimization algorithm for a supply chain, which is a network that has different entities such as manufacturers, suppliers, distributors, retailers, transporters and customers or end-users. To this end, this work presents two case studies, a manufacturing, and an assembly company. The first company assembles furniture; while the second focuses on the assembly of televisions and motorcycles. In the first case study, the objective is to minimize the cost of the supply chain that is subject to several variables, such as transportation, distribution or manufacturing costs; and it is also desired to minimize the cost of product cutting. Meanwhile, in the second case, in addition to minimizing the cost of the chain, the objective is to maximize customer satisfaction. To optimize these statements, it was necessary to build functions, known as objective functions; likewise, there were several restrictions, such as the existing demand or the storage capacity of a product that has a distributor. In order to determine if this new hybrid algorithm finds the optimal solution to the problem, it was necessary to make a comparison among all the algorithms. This comparison is based on the runtime required for the algorithm to perform its work and the quality of the algorithm´s convergence. These results were found thanks to the implementation and execution of the algorithms using the same execution environment and the same general characteristics. In the end, a discussion and conclusion are made where the strongest points of the hybrid algorithm are determined, as well as, a comparison with the other algorithms and with hybrid algorithms proposed by other authors.
New hybrid algorithm for supply chain optimization
(Springer, Cham, 2021) Cevallos Tapia, Carlos Patricio; Peña Ortega, Mario Patricio; Sigüenza Guzmán, Lorena Catalina; Sigüenza Guzmán, Lorena Catalina
Optimization is the process of obtaining the best solutions to specific problems. In the literature, those problems have been optimized through a plethora of algorithms. However, these algorithms have many advantages but also disadvantages. In this article, a New Hybrid Algorithm for Supply Chain Optimization, NHA-SCO has been proposed in order to improve the benefits of objective function convergence. For the analysis of the results, three assembly companies have been utilized as case studies. These companies present their supply chains, i.e., networks where products flow from their raw material to the final products delivered to clients. These supply chains must satisfy different objectives, such as maximize benefits and service level and minimize scrap. For the evaluation of results, NHA-SCO has been compared to other well-known optimization algorithms. In the presented case studies, the NHA-SCO algorithm performs faster, or it converges in fewer iterations, obtaining similar or even better results than the other algorithms tested.