Olivares-Benitez, Elias
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Preferred name
Olivares-Benitez, Elias
Official Name
Olivares Benítez, Elias
Alternative Name
eolivaresb
Main Affiliation
ORCID
Scopus Author ID
55574580400
Researcher ID
R-7075-2018

Research Output
Now showing 1 - 10 of 38
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Analysis of the Hiring Cost Impact with a Bi-objective Model for the Multi-depot Open Location Routing Problem

2021 , Rodriguez-Escoto, Joel-Novi , Nucamendi-Guillén, Samuel , Olivares-Benitez, Elias

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Inventory replenishment decisions model for the supplier selection problem facing low perfect rate situations

2019 , Alejo-Reyes, Avelina , Mendoza, Abraham , Olivares-Benitez, Elias

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Design of a Logistics Nonlinear System for a Complex, Multiechelon, Supply Chain Network with Uncertain Demands

2018 , Aaron Guerrero Campanur , Olivares-Benitez, Elias , Pablo A. Miranda , Rodolfo Eleazar Perez-Loaiza , Jose Humberto Ablanedo-Rosas

Industrial systems, such as logistics and supply chain networks, are complex systems because they comprise a big number of interconnected actors and significant nonlinear and stochastic features. This paper analyzes a distribution network design problem for a four-echelon supply chain. The problem is represented as an inventory-location model with uncertain demand and a continuous review inventory policy. The decision variables include location at the intermediate levels and product flows between echelons. The related safety and cyclic inventory levels can be computed from these decision variables. The problem is formulated as a mixed integer nonlinear programming model to find the optimal design of the distribution network. A linearization of the nonlinear model based on a piecewise linear approximation is proposed. The objective function and nonlinear constraints are reformulated as linear formulations, transforming the original nonlinear problem into a mixed integer linear programming model. The proposed approach was tested in 50 instances to compare the nonlinear and linear formulations. The results prove that the proposed linearization outperforms the nonlinear formulation achieving convergence to a better local optimum with shorter computational time. This method provides flexibility to the decision-maker allowing the analysis of scenarios in a shorter time.

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Operational Efficiency of Mexican Water Utilities: Results of a Double-Bootstrap Data Envelopment Analysis

2020 , Jose Humberto Ablanedo-Rosas , Aaron Guerrero Campanur , Olivares-Benitez, Elias , Sánchez-García, Jacqueline Y. , Núñez-Ríos, Juan E.

The objective of this paper is to estimate the operational efficiency of Mexican water utilities and identify the context variables that impact their efficiency. In particular, a bootstrap data envelopment analysis (DEA) and a bootstrap truncated regression analysis are combined in a two-stage research method. In the first stage, an input-oriented DEA model is used to determine bootstrap efficiency scores. Then, the corrected distribution function of the efficiency scores is used to estimate a truncated regression which is aimed to identify the significant influential context variables. Three categorical and two continuous context variables are considered in the analysis. Results show that only one context variable has a significant impact on the water utilities efficiency scores. Managerial recommendations are drawn from the analysis. It is suggested that water utilities continue or implement wastewater treatment, persist in decreasing and controlling leakage across the distribution network, and maximizing sewer coverage.

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Decarbonization in Mexico by extending the charging stations network for electric vehicles

2023 , Francisco Ruiz-Barajas , Ramirez-Nafarrate, Adrian , Olivares-Benitez, Elias

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A Formulation for the Stochastic Multi-Mode Resource-Constrained Project Scheduling Problem Solved with a Multi-Start Iterated Local Search Metaheuristic

2023 , Alfredo S. Ramos , Pablo A. Miranda-Gonzalez , Nucamendi-Guillén, Samuel , Olivares-Benitez, Elias

This research introduces a stochastic version of the multi-mode resource-constrained project scheduling problem (MRCPSP) and its mathematical model. In addition, an efficient multi-start iterated local search (MS-ILS) algorithm, capable of solving the deterministic MRCPSP, is adapted to deal with the proposed stochastic version of the problem. For its deterministic version, the MRCPSP is an NP-hard optimization problem that has been widely studied. The problem deals with a trade-off between the amount of resources that each project activity requires and its duration. In the case of the proposed stochastic formulation, the execution times of the activities are uncertain. Benchmark instances of projects with 10, 20, 30, and 50 activities from well-known public libraries were adapted to create test instances. The adapted algorithm proved to be capable and efficient for solving the proposed stochastic problem.

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Multi-objective design of balanced sales territories with taboo search: A practical case

2021 , Olivares-Benitez, Elias , Bernábe-Loranca, María Beatríz , Caballero-Morales, Santiago-Omar , Granillo-Macias, Rafae

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Book of abstracts CLAIO/CSMIO 2024 conference

2024 , Olivares-Benitez, Elias , Rodriguez-Escoto, Joel-Novi , M. Angélica Salazar-Aguilar , Paulina González-Ayala

El Book of Abstracts CLAIO/CSMIO 2024 es un compendio que incluye los resúmenes de las ponencias y estudios presentados en el XXII Congreso Latino Iberoamericano de Investigación Operativa (CLAIO) y el XII Congreso de la Sociedad Mexicana de Investigación de Operaciones (CSMIO). Este evento, celebrado del 28 de octubre al 1 de noviembre de 2024 en Guadalajara, México, reúne a investigadores y profesionales de Latinoamérica que trabajan en diversos campos de la Investigación Operativa. El libro contiene una amplia variedad de trabajos enfocados en temas de optimización matemática, simulación, inteligencia artificial, logística, programación estocástica, aprendizaje automático, transporte, gestión de la cadena de suministro, entre otros. Se presentan metodologías innovadoras, modelos y aplicaciones prácticas en diferentes áreas como la salud, el transporte y la sostenibilidad.

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A heuristic method for the supplier selection and order quantity allocation problem

2021 , Alejo-Reyes, Avelina , Mendoza, Abraham , Olivares-Benitez, Elias

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Hybrid model to design a distribution network in contract farming

2019 , Rafael Granillo-Macias , Isidro Jesus Gonzalez Hernandez , Jose Luis Martinez-Flores , Santiago Omar Caballero-Morales , Olivares-Benitez, Elias

This paper suggests a hybrid model to solve a distribution problem incorporating the impact of uncertainty in the solution. The model combines the deterministic approach and the simulation including stochastic variables such as harvest yield, loss risk and penalties/benefits to design a distribution network with the minimal cost. Through a case study that includes farmers, hubs and malt producers in the supplying chain of barley in Mexico, nine possible scenarios were analyzed to plan and distribute the harvested grain based on contract farming. This approach gets an optimal solution through an iterative process simulating the suggested solution by a mixed-integer linear programming model under uncertain conditions. The results show the convenience of maintaining the operation between four and five hubs depending on the possible scenario; besides, the variation of the levels of the barley producers’ capacities are key elements in the planning to minimize the distribution cost throughout the suggested chain

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