Olivares-Benitez, Elias

Main Affiliation

Preferred name

Official Name

Olivares Benítez, Elias

ORCID

0000-0001-7943-3869

Researcher ID

R-7075-2018

Scopus Author ID

55574580400

Now showing 1 - 10 of 47

Some of the metrics are blocked by your
consent settings
Exploration of a Generalized Benders Decomposition Method for Solving Project Scheduling Problems with Resource Constraints
(SCITEPRESS - Science and Technology Publications, 2025)
;
Pablo Miranda-Gonzalez
;
This research introduces a new Generalized Benders Decomposition-based Algorithm (GBDA) to solve the Multi-Mode Resource-Constrained Project Scheduling Problem (MRCPSP). The MRCPSP is a scheduling problem that besides precedence constraints, includes renewable and non-renewable resource constraints, as well as the selection of execution modes for the project activities. This mode selection determines the resource usage and duration of each activity. The GBDA splits the problem into a Master Problem (MP) and a Sub- Problem (SP) with a relaxation. Both problems are solved alternately, each one incorporating information from the other at each iteration, until a stopping criterion is met. Additionally, at each iteration, a non-relaxed SP is solved to obtain a solution for the original problem, and the best solution from all iterations is reported. The GBDA was tested, with three different stopping criteria, on benchmark instances from a public library and compared against solving the traditional formulation of the problem with an exact Mixed Integer Linear Programming (MILP) method. The GBDA found solutions of good quality in less than half the computing time than the exact method, with one of the stopping criteria. The analysis of the results provides valuable insights for future research.
Some of the metrics are blocked by your
consent settings
Generalized Benders decomposition-based matheuristics for the multi-mode resource-constrained project scheduling problem
(Springer Science and Business Media LLC, 2025-03-13)
;
Pablo A. Miranda-Gonzalez
;
;
The multi-mode resource-constrained project scheduling problem is an NP-hard optimization problem with practical applications in construction, software development, manufacturing, and other industrial and business situations. It involves a set of activities that need to be sequenced while considering precedence and resource constraints as well as different alternative execution modes, which determine each activity’s duration and resource consumption. This research proposes three matheuristic strategies based on a reformulation and partial relaxation of the problem, including a generalized Benders decomposition (GBD)-based algorithm to solve the relaxed problem and three different procedures to find a solution to the original problem. The strategies were tested using benchmark instances of various sizes obtained from published libraries. These strategies showed a significant improvement in speed, achieving up to 92.77% faster performance than the exact method for finding high-quality sub-optimal solutions. This offers a valuable trade-off between computation time and solution quality. Additionally, the GBD-based algorithm generated tighter lower bounds than other existing methods in the literature for a substantial number of the tested instances, all within a very short computing time.
Some of the metrics are blocked by your
consent settings
An Airline Profit Management Model with Overbooking and No-Shows
(SCITEPRESS - Science and Technology Publications, 2025)
;
Ana Esparza
;
Orejel, Juan
;
Catya Zuniga
Some of the metrics are blocked by your
consent settings
Exploring fairness and centralisation in insular waste collection systems
(Elsevier BV, 2025-12)
Pablo A. Miranda-Gonzalez
;
Dylan Jones
;
;
Luis Olivares-Alvarez
Some of the metrics are blocked by your
consent settings
Multiobjective model to optimize charging station location for the decarbonization process in Mexico
(2025)
Ruiz Barajas, Francisco
;
;
Adrian Ramirez‐Nafarrate
;
Rosa G. González‐Ramírez
<jats:title>Abstract</jats:title><jats:p>Electric vehicles (EVs) offer significant potential for advancing sustainable environmental goals. However, their widespread adoption has been concentrated in urban areas, raising challenges for interurban travel. In many countries, charging station networks are primarily located within cities, highlighting a key opportunity for expansion to support longer distance journeys. This article addresses the facility location problem for EV charging stations to enable interurban travel. We propose a multiobjective optimization model based on the flow refueling location model with three objectives: maximizing CO<jats:sub>2</jats:sub> emissions reduction, minimizing total costs, and reducing user charging time. The model is solved using an epsilon constraint approach, and Mexico's charging station network is used as a case study. Through computational experiments, various scenarios are evaluated, and a comparative analysis is performed between electric and internal combustion vehicles. Results show that deploying 20 strategically located charging stations could mitigate 3.1 million tons of CO<jats:sub>2</jats:sub>, requiring an investment of nearly USD 3.9 million.</jats:p>
4
Some of the metrics are blocked by your
consent settings
Inventory replenishment decisions for the supplier selection problem considering transportation freight rates and quality
(2018)
;
;
4 2
Some of the metrics are blocked by your
consent settings
Dynamic Optimization of a Supply Chain Operation Model with Multiple Products
(2024)
Carlos E. Lopez-landeros
;
Ricardo Valenzuela-Gonzalez
;
<jats:p>Determination of the optimal operational policy for an automotive supply chain is explored under a centralized management approach using dynamic programming. A deterministic optimal control model is proposed to meet multi-product demand over a period while minimizing a cost performance index for a five-echelon network. The production-inventory levels are the state variables and the raw material acquisition rates are the control variables to be decided in the problem. The novelties include parts mixing operations, assembly requirements, and a push–pull chain operation strategy. The continuous model is solved using Iterative Dynamic Programming, an algorithm with successful applications in chemical engineering problems. Its implementation here is the first in supply chain (SC) management models. The results demonstrate that the proposal is suitable to represent the dynamic behavior of the SC and provides useful information to outline a cooperative decision-making process. Managerial insights are derived to improve the resilience and efficiency of the chain.</jats:p>
26
Some of the metrics are blocked by your
consent settings
An Inventory Service-Level Optimization Problem for a Multi-Warehouse Supply Chain Network with Stochastic Demands
(2024)
Roberto León
;
Pablo A. Miranda-Gonzalez
;
Francisco J. Tapia-Ubeda
;
<jats:p>This research aims to develop a mathematical model and a solution approach for jointly optimizing a global inventory service level and order sizes for a single-commodity supply chain network with multiple warehouses or distribution centers. The latter face stochastic demands, such as most real-world supply chains do nowadays, yielding significant model complexity. The studied problem is of high relevance for inventory management, inventory location, and supply chain network design-related literature, as well as for logistics and supply chain managers. The proposed optimization model minimizes the total costs associated with cycle inventory, safety stock, and stock-out-related events, considering a global inventory service level and differentiated order sizes for a fixed and known set of warehouses. Subsequently, the model is solved by employing the Newton–Raphson algorithm, which is developed and implemented assuming stochastic demands with a normal approximation. The algorithm reached optimality conditions and the convergence criterion in a few iterations, within less than a second, for a variety of real-world sized instances involving up to 200 warehouses. The model solutions are contrasted with those obtained with a previous widely employed approximation, where safety stock costs were further approximated and order sizes were optimized without considering stock-out-related costs. This comparison denotes valuable benefits without significant additional computational efforts. Thus, the proposed approach is suitable for managers of real-world supply chains, since they would be able to attain system performance improvements by simultaneously optimizing the global inventory service level and order sizes, thereby providing a better system cost equilibrium.</jats:p>
12
Some of the metrics are blocked by your
consent settings
Book of abstracts CLAIO/CSMIO 2024 conference
(Universidad Panamericana, 2024)
;
;
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.
1269 537
Some of the metrics are blocked by your
consent settings
The multi-depot open location routing problem with a heterogeneous fixed fleet
(2021)
;
Alejandra Gómez Padilla
;
;
J. Marcos Moreno-Vega
Scopus© Citations 27 9 1

Olivares-Benitez, Elias

Filters

Settings

Sort By

Results per page