Nucamendi-Guillén, Samuel
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Preferred name
Nucamendi-Guillén, Samuel
Official Name
Nucamendi Guillén, Samuel Moisés
Alternative Name
snucamendi
Main Affiliation
ORCID
Scopus Author ID
57191972691
Researcher ID
M-8732-2018

Research Output
Now showing 1 - 10 of 29
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A Bi-Level Vaccination Points Location Problem That Aims at Social Distancing and Equity for the Inhabitants

2023 , Edith Salinas , José-Fernando Camacho-Vallejo , Nucamendi-Guillén, Samuel

Designing efficient vaccination programs that consider the needs of the population is very relevant to prevent reoccurrence of the COVID-19 pandemic. The government needs to provide vaccination points to give out vaccine doses to the population. In this paper, the authors analyze the location of vaccination points whilst addressing the inhabitants’ preferences. Two objectives that prevent crowding of inhabitants are considered. The government aims for the minimum distance between located vaccination points is maximized, and for the number of inhabitants that attend the different vaccination points to be equitable. One of the key aspects of this problem is the assumption that inhabitants freely choose the located vaccination point to go. That decision affects the objectives of the government, since crowding at vaccination points may appear due to the inhabitants’ decisions. This problem is modeled as a bi-objective, bi-level program, in which the upper level is associated to the government and the lower level to the inhabitants. To approximate the Pareto front of this problem, a cross-entropy metaheuristic is proposed. The algorithm incorporates criteria to handle two objective functions in a simultaneous manner, and optimally solve the lower-level problem for each government decision. The proposed algorithm is tested over an adapted set of benchmark instances and pertinent analysis of the results is included. An important managerial insight is that locating far vaccination points does not lead us to a more equitable allocation of inhabitants.

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New formulations and solution approaches for the latency location routing problem

2022 , Nucamendi-Guillén, Samuel , Iris Martínez-Salazar , Sara Khodaparasti , Maria Elena Bruni

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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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Linking Lean Adoption and Implementation in Healthcare to National Cultures

2021 , Rodrigo E. Peimbert-García , Tapani Jorma , Leopoldo Eduardo Cárdenas-Barrón , Nucamendi-Guillén, Samuel , Heriberto García-Reyes

Lean Healthcare (LHC) is a widely accepted approach to improve the quality of care around the world. This research compares two studies, which evaluated the adoption/implementation of LHC in Finland and Mexico, to understand how cultural similarities/differences influence LHC implementations. Data were gathered from previous questionnaire-based studies administered to healthcare professionals in both countries. Statistics (X2, p, and Wilcoxon tests) are used to compare both studies across topics related to adoption, introduction, integration, success, and barriers of LHC projects, and results are linked to cultural dimensions. Driven by economic savings, LHC has been more adopted in Finland than in Mexico (75/13%). Upon introduction, similarities are found in the way LHC projects are conducted, high project success rate, poor level of integration, and enabling/disabling factors. Conversely, differences were mainly found in the objectives of implementing LHC. These similarities/differences are linked to national factors involving culture, social structure, uncertainty management, time orientation, and indulgence level. In particular, uncertainty avoidance, equal rights’ structure, and a feminine culture are positive for implementing Lean. These findings can be a benchmark to evaluate cultural practices. Thus, this study provides insight into how national cultures relate to LHC and determined distinctive sociotechnical aspects that influence its adoption/implementation.

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The bi-objective minimum latency problem with profit collection and uncertain travel times

2020 , Nucamendi-Guillén, Samuel , Maria Elena Bruni , Sara Khodaparasti

This paper introduces a new bi-objective minimum latency problem with profit collection, where routes must be constructed in order to maximize the collected profit and to minimize the total latency. These objectives are usually conflicting. Thus, considering some important features, as the segmentation of the customers into two classes, mandatory and optional, and the presence of uncertain travel times, we follow a bi-objective approach, aiming to compute a set of Pareto-optimal alternatives with different trade-offs for a decision-maker to choose from. In order to address this computationally challenging problem, we propose a Multi-Objective Iterated Local Search. Computational results confirm the practicality of the algorithm, in terms of the quality of the solutions, and its computational efficiency in terms of time spent. We conclude that the algorithm finds good-quality solutions for small and medium-size instances.

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A mixed integer formulation and an efficient metaheuristic for the unrelated parallel machine scheduling problem: Total tardiness minimization

2022 , Héctor G.-de-Alba , Nucamendi-Guillén, Samuel , Oliver Avalos-Rosales

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A capacitated lot-sizing problem in the industrial fashion sector under uncertainty: a conditional value-at-risk framework

2022 , Yajaira Cardona-Valdés , Nucamendi-Guillén, Samuel , Luis Ricardez-Sandoval

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A methodology for increasing revenue in fashion retail industry

2018 , Nucamendi-Guillén, Samuel , Moreno, Miguel A. , Mendoza, Abraham

Purpose Fashion retail companies typically exhibit short life-cycles, high volatility and low predictability. Therefore, their success is largely determined by the organisation’s flexibility and responsiveness. The purpose of this paper is to present a methodology to facilitate inventory control to minimise both shortages and excess inventory for a multi-product, multi-period finite time horizon inventory problem by using statistical and stochastic analysis. Design/methodology/approach The proposed methodology operates in two phases: the first phase consists on determining an aggregate plan (AP) that will be used for monitoring the behaviour of the items during the time horizon. This plan is obtained by statistically analysing historical data related to sales and inventory shortages and is used to determine a demand forecast during the time horizon that allows to handle with potential disruptions derived from real demand variations. Finally, supply replenishment policies are defined to facilitate the monitoring process during the second phase. For the second phase, the behaviour of real demand for every item is captured into a database and compared against its projected demand (from the AP). If needed, adjustments are made in the procurement of future deliveries to reduce the probability of having shortages and/or excess inventory. Findings A case study in a Mexican fashion retail company was conducted to assess the performance of the methodology. Results indicate that shortage in early periods can be reduced totally for certain products while, for others, the reduction is about 90.5 per cent. In addition, the incomes of the company were increased over 57 per cent. Research limitations/implications Even when the success of the methodology has been shown, cultural and behavioural factors were not considered. An extensive study is suggested to determine if these factors should be included to enhance the performance of the methodology. Practical implications A case study of a Mexican fashion retail company was conducted to assess the performance of the proposed methodology. The methodology is easy to implement and effectively and quickly responds to disruptions in the demand and it also significantly reduces the level of shortages while increasing sales and revenue for the company. Originality/value This paper proposes a methodology that is able to anticipate product’s behaviour from early weeks. Additionally, replenishment policies allow to quickly adjust future orders to guarantee the availability of items and minimise overstock.

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A clustering algorithm for ipsative variables

2019 , Jesica Rubiano Moreno , Carlos Alonso Malaver , Nucamendi-Guillén, Samuel , López-Hernández, Carlos

The aim of this study is to introduce a new clustering method for ipsatives variables. This  method can be used for nominals or ordinals variables for which responses must be mutually exclusive, and it is independent of data distribution. The proposed method is applied to outline motivational profiles for individuals based on a declared preferences set.  A case study is used to analyze the performance of the proposed algorithm by comparing proposed method results versus the PAM method. Results show that proposed method generate a better segmentation and differentiated groups. An extensive study was conducted to validate the performance clustering method against a set of random groups by clustering measures.

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The multi-depot k-traveling repairman problem

2022 , Maria Elena Bruni , Sara Khodaparasti , Iris Martínez-Salazar , Nucamendi-Guillén, Samuel

AbstractIn this paper, we study the multi-depot k-traveling repairman problem. This problem extends the traditional traveling repairman problem to the multi-depot case. Its objective, similar to the single depot variant, is the minimization of the sum of the arrival times to customers. We propose two distinct formulations to model the problem, obtained on layered graphs. In order to find feasible solutions for the largest instances, we propose a hybrid genetic algorithm where initial solutions are built using a splitting heuristic and a local search is embedded into the genetic algorithm. The efficiency of the mathematical formulations and of the solution approach are investigated through computational experiments. The proposed models are scalable enough to solve instances up to 240 customers.

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