Browsing by Author "López-Ospina, Héctor"

Now showing 1 - 9 of 9
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    (33, 33) Meza, Armando; Latorre, Paolo; Bonacic, Milena; López-Ospina, Héctor; Pérez, Juan; 33
    This paper presents a profit optimization model for substitute products in a competitive, time-sensitive market with scarcity and shifting user preferences. The model maximizes profit, considering production costs and inventory maintenance. It uses a discrete choice model to represent demand, sensitivity to price, availability, and changing preferences. A two-phase PSO-type metaheuristic solution tackles the nonlinear, recursive model, efficiently managing inventories and evolving consumer preferences. The model integrates production decisions, inventories, and sales prices, considering scarcity conditions and user preferences. It uses a multinomial logit for the consumers’ demand function with soft exogenous constraints, which influence utility and change consumption preferences and choices. This research offers a tool for companies to manage stock, production, and pricing in a context where goods are substitutes, providing a new perspective on business strategy.
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    (33, 33) Moreno-Palacio, Diana P.; Gonzalez-Calderon, Carlos A.; López-Ospina, Héctor; Gil-Marin, Jhan Kevin; Posada-Henao, John Jairo; 33
    This paper presents an improved entropy-based freight tour synthesis (FTS) using fuzzy logic (FL). One approach used in formulating FTS models is entropy maximization, which aims to obtain the most probable freight (trucks) tour flow distribution in a network based on traffic counts. These models consider fixed parameters and constraints. However, the variations in costs, traffic counts, and truck demands depending on human behavior, are not always captured in detail in such models. FL can include such variabilities in its modeling. The flexibility FL provides to the model allows to obtain solutions where some or all the constraints do not entirely satisfy—but are close to—their expected values. Moreover, the modeling approach used based on FL theory is the membership function, specifically the triangular membership function, which is defined by three points corresponding to the vertices. This optimization problem was transformed into a bi-objective problem when the optimization variables are the membership and the entropy. The performance of the proposed formulation was assessed in the Sioux Falls network. To solve the problem, the model was run in General Algebraic Modeling System (GAMS), applying the ? approach, where ? value (?? [0, 1] with steps of 0.01) represents the level of accomplishment that at least one of the constraints (but can be more) gets. The results show that the entropy value decreased as the accomplishment level increased, and this behavior indicates a Pareto frontier, which proves that the optimization problem is bi-objective.
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    (33, 33) Bonacic, Milena; López-Ospina, Héctor; Bravo, Cristián; Pérez, Juan; 33
    Portfolio management typically aims to achieve better returns per unit of risk by building efficient portfolios. The Markowitz framework is the classic approach used when decision-makers know the expected returns and covariance matrix of assets. However, the theory does not always apply when the time horizon of investments is short; the realized return and covariance of different assets are usually far from the expected values, and considering additional factors, such as diversification and information ambiguity, can lead to better portfolios. This study proposes models for constructing efficient portfolios using fuzzy parameters like entropy, return, variance, and entropy membership functions in multi-criteria optimization models. Our approach leverages aspects related to multi-criteria optimization and Shannon entropy to deal with diversification, and fuzzy and fuzzy entropy variants provide a better representation of the ambiguity of the information according to the investors’ deadline. We compare 418 optimal portfolios for different objectives (return, variance, and entropy), using data from 2003 to 2023 of indexes from the USA, EU, China, and Japan. We use the Sharpe index as a decision variable, in addition to the multi-criteria decision analysis method TOPSIS. Our models provided high-efficiency portfolios, particularly those considering fuzzy entropy membership functions for return and variance.
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    (33, 33) Quezada, Luis E.; López-Ospina, Héctor; González, Miguel Ángel; Oddershede, Astrid; Palominos, Pedro; 33
    This paper presents a method for identifying causal relationships between strategic objectives within a strategy map of a Balanced Scorecard. Strategy maps are modeled as a network of strategic objectives (nodes) and causal relationships (directed arcs). The nodes are also grouped into clusters that represent the perspectives of a Balanced Scorecard: (a) Finances, Clients, Internal Processes and Growth and Learning. The method uses the Analytic Network Process (ANP) to establish the importance of every relationship and uses a multi-objective integer linear programming model to select the relationships to be included within a strategy map of a company. The method provides a method that optimizes the selection of the relationships to be included in a strategy map. An illustration of the application of the method in a manufacturing company is presented.
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    (33, 33) Latorre, Paolo; López-Ospina, Héctor; Maldonado, Sebastián; Guevara, C. Angelo; Pérez, Juan; 33
    Employee turnover significantly impacts organizations, particularly those with substantial investments in training their workforce. To mitigate these effects, we propose a Prescriptive Human Resources Analytics approach that optimizes employee benefits to minimize total costs, focusing on turnover management The methodology models employee decision-making using a discrete choice model, with parameters estimated through maximum likelihood. We solve the resulting nonlinear optimization problem with a heuristic tailored to the problem's complexity. We applied this methodology to a hospital case study, which was used to enhance the transportation system as an employee benefit, considering the associated turnover costs. The results demonstrate that our approach can reduce total costs, optimize the usage level of the designed benefits, and increase employee satisfaction. This research provides a robust framework for data-driven decision-making in HR, offering practical tools for improving employee retention strategies.
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    (33, 33) González-Solano, Fernando; Galindo, Gina; González-Ramírez, Rosa G.; López-Ospina, Héctor; Romero-Rodriguez, Daniel; 33
    Disruptive events increasingly challenge global port operations and infrastructure, emphasizing the need for robust resilience plans to maintain operational continuity during crises. Designing effective resilience strategies in port operations involves prioritizing key drivers and strategies by establishing a hierarchical structure and analyzing their systemic interactions. This paper proposes a methodology to prioritize resilience drivers with its strategies by considering multiple key port stakeholders. This approach aims to create a roadmap for implementing these drivers, providing ports with a comprehensive tool for planning and executing resilience strategies. The methodology integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL) with Interpretative Structural Modeling (ISM). To illustrate its development, a case study focusing on two primary stakeholders, terminals and port authorities, in the Chilean port context is presented. The findings reveal that the main resilience strategies in Chilean ports include remote access to information by port authorities, alternative transport modes for port access and exit, rescheduling dispatches and cargo reception by port terminals, communication and call chains of the port authority, and remote access to information by port terminals. These results offer practical implications for port operations, providing clear guidance for implementing resilience strategies.
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    (33, 33) López-Ospina, Héctor; Cortés, Cristián E.; Pérez, Juan Eduardo; Peña, Romario; Figueroa-García, Juan Carlos; Urrutia-Mosquera, Jorge; 33
    We formulate a bi-objective distribution model for urban trips constrained by origins and destinations while maximizing entropy. We develop a flexible and consistent approach in which the estimations of generated/attracted parameters are fuzzy with entropic membership functions. Based on a fuzzy-entropy approach, we measure the uncertainty associated with fuzzy variables. We solve the problem by means of compromise programming considering a weighted sum objective function. We compute and extend concepts such as accessibility, attractiveness, and generalized cost, typically obtained in transport economic analyzes. Considering that our formulation is convex, we solve the problem in one step only, maintaining the uniqueness of the the optimization problem solution. We present two numerical examples to illustrate the proposed methodology, analyzing the impact of the results based on strong mathematical and statistical arguments. Finally, we show that our approach has better prediction capabilities than traditional fuzzy models regarding aggregated indicators and structural distribution patterns.
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    Design of a location and transportation optimization model including quality of service using constrained multinomial logit
    López-Ospina, Héctor; Agudelo-Bernal, Ángela; Reyes-Muñoz, Lina; Zambrano-Rey, Gabriel; Pérez, Juan