Conference Agenda

This paper proposes a methodology that integrates a genetic algorithm and a piecewise linear programming model to optimize facility layout design. This class of problems represents a major challenge in engineering due to its combinatorial and nonlinear nature. The problem considers as input data the areas of the facilities, the material flow between them, and the dimensions of the plant. The main objective is to minimize the total weighted distance between the centroids of the facilities. The genetic algorithm determines the relative arrangement of the facilities, while the piecewise linear programming model accurately adjusts the final dimensions and coordinates of each facility. The results obtained from instances of different sizes demonstrate the effectiveness of the proposed methodology in finding feasible solutions within reasonable computational times.

Currently, modernizing logistical processes in the Armed Forces is essential to achieve efficient, sustainable, and adaptable operations in complex scenarios. In the Peruvian Army, helicopter supply route planning is hindered by manual methods, lack of intelligent tools, and limited resources, reducing response capacity and operational effectiveness. The core problem is the need to optimize route planning in environments with diverse supplies and dynamic conditions, where traditional methods fail to ensure efficient use of time, distance, and fuel. The absence of an integrated system further limits strategic decision-making and resource utilization.

To address this challenge, AeroEP was developed—a web-based machine learning tool for optimizing helicopter routes in the Peruvian Army. Developed using the SCRUM agile methodology, AeroEP follows three phases: planning, design and development, and testing. The system incorporates a 2-opt route optimization algorithm that considers distance, flight time, and fuel consumption, enabling efficient simulation and reorganization of routes. Its modular design and interactive web interface allow real-time data integration and availability for operational decision-making.

Simulation results show that AeroEP significantly enhances operational efficiency: total distance traveled decreased by 13.98%, flight time by 13.55%, and fuel consumption by 13.97%, achieving an average efficiency improvement of 13.84%. The tool optimized routes in only 0.1658 seconds, demonstrating its suitability for dynamic and resource-constrained environments.

These findings confirm AeroEP’s effectiveness as a decision-support system for logistical planning, supporting institutional modernization, optimal resource utilization, and improved operational resilience in the Peruvian Army.

This research focuses on improving food supply logistics through mathematical programming applied to community kitchens in Villa El Salvador, applying Binary linear programming and vehicle routing techniques (CVRP). The main objective is to improve distribution efficiency, reducing distances, delivery times and maximizing the use of available resources using data provided by the Municipality of Villa El Salvador, two mathematical models were developed. The first one, implemented in PYTHON, allows to optimally assign the 150 canteens to one of the eight distribution centers (DC), considering the distances and the service capacity of each DC. The second model, implemented in Python with ORS and Pulp CBC, generates efficient daily delivery routes, considering that each truck has a capacity of 2 tons and a maximum working day of 8 hours. Since each canteen requires a quantity that is defined by the number of beneficiaries, the model simulates multiple trips per truck, each one returning to the distribution center for reloading.Since it is a single monthly delivery per canteen, the results obtained allow planning in advance the necessary delivery days, guaranteeing the fulfillment of demand without exceeding logistical capacities. The model helps to improve operational organization and reduce costs in food distribution in the district.

Urban public transport in Latin American cities has high operating costs and deficiencies in fleet management due to the absence of tactical planning based on real data. This study proposes a bus timetable allocation strategy through the integration of aggregate planning, optimization and discrete event simulation, applied to a real route in Metropolitan Lima. The methodology is structured in four stages: gathering operational information through focus groups, formulating the aggregate planning model, comparative evaluation of tactical strategies, and validation through simulation in ProModel. The results show that the pursuit strategy significantly reduces daily operating costs, moving from an oversized scheme to optimal fleet allocation without affecting service quality. The simulation confirms the operational stability of the system, showing controlled passenger flows and efficient use of resources. The proposal is replicable for urban transport companies with technological limitations and contributes to improving tactical decision-making in contexts of high demand variability.

This research aims to propose improvements to evacuation processes for vulnerable populations during accident scenarios within Lima’s Bus Rapid Transit (BRT) system, drawing on cases reported in the risk management literature. This population is examined due to its heightened vulnerability in emergency situations, primarily resulting from mobility constraints associated with visual impairments, physical disabilities, or wheelchair use. The study finds that transportation systems that incorporate inclusive protocols and accessibility criteria achieve higher levels of safety for vulnerable users. These findings are relevant for public transportation authorities, as they provide valuable insights to enhance evacuation planning and contribute to the development of safer, more accessible, and more inclusive public transportation systems.

The pharmaceutical cold chain is a critical component for preserving the quality, safety, and efficacy of thermosensitive medicines throughout importation and distribution processes. In Honduras, strong dependence on the international market, together with limitations in specialized infrastructure, thermal monitoring, and standardized logistical procedures, has created significant vulnerabilities that increase both health and economic risks. This study aimed to analyze the operational practices, risks, and vulnerabilities associated with the multimodal importation, air, maritime, and land, of pharmaceutical products under cold chain conditions in Honduras, in order to identify gaps in relation to international standards such as Good Distribution Practices and applicable ISO standards. A qualitative, exploratory, and descriptive research approach was adopted. Data were collected through semi-structured interviews with key stakeholders involved in pharmaceutical logistics, including personnel responsible for importation, logistics, storage, and quality assurance. The study population consisted of 36 importing companies identified from official records, from which a non-probabilistic convenience sample of 16 importers was selected. Qualitative content analysis was used to examine the data. The findings indicate that logistical decisions are largely based on empirical criteria, such as cost and prior experience, with limited use of advanced thermal control systems and low levels of procedural standardization. Customs delays, international transshipment points, and transfer facilities were identified as critical stages, where more than 70% of importers reported recent temperature deviations, resulting in significant economic losses. The study concludes that strengthening thermal controls, traceability, and alignment with international standards is essential to mitigate risks in pharmaceutical importation in Honduras.