Localización de instalaciones y ruteo de personal especializado en logística humanitaria post-desastre - caso inundaciones

98 Páginas.La cadena de suministro en la logística humanitaria puede describirse como una red de personal voluntariado y especializado que interactúa con un conjunto de bienes y servicios, esto con el propósito de satisfacer la demanda de la población afectada por una inundación repentina. Este trabajo se enfoca en determinar propuestas de solución para el problema de localización de un punto de distribución y múltiples albergues considerando el riesgo de inundación asociado a la zona, además del problema de ruteo del personal especializado que permita aliviar las calamidades médicas y psicológicas entre otras presentes en la población afectada en una situación post-desastre. Dichos propuestas consideran la aplicación de investigación de operaciones como herramienta de solución. El objetivo de este artículo es proporcionar un modelo funcional para diseñar de manera estratégica la red instalaciones, además coordinar el suministro de servicios requeridos por la población en el menor tiempo posible. Se toma como caso de estudio la inundación que sufrió el municipio de Santa Lucía en el departamento del Atlántico, Colombia en 2010.

A Framework for Applying Reinforcement Learning to Deadlock Handling in Intralogistics

Intralogistics systems, while complex, are crucial for a range of industries. One of their challenges is deadlock situations that can disrupt operations and decrease efficiency. This paper presents a four-stage framework for applying reinforcement learning algorithms to manage deadlocks in such systems. The stages include Problem Formulation, Model Selection, Algorithm Selection, and System Deployment. We carefully identify the problem, select an appropriate model to represent the system, choose a suitable reinforcement learning algorithm, and finally deploy the solution. Our approach provides a structured method to tackle deadlocks, improving system resilience and responsiveness. This comprehensive guide can serve researchers and practitioners alike, offering a new avenue for enhancing intralogistics performance. Future research can explore the framework’s effectiveness and applicability across different systems

A Framework for Applying Reinforcement Learning to Deadlock Handling in Intralogistics

Intralogistics systems, while complex, are crucial for a range of industries. One of their challenges is deadlock situations that can disrupt operations and decrease efficiency. This paper presents a four-stage framework for applying reinforcement learning algorithms to manage deadlocks in such systems. The stages include Problem Formulation, Model Selection, Algorithm Selection, and System Deployment. We carefully identify the problem, select an appropriate model to represent the system, choose a suitable reinforcement learning algorithm, and finally deploy the solution. Our approach provides a structured method to tackle deadlocks, improving system resilience and responsiveness. This comprehensive guide can serve researchers and practitioners alike, offering a new avenue for enhancing intralogistics performance. Future research can explore the framework’s effectiveness and applicability across different systems

A Framework for Applying Reinforcement Learning to Deadlock Handling in Intralogistics

Intralogistics systems, while complex, are crucial for a range of industries. One of their challenges is deadlock situations that can disrupt operations and decrease efficiency. This paper presents a four-stage framework for applying reinforcement learning algorithms to manage deadlocks in such systems. The stages include Problem Formulation, Model Selection, Algorithm Selection, and System Deployment. We carefully identify the problem, select an appropriate model to represent the system, choose a suitable reinforcement learning algorithm, and finally deploy the solution. Our approach provides a structured method to tackle deadlocks, improving system resilience and responsiveness. This comprehensive guide can serve researchers and practitioners alike, offering a new avenue for enhancing intralogistics performance. Future research can explore the framework’s effectiveness and applicability across different systems

What are Management Systems? The Effect of Management Style According to System Dynamics

9 páginasOrganizational management models involve various perspectives, such as management style, cultural dimensions, business models, organizational models, behavioural models and organizational learning. Each of these perspectives has an effect and thus a bearing on management. Accordingly, it becomes difficult to determine which of these perspectives is the most significant for a management model. For example, management style can be held to be a variable with a major impact on the organization because it affects the definition of organizational objectives while coordinating and controlling resources. Therefore, this study presents a proposal for the characterization of management models and presents in the form of a causal chart what could be considered a matrix framework for an organizational management model. To construct this matrix, possible relationships were established between each of the previously proposed systems considered to constitute organizations