Mathematical Model Developed Using Meta-Initiative Optimization Algorithm for Production and Labor Planning

In todays competitive environment, production efficiency is a very important and key issue in success in the market. However, all decisions of the production unit are interdependent and it is necessary to use an integrated form which leads to finding a better approach for the management. Accordingly, in this research, the integration of three important fields in manufacturing companies has been addressed. These fields include production planning, maintenance, and labor scheduling. In this regard, a novel mathematical model with the aim of optimal use of labor and increasing production volume is presented. In this model of workers’ experience, machine utilization rate and machine failure rate are expressed using fuzzy numbers. To optimize this model, the ant colony optimization algorithm has been used. Numerical results obtained from the implementation of the mathematical model and solution method show that the used algorithm can provide solutions with the least possible error in a reasonable time. Moreover, the sensitivity analysis shows that the failure rate of the machine before and after maintenance has a great impact on the objective function of the mathematical model

Augmented Probability Simulation Methods for Non-cooperative Games

We present a robust decision support framework with computational algorithms for decision makers in non-cooperative sequential setups. Existing simulation based approaches can be inefficient when there is a large number of feasible decisions and uncertain outcomes. Hence, we provide a novel alternative to solve non-cooperative sequential games based on augmented probability simulation. We propose approaches to approximate subgame perfect equilibria under complete information, assess the robustness of such solutions and, finally, approximate adversarial risk analysis solutions when lacking complete information. This framework could be especially beneficial in application domains such as cybersecurity and counter-terrorism

Planeamiento y control de producción en la empresa Serprovisa S.A.C Huachipa

La indagación fue basada en el estudio de planeamiento y control de producción de la empresa Serprovisa S.A.C Huachipa, de tal forma que se ha estudiado y analizado las dos variables de estudio con sus respectivas dimensiones, teniendo como objetivo general determinar la relación entre planeamiento y control de producción en la empresa Serprovisa S.A.C. El diseño de estudio es descriptivo correlacional, el cual se sustenta bajo los fundamentos teóricos de Navajo, Tadeo, Torres sobre planeamiento y fundamentos teóricos de Anaya, Gonzales, Serpell & Alarcon, sobre el control de producción. La población del presente estudio estuvo conformada por 97 colaboradores, derivando recolección de datos se utilizó el cuestionario, así mismo la validez de los instrumentos se obtuvieron mediante el juicio de expertos de la Universidad Cesar Vallejo, obteniendo un nivel de confiabilidad de coeficiencia Alfa de Cronbach 0.787 para el cuestionario de planeamiento y 0.725 para el cuestionario de control de producción. La encuesta cuenta con 30 preguntas cuestionadas que procesaron un determinado momento. Finalmente se realizó la prueba de hipótesis, dando como resultado que existe correlación moderada entre ambas variables, con un nivel de significancia 0,000 (bilateral) con un grado de correlación Rho de Spearman de 0.648

Cálculo de costos ocultos por obsolescencia de maquinaria en un sistema de manufactura.

Dentro de los procesos de un sistema de manufactura, la maquinaria es una parte importante para convertir la materia prima en producto terminado, sin embargo, el uso constante de estos equipos puede con llevar a un envejecimiento, lo cual genera que las maquinarias fallen por diversas causas y generan paros de tal forma que deben ser sometidas a ciertas actividades de mantenimiento, incrementándose los costos usuales de operación, los costos de mantenimiento y reparación. Adicionalmente, junto con estos costos surgen los costos ocultos, relacionados con la calidad del producto terminado y la productividad de la máquina como consecuencia de las fallas y los paros constantes. No obstante, estos costos ocultos no se evidencian claramente en los estados financieros de una empresa, lo cual impide la toma de decisiones sobre reemplazos e inversiones futuras en maquinaria y equipos del sistema de manufactura. Este trabajo pretende mostrarle al lector un método para calcular los costos ocultos, utilizando la herramienta de simulación de redes de Petri, que se generan como consecuencia de mantener en funcionamiento una maquina obsoleta dentro de un sistema de manufactura considerando la no calidad y la no productividad del sistema.PregradoINGENIERO(A) EN INDUSTRIA

Cálculo de costos ocultos por obsolescencia de maquinaria en un sistema de manufactura.

Dentro de los procesos de un sistema de manufactura, la maquinaria es una parte importante para convertir la materia prima en producto terminado, sin embargo, el uso constante de estos equipos puede con llevar a un envejecimiento, lo cual genera que las maquinarias fallen por diversas causas y generan paros de tal forma que deben ser sometidas a ciertas actividades de mantenimiento, incrementándose los costos usuales de operación, los costos de mantenimiento y reparación. Adicionalmente, junto con estos costos surgen los costos ocultos, relacionados con la calidad del producto terminado y la productividad de la máquina como consecuencia de las fallas y los paros constantes. No obstante, estos costos ocultos no se evidencian claramente en los estados financieros de una empresa, lo cual impide la toma de decisiones sobre reemplazos e inversiones futuras en maquinaria y equipos del sistema de manufactura. Este trabajo pretende mostrarle al lector un método para calcular los costos ocultos, utilizando la herramienta de simulación de redes de Petri, que se generan como consecuencia de mantener en funcionamiento una maquina obsoleta dentro de un sistema de manufactura considerando la no calidad y la no productividad del sistema.PregradoINGENIERO(A) EN INDUSTRIA

Models, Theoretical Properties, and Solution Approaches for Stochastic Programming with Endogenous Uncertainty

In a typical optimization problem, uncertainty does not depend on the decisions being made in the optimization routine. But, in many application areas, decisions affect underlying uncertainty (endogenous uncertainty), either altering the probability distributions or the timing at which the uncertainty is resolved. Stochastic programming is a widely used method in optimization under uncertainty. Though plenty of research exists on stochastic programming where decisions affect the timing at which uncertainty is resolved, much less work has been done on stochastic programming where decisions alter probability distributions of uncertain parameters. Therefore, we propose methodologies for the latter category of optimization under endogenous uncertainty and demonstrate their benefits in some application areas. First, we develop a data-driven stochastic program (integrates a supervised machine learning algorithm to estimate probability distributions of uncertain parameters) for a wildfire risk reduction problem, where resource allocation decisions probabilistically affect uncertain human behavior. The nonconvex model is linearized using a reformulation approach. To solve a realistic-sized problem, we introduce a simulation program to efficiently compute the recourse objective value for a large number of scenarios. We present managerial insights derived from the results obtained based on Santa Fe National Forest data. Second, we develop a data-driven stochastic program with both endogenous and exogenous uncertainties with an application to combined infrastructure protection and network design problem. In the proposed model, some first-stage decision variables affect probability distributions, whereas others do not. We propose an exact reformulation for linearizing the nonconvex model and provide a theoretical justification of it. We designed an accelerated L-shaped decomposition algorithm to solve the linearized model. Results obtained using transportation networks created based on the southeastern U.S. provide several key insights for practitioners in using this proposed methodology. Finally, we study submodular optimization under endogenous uncertainty with an application to complex system reliability. Specifically, we prove that our stochastic program\u27s reliability maximization objective function is submodular under some probability distributions commonly used in reliability literature. Utilizing the submodularity, we implement a continuous approximation algorithm capable of solving large-scale problems. We conduct a case study demonstrating the computational efficiency of the algorithm and providing insights

Operational and maintenance planning of production and utility systems in process industries.

Major process industries have installed onsite the utility systems that can generate several types of utilities for meeting the utility requirements of the main production systems. A traditional sequential approach is typically used for the planning of production and utility systems. However, this approach provides suboptimal solutions because the interconnected production and utility systems are not optimised simultaneously. In this research, a general optimisation framework for the simultaneous operational and maintenance planning of utility and production systems is presented with the main purpose of reducing the energy needs and resources utilisation of the overall system. A number of industrial-inspired case studies solved show that the solutions of the proposed integrated approach provides better solutions than the solutions obtained by the sequential approach. The results reported a reduction in total costs from 5% to 32%. The reduction in total costs demonstrate that the proposed integrated approach can result in efficient operation of utility systems by avoiding unnecessary purchases of utility resources and improved utilisation of energy and material resources. In addition, the proposed integrated optimisation-based model was further improved with the presence of process uncertainty in order to address dynamic production environment in process industries. However, integrated planning problems of production and utility systems results to large mixed integer programming (MIP) model that is difficult to solve to optimality and computationally expensive. With this regards, three-stage MIP-based decomposition strategy is proposed. The computational experiments showed that the solutions of the proposed MIP-based decomposition strategy can achieve optimal or near-optimal solutions at further reduced computational time by an average magnitude of 4. Overall, the proposed optimisation framework could be used to integrate production and utility systems for effective planning management in the realistic industrial scenarios.PhD in Energy and Powe