A Multi Objective Evolutionary Algorithm based on Decomposition for a Flow Shop Scheduling Problem in the Context of Industry 4.0

Under the novel paradigm of Industry 4.0, missing operations have arisen as a result of the increasingly customization of the industrial products in which customers have an extended control over the characteristics of the final products. As a result, this has completely modified the scheduling and planning management of jobs in modern factories. As a contribution in this area, this article presents a multi objective evolutionary approach based on decomposition for efficiently addressing the multi objective flow shop problem with missing operations, a relevant problem in modern industry. Tests performed over a representative set of instances show the competitiveness of the proposed approach when compared with other baseline metaheuristics.Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Nesmachnow, Sergio. Universidad de la República; UruguayFil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentin

The argentinian forest sector: Opportunities and challenges in supply chain management

The rise in the worldwide demand of forest products of the last decades predicts an expansion of the forest harvesting industry. In this context, the Argentinian Northeastern Region (NEA) is considered a promising land since the local forest harvesting industry has one of the largest growing rates in the world. Despite its potential, this region faces some challenging obstacles: budget shortage, trade barriers and poor logistic infrastructure. For instance, traditionally the forest products are delivered by truck, which is from three to five times more expensive than other means of transport, like maritime or river transport. This is why in this paper, after a revision of the most recent advances in the worldwide supply chain management practices in the forest industry, recommendations for Argentina in order to overcome its main drawbacks in the forest sector are presented.Fil: Broz, Diego Ricardo. Universidad Nacional de Misiones. Facultad de Ciencias Forestales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Rossit, Diego Gabriel. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Cavallin, Antonella. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentin

Desarrollo de modelos y algoritmos para optimizar redes logísticas de residuos sólidos urbanos

La creciente urbanización e industrialización de la sociedad, sumado a una disponibilidad finita de recursos naturales, vuelve primordial hallar soluciones sustentables y eficientes que permitan la valorización de los residuos y su reincorporación nuevamente en forma de recursos al sistema productivo o, en su defecto, su correcta disposición final. Estas soluciones no sólo deben mitigar los importantes impactos ambientales asociados a un manejo inadecuado de residuos sino también, a través de una implementación simple y poco costosa, deben contribuir a la prosperidad de las comunidades, sobretodo aquellas que presentan acuciantes problemas de desarrollo. La Investigación Operativa, disciplina que consiste en el uso de modelos matemáticos y computacionales con objeto de racionalizar el proceso de toma de decisiones, puede realizar un aporte fundamental en el logro de sistemas eficientes que permitan satisfacer las necesidades actuales de la sociedad sin comprometer las generaciones futuras. Es por eso que en esta Tesis se analizan modelos para el diseño de los primeros eslabones de la cadena de logística inversa de Residuos Sólidos Urbanos (RSU) diferenciados en un ambiente urbano. En la primer parte, se aborda el problema de optimizar la localización de puntos de acumulación de residuos en un entorno urbano, a través de un sistema de contenedores comunitarios, considerando los objetivos de minimizar el costo del sistema así como maximizar la calidad del servicio brindado. Los modelos se aplican sobre escenarios de dos ciudades distintas. La primera es la ciudad de Bahía Blanca (Argentina), donde se comparan dos métodos para optimizar problemas multiobjetivo discretos. El segundo caso se corresponde a la ciudad de Montevideo (Uruguay), donde debido a la mayor complejidad de los escenarios se propone una reforma a uno de los métodos utilizados en el caso anterior a los efectos de poder abordar el problema. En ambos caso se logran obtener un conjunto de soluciones mutiobjetivo del problema. La segunda parte de este trabajo se enfoca sobre los problemas de ruteo de vehículos para optimizar los recorridos de los transportes que deben recolectar los residuos acumulados. Luego de un primer análisis, se encuentra un tema que aún no ha sido completamente estudiado en la literatura, y que ha sido aplicado en problemas de transporte de RSU, como lo es el concepto de atracción visual en problemas de ruteo. Se desarrolla una extensa revisión del concepto de atracción visual, analizándose su origen, su relación con los objetivos tradicionales y su importancia en las aplicaciones prácticas de la planificación de rutas. Además, se aplican y comparan las diversas métricas utilizadas para medir atracción visual disponibles en la literatura a los encontrando similitudes entre algunas de las métricas y, a partir de ello, se realizan recomendaciones para que otros autores puedan elegir la métrica que mejor se ajuste a sus intereses. Finalmente, se presenta una heurística para optimizar la atracción visual en una variante del problema de ruteo, la cual logra mejorar la atracción visual con respecto a otras soluciones propuestas en la literatura para un conjunto de instancias.An increasing urbanized and industrialized society, in addition to a shortage of natural resources, has put pressure on the necessity of implementing efficient and sustainable policies that allow the recovery of the resources that are present in our waste, or, at least, allow a suitable final disposition. These policies should not only mitigate the severe environmental impacts associated to garbage mishandling but also, through an inexpensive and straightforward implementation, help to enhance the prosperity of the communities, especially those that are struggling to find a path of sustainable development. Operations Research, a discipline that consists in the development of support tools for the decision-making process through mathematical and computational models, can enormously contribute to obtain efficient systems that satisfy the current society needs without reducing the chances of future generations to have an equally high standard of living. For these reasons, in this Thesis different models to optimize the initial stages in the reverse logistic chain of Municipal Solid Waste (MSW) are analyzed. On the first stage, the problem of optimizing the location of garbage accumulation points in an urban area, while considering the aims of reducing investment costs and enhancing the quality of service, is addressed. The models are applied to scenarios that belong to two different cities. The first one is the Argentinian city of Bahía Blanca, where two different multiobjective resolution methods for discrete problems are compared. The second case corresponds to the Uruguayan city of Montevideo, where due to the higher complexity of the analyzed scenarios a slight reform has to be made to the resolution method used in the rst city. In both cases it was possible to obtain a set of multiobjective solutions of the proposed scnarios. The second part of this Thesis focuses on the routing problems in waste management. After an initial revision, it was found that some works consider visual attractiveness in their optimization process, a topic that has not been completely studied yet. A throughout bibliographic review is performed in order to shed some light on the concept of visual attractiveness and its importance for real-world applications. Furthermore, the different metrics that are used in the literature are compared with the aim of nding similarities and making suggestions about the suitability of each metric in different contexts. Finally, a heuristic to that is able to optimize visual attractiveness in a variant of routing problems is proposed.Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentin

Forest management decision making based on a real options approach: An application to a case in northeastern Argentina

The Net Present Value (NPV) approach is widely applied to assess forest investments, but this method has serious shortcomings, which we propose to overcome by switching to the assessment through the Real Options Approach (ROA). The model in this paper starts with the simulation of the forest´s growth, combined with the projection of the products´ prices and valuing the assets using a binomial model. We include an option of postponement, determining the optimal period of felling. We find that ROA is more robust than the NPV approach because it relaxes the assumption of constancy of both the prices and the discount rate, allowing the determination of the optimal time of felling based on the growth rate of either the forest or the prices of its products. Contrary to the traditional NPV approach, the results obtained with ROA exhibit longer harvest turns and consequently higher profits. The key variable in the ROA, the Real Option Value (ROV) can be shown to be less (albeit moderately) sensitive to decreases of the discount rate than NPV. Moreover, ROV is moderately sensitive to decreases in the price of logs and is negligibly affected by rises in the costs of harvesting, loading and transporting rolls.Fil: Broz, Diego Ricardo. Universidad Nacional de Misiones. Facultad de Ciencias Forestales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Milanesi, Gastón. Universidad Nacional del Sur. Departamento de Ciencias de la Administración; ArgentinaFil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentin

Solving a flow shop scheduling problem with missing operations in an Industry 4.0 production environment

Industry 4.0 is a modern approach that aims at enhancing the connectivity between the different stages of the production process and the requirements of consumers. This paper addresses a relevant problem for both Industry 4.0 and flow shop literature: the missing operations flow shop scheduling problem. In general, in order to reduce the computational effort required to solve flow shop scheduling problems only permutation schedules (PFS) are considered, i.e., the same job sequence is used for all the machines involved. However, considering only PFS is not a constraint that is based on the real-world conditions of the industrial environments, and it is only a simplification strategy used frequently in the literature. Moreover, non-permutation (NPFS) orderings may be used for most of the real flow shop systems, i.e., different job schedules can be used for different machines in the production line, since NPFS solutions usually outperform the PFS ones. In this work, a novel mathematical formulation to minimize total tardiness and a resolution method, which considers both PFS and (the more computationally expensive) NPFS solutions, are presented to solve the flow shop scheduling problem with missing operations. The solution approach has two stages. First, a Genetic Algorithm, which only considers PFS solutions, is applied to solve the scheduling problem. The resulting solution is then improved in the second stage by means of a Simulated Annealing algorithm that expands the search space by considering NPFS solutions. The experimental tests were performed on a set of instances considering varying proportions of missing operations, as it is usual in the Industry 4.0 production environment. The results show that NPFS solutions clearly outperform PFS solutions for this problem.Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Nesmachnow, Sergio. Facultad de Ingeniería; Urugua

Un modelo bi-objetivo de programación entera para localizar puntos de acumulación de residuos: un estudio de caso

Aumentar la eficiencia en la gestión de los Residuos Sólidos Urbanos (RSU) es crucial para los gobiernos municipales, que son los que generalmente se encargan de la recolección, ya que esta actividad consume un porcentaje importante de sus recursos presupuestarios. La incorporación de herramientas de apoyo a la toma de decisiones puede contribuir a mejorar el sistema de gestión de RSU, especialmente reduciendo los costos de inversión requeridos. Este artículo propone una formulación metemática, basada en programación entera, para determinar la localización de puntos de acumulación de residuos minimizando los costos del sistema, incluyendo tanto el costo de instalación de los contenedores como la cantidad de visitas necesarias del vehículo de recolección, lo cual está relacionado con los costos de la logística de recolección. El modelo se aplicó en un conjunto de escenarios reales de una importante ciudad argentina que todavía utiliza un sistema de puerta a puerta, incluyendo tanto intancias que donde los residuos son recolectados sin clasficar, como actualmente se realiza en esta ciudad, como instancias que incorporan la clasificación en origen de los mismos. A pesar de que los escenarios con clasificación en origen resultaron más desafiantes para el algoritmo de resolución propuesto, se obtuvieron un conjunto de soluciones factibles para todos los escenarios planteados. Estas soluciones pueden ser utilizadas como un punto inicial para migrar desde un sistema de puerta a puerta a uno de contenedores comunitarios.Enhancing efficiency in Municipal Solid Waste (MSW) management is crucial for local governments, which are generally in charge of collection, since this activity explains a large proportion of their budgetary expenses. The incorporation of decision support tools can contribute to improve the MSW system, specially by reducing the required investment of funds. This article proposes a mathematical formulation, based on integer programming, to determine the location of garbage accumulation points while minimizing the expenses of the system, i.e., the installment cost of bins and the required number of visits the collection vehicle which is related with the routing cost of the collection. The model was tested in some scenarios of an important Argentinian city that stills has a door-to-door system, including instances with unsorted waste, which is the current situation of the city, and also instances with source classified waste. Although the scenarios with classified waste evidenced to be more challenging for the proposed resolution approach, a set of solutions was provided in all scenarios. These solutions can be used as a starting point for migrating from the current door-to-door system to a community bins system.Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Nesmachnow, Sergio. Universidad de la República; UruguayFil: Toutouh, Jamal. Massachusetts Institute of Technology; Estados Unido

Multiobjective design of sustainable public transportation systems

The design of the bus network is a complex problem in modern cities, since different conflicting objectives have to be considered, from both the perspective of bus companies and the citizens. This article presents a multiobjective model for designing a sustainable public transportation network that simultaneously optimizes the covered travel demands by passengers, the total travel time, and the generated pollution. The proposed model is solved using exact weighted sum and a heuristic procedure based on the standard shortest path problem. Preliminary tests were performed in small real-world instances of Montevideo, Uruguay. Experiments allowed obtaining a set of compromising solutions that in turn allow exploring different trade-off among the optimization criteria. The proposed heuristic was competitive, being able to find a good compromising solution in short computing times.Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Nesmachnow, Sergio. Universidad de la República; UruguayFil: Toutouh, Jamal. Massachusetts Institute of Technology; Estados Unidos1st International Workshop on Advanced Information and Computation Technologies and SystemsIrkutskRusiaMatrosov Institute for System Dynamics and Control Theory of the Siberian Branch of the Russian Academy of Science

Routing in waste collection: a simulated annealing algorithm for an Argentinean case study

The management of the collection of Municipal Solid Waste is a complex task for local governments since it consumes a large portion of their budgets. Thus, the use of computer-aided tools to support decision-making can contribute to improve the efficiency of the system and reduce the associated costs, especially in developing countries, which usually suffer from a shortage of resources. In the present work, a simulated annealing algorithm is proposed to address the problem of designing the routes of waste collection vehicles. The proposed algorithm is compared to a commercial solver based on a mixed-integer programming formulation and two other metaheuristic algorithms, i.e., a state-of-the-art large neighborhood search and a genetic algorithm. The evaluation is carried out on both a well-known benchmark from the literature and real instances of the Argentinean city of Bahía Blanca. The proposed algorithm was able to solve all the instances, having a performance similar to the large neighborhood procedure, while the genetic algorithm showed the worst results. The simulated annealing algorithm was also able to improve the solutions of the solver in many instances of the real dataset.Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Fermani, Matías. Universidad Nacional del Sur. Departamento de Ingeniería; Argentin

An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes

Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real-world energy consumption data, Time-of-Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.Fil: Nesmachnow, Sergio. Facultad de Ingeniería; UruguayFil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Toutouh, Jamal. Massachusetts Institute of Technology. Science And Artificial Intelligence Laboratory; Estados UnidosFil: Luna, Francisco. Universidad de Málaga. Instituto de Tecnologías e Ingeniería del Software; Españ

Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach

In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in demand-side management is crucial for achieving a more sustainable usage of the available resources. In this line of work, this article presents an optimization model to schedule deferrable appliances in households, which simultaneously optimize two conflicting objectives: the minimization of the cost of electricity bill and the maximization of users satisfaction with the consumed energy. Since users satisfaction is based on human preferences, it is subjected to a great variability and, thus, stochastic resolution methods have to be applied to solve the proposed model. In turn, a maximum allowable power consumption value is included as constraint, to account for the maximum power contracted for each household or building. Two different algorithms are proposed: a simulation-optimization approach and a greedy heuristic. Both methods are evaluated over problem instances based on real-world data, accounting for different household types. The obtained results show the competitiveness of the proposed approach, which are able to compute different compromising solutions accounting for the trade-off between these two conflicting optimization criteria in reasonable computing times. The simulation-optimization obtains better solutions, outperforming and dominating the greedy heuristic in all considered scenarios.Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Nesmachnow, Sergio. Universidad de la República; UruguayFil: Toutouh, Jamal. Universidad de Málaga. Departamento de Lenguajes y Ciencias de la Computación; EspañaFil: Luna, Francisco. Universidad de Málaga. Departamento de Lenguajes y Ciencias de la Computación; Españ