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

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

Warehouse Management Problem and a KPI Approach: a Case Study

Warehouse and inventory management is a recurring issue in many of the different supplychains in diverse industries, where the constant changes in the markets have a direct impacton the management of warehouses and inventories, either generating over-stocks or shortages.This paper presents a case study on warehouse and inventory management control. Thecompany under study was having problems in this area, where over-stocks were generatedfrequently, leading to various incidents, such as having to store finished and packaged productin unsuitable places, with the associated risk of deterioration. To deal with this problem,control tools based on the KPI (Key Performance Indicator) concept were developed. To thisend, the corresponding problem and the information management process within the SupplyChain department had to be analyzed. In this case, it was observed that the databases werenot synchronized, therefore strategies were proposed to systematize the collection and updating of data. In addition, to summarize the information, we proceeded to the implementationof an interactive form that facilitates the visualization and interpretation of the evolution ofthe process, and to be able to apply an efficient control on it, and thus to propose correctiveactions supported by evidence.Fil: Marziali, Micaela. Universidad Nacional del Sur. Departamento de Ingeniería; 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; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; Argentin

Resolução de um problema de programação de produção em um ambiente flowshop através de um procedimento metaheurístico biobjetivo

En este trabajo se analiza un problema de secuenciación correspondiente a un entorno de producción del tipo flowshop. Se recurre a un procedimiento metaheurístico biobjetivo para resolver el problema de secuenciación correspondiente a dicha configuración productiva. El problema de programación de la producción considerado puede caracterizarse como un skip flowshop en el que se tiene en cuenta la vinculación con los almacenes de materias primas y de productos terminados. Al evaluar la calidad de las soluciones propuestas se tienen en cuenta simultáneamente los objetivos de minimización del tiempo de producción total (makespan) y minimización del retraso total (total tardiness). El procedimiento propuesto está basado en una estrategia multiobjetivo de búsqueda local que responde a la estructura de la metaheurística recocido simulado (simulated annealing). En este caso, el método metaheurístico desarrollado genera un conjunto de soluciones que intenta proporcionar una buena aproximación a la frontera óptima de Pareto. Con el fin de evaluar el rendimiento de la metodología propuesta se realizan una serie de experimentos sobre dos conjuntos de problemas de prueba. A partir de los resultados obtenidos con el primer conjunto de problemas de tamaño más reducido y su comparación con un método exacto de resolución puede concluirse que las soluciones proporcionadas por el procedimiento utilizado resultan adecuadas tanto desde el punto de vista de su calidad como del esfuerzo computacional invertido en su generación. Además, el algoritmo fue probado con problemas de mayor tamaño, para evaluar su comportamiento en espacios de búsqueda más amplios.In this work, we analyze a sequencing problem corresponding to a production environment of the flowshop type. A biobjective metaheuristic procedure is used to solve the sequencing problem corresponding to the aforementioned configuration. The production scheduling problem can be characterized as a skip flowshop in which the links with the warehouses of raw materials and finished products is taken into account. When evaluating the quality of the proposed solutions, the objectives of minimization of maximum completion time (makespan) and minimization of total tardiness are taken into account simultaneously. The proposed procedure is based on a biobjective local search strategy that responds to the structure of simulated annealing metaheuristics. In this case, the metaheuristic method developed generates a set of solutions that try to provide a good approximation to the Pareto optimal front. In order to evaluate the performance of the proposed methodology, a series of experiments are carried out on two sets of test problems. From the results obtained with the first set of problems of smaller size and its comparison with an exact method of resolution, it can be said that the solutions provided by the proposed procedure are suitable both from the point of view of their quality and the computational effort invested in their generation. In addition, the algorithm was tested with larger problems, to evaluate its behavior in wider search spaces.Neste trabalho analisamos um problema de sequenciamento correspondente a um ambiente de produção do tipo flowshop. Um procedimento metaheurístico biobjetivo é usado para resolver o problema de sequenciamento correspondente à referida configuração produtiva. O problema de programação da produção considerado pode ser caracterizado como um skip flowshop no qual a ligação com os armazéns de matérias-primas e produtos acabados é levada em consideração. Ao avaliar a qualidade das soluções propostas, os objetivos de minimização do tempo total de produção e minimização do atraso total são levados em consideração simultaneamente. O procedimento proposto é baseado em uma estratégia de busca local multiobjetivo que responde à estrutura de metaheurísticas de recozimento simulado. Nesse caso, o método metaheurístico desenvolvido gera um conjunto de soluções que tentam fornecer uma boa aproximação à fronteira ótima de Pareto. A fim de avaliar o desempenho da metodologia proposta, uma série de experimentos são realizados em dois conjuntos de problemas de teste. A partir dos resultados obtidos com o primeiro conjunto de problemas de menor tamanho e sua comparação com um método exato de resolução pode-se afirmar que as soluções fornecidas pelo procedimento proposto são adequadas tanto do ponto de vista de sua qualidade quanto do esforço computacional investido em sua geração. Além disso, o algoritmo foi testado com problemas maiores, para avaliar seu comportamento em espaços de busca mais extensos.Fil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; 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; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaXIX Semana da Engenharia de Produção e Mecânica Sulamericana y XIX Seprosul: A Nova Indústria e a Contribuição para os Objetivos do Desenvolvimento SustentávelCuritibaBrasilAsociación de Universidades Grupo Montevideo. Núcleo Disciplinario de Ingeniería Mecánica y de la producción. Programas de Postgrado en Ingeniería Mecánica e Ingeniería de Producció

Flow shop scheduling problem with non-linear learning effects: A linear approximation scheme for non-technical users

Scheduling problems with learning effect have taken a renewed interest in recent years due to increasingly personalized productions, leveraged by the capabilities provided by Industry 4.0. In this work, a learning effect problem described by an exponential curve proportional to the accumulated processing time in a flow shop type configuration was addressed. The objective to be minimized is the makespan. This problem is non-linear, which prevents it from being addressed by standard software such as spreadsheets and commercial MILP solvers. For overcoming this issue a linear approximation approach is proposed. This linear approximation approach consists in representing the exponential curve by a set of piecewise smooth lines. The parameterization of the piecewise smooth line can be solved with spreadsheet tools, using probabilistic models that implicitly provide information about the difficulty of modeling an exponential curve by means of straight lines. Then, a MILP model was generated based on this approximation scheme, which can be solved by standard solvers such as CPLEX or Gurobi. In turn, the problem was also modeled in its MINLP format, and it was solved with a state-of-the-art MINLP solver. The results show the improvement of the linear approximation solution with respect to the MINLP solution, where improvements greater than 10% are achieved in terms of makespan.Fil: Ferraro, Augusto. Universidad Nacional del Sur; 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: Toncovich, Adrián Andrés. Universidad Nacional del Sur; Argentin

Order picking and loading-dock arrival punctuality performance indicators for supply chain management: a case study

Supply chain activity control is an essential part of Supply Chain Management (SCM), ensuring compliance with customer requirements. This paper presents a case study into the control of SCM activities. The study analysed two areas involving two different SC links associated with order picking, and outsourced truck freights, respectively. The studied company had problems with these links. An approach based on developing a KPI (Key Performance Indicator) was proposed to address the issues. Consequently, different affected processes were analysed and characterised, considering the relevant data for defining a KPI. Then, strategies and methods were devised for data collection and processing regarding the system’s current state. Finally, tools were designed to facilitate the interpretation of the system’s current state and thus, pave the way for the decision-making process on corrective measures.Fil: Marziali, Micaela. Universidad Nacional del Sur. Departamento de Ingeniería; 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; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; Argentin

Un problema de programación de la producción en células de fabricación que incluye almacenes

En este trabajo se presenta un problema específico de programación de la producción flow-shop de interés práctico. El sistema de fabricación está configurado como una célula de fabricación y en el planteamiento del problema se consideran los almacenes de materias primas y de productos terminados. El desempeño de la programación se evalúa de una manera multi-objetivo, considerando el tiempo total de producción (makespan) y la tardanza total (tardiness). Se propone una formulación matemática para el problema. Además, se presenta una estrategia meta-heurística para resolver eficientemente dicho problema y obtener soluciones de buena calidad en un tiempo computacional razonable. El procedimiento aplicado se basa en una adaptación de la meta-heurística de recocido simulado. Se generaron conjuntos de problemas para evaluar el método propuesto, obteniendo soluciones óptimas o casi óptimas en tiempos significativamente menores que los requeridos por el enfoque de optimización resuelto mediante CPLEX. Además, el algoritmo fue probado con problemas de mayor tamaño, para evaluar su comportamiento en espacios de búsqueda más extensos.Fil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; 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; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Frutos, Mariano. 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; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaXLVI Jornadas Argentinas de Informática e Investigación OperativaCórdobaArgentinaSociedad Argentina de Informática e Investigación Operativ

Un estudio comparativo de algoritmos metaheurísticos sobre instancias reales de recolección de RSU

La gestión de la recolección de Residuos Sólidos Urbanos es una tarea compleja que deben enfrentar los gobiernos locales, consumiendo gran parte de su presupuesto. La utilización de herramientas computacionales que sirvan de apoyo a la toma de decisiones puede contribuir a mejorar la eficiencia del sistema y disminuir los costos asociados. En el presente trabajo se propone la evaluación de distintas herramientas informáticas exactas y metaheurísticas sobre casos reales de la ciudad de Bahía Blanca. Particularmente, se propone la utilización de CPLEX para resolver un problema de programación matemática y su comparación con algortimos metaheurísticos basados en las técnicas de Simulated Annealing y Large Neighborhood Search. Los resultados muestran que CPLEX encuentra limitaciones para resolver los escenarios más grandes. Por otro lado, las herramientas heurísticas propuestas son competitivas, obteniendo valores cercanos a los métodos exactos con tiempos de cómputo mucho menores. Las herramientas heurísticas también son validadas con respecto a conocidos benchmarks de la literatura.The management of the Municipal Solid Waste collection is a complex task that local governments must face, consuming a large part of their budget. The use of computational tools that support decision-making can contribute to improve the efficiency of the system and reduce the associated costs. This paper proposes the evaluation of different exact and metaheuristic tools on real-world scenarios in the city of Bahía Blanca. In particular, the use of CPLEX is proposed to solve a mathematical programming problem and it is compared with two metaheuristic algorithms based on Simulated Annealing and Large Neighborhood Search techniques. The results show that the exact tool face limitations to solve the larger scenarios. On the other hand, the proposed heuristic tools are competitive, obtaining values that are close to the exact solution in much smaller computing times. The heuristic tools are also validated with respect to well-known benchmarks of the literature.Fil: Fermani, Matías. 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; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaXII Congreso de Ingeniería IndustrialRío GallegosArgentinaUniversidad Tecnológica Nacional. Facultad Regional Santa Cruz. Asociación Argentina de Carreras de Ingeniería Industria

Una Metaheurística de Recocido Simulado para Resolver un Problema de Ruteo de Vehículos en la Recolección de​ Residuos

La gestión de la recolección de Residuos Sólidos Urbanos es una tarea compleja que deben enfrentar los gobiernos locales, consumiendo gran parte de su presupuesto. La utilización de herramientas computacionales que sirvan de apoyo a la toma de decisiones puede contribuir a mejorar la eficiencia del sistema y disminuir los costos asociados. En el presente trabajo se propone un algoritmo de recocido simulado para abordar el problema del diseño de las rutas de vehículos de recolección de residuos. El algoritmo propuesto es comparado contra otros dos algoritmos metaheurísticos: un algoritmo ​ Large Neighborhood Search (LNS) de la literatura y un algoritmo genético estándar. La evaluación se realiza sobre instancias reales de la ciudad de Bahía Blanca y sobre ​ benchmarks de la literatura. El algoritmo propuesto fue capaz de resolver todas las instancias planteadas teniendo un desempeño similar al LNS, mientras que el algoritmo genético estándar evidenció peores resultados.Fil: Fermani, Matías. 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; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaXth International Conference of Production Research-AmericasBahía BlancaArgentinaUniversidad Nacional del SurInternational Foundation of Production Researc

Flow Shop Scheduling Problems in Industry 4.0 Production Environments: Missing Operation Case

The Fourth Industrial Revolution or Industry 4.0 is forcing a completely reorganization of the manufacturing systems in order to implement increasingly automatized processes and customized products. Within this context, advanced computer-aid tools can contribute to give support to decision-makers in this increasingly complex conditions. As a contribution to this process, this chapter addresses an optimization problem that has become progressively common within the Industry 4.0 context: the missing operations flow shop scheduling problem. Conversely, to the traditional flow shop, this problem considers the customization of the final products based on the requirements of the clients. Thus, several operations of the manufacturing cell can be skipped. Moreover, the missing operations can vary from one client to another, increasing the difficulty of the decision-making process. In this chapter we revise the missing operations flow shop scheduling problem under two of the main paradigms of the scheduling literature: considering only permutative schedules, i.e., the same job sequence is used for all the machines involved, and the more computationally expensive case of allowing the optimization problem to consider non-permutative schedules, i.e., different job schedules can be used for different machines in the production line. For these two cases, mathematical formulations that aim at minimizing total tardiness are presented. Furthermore, a two-echelon resolution approach is discussed. This involves firstly a Genetic Algorithm (GA), which only considers permutative schedules, and secondly, a Simulated Annealing algorithm, which taking as an input the solution of the GA it expands the search space by considering non-permutative schedules. Computer experimentation was performed on a set of instances with different proportions of missing operations in order to represent a large variety of the situations that occur in practice at real-world manufacturing cells.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; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Nesmachnow, Sergio. Universidad de la República; Urugua