A Decomposition Algorithm for Single and Multiobjective Integrated Market Selection and Production Planning

We study an integrated market selection and production planning problem. There is a set of markets with deterministic demand, and each market has a certain revenue that is obtained if the market's demand is satisfied throughout a planning horizon. The demand is satisfied with a production scheme that has a lot-sizing structure. The problem is to decide on which markets' demand to satisfy and plan the production simultaneously. We consider both single and multiobjective settings. The single objective problem maximizes the profit, whereas the multiobjective problem includes the maximization of the revenue and the minimization of the production cost objectives. We develop a decomposition-based exact solution algorithm for the single objective setting and show how it can be used in a proposed three-phase algorithm for the multiobjective setting. The master problem chooses a subset of markets, and the subproblem calculates an optimal production plan to satisfy the selected markets' demand. We investigate the subproblem from a cooperative game theory perspective to devise cuts and strengthen them based on lifting. We also propose a set of valid inequalities and preprocessing rules to improve the proposed algorithm. We test the efficacy of our solution method over a suite of problem instances and show that our algorithm substantially decreases solution times for all problem instances.</p

A variable neighborhood descent heuristic for a multi-item lot-sizing problem with remanufacturing

Objective: to implement the neighborhood descent heuristic approach into solving the lot-sizing problem.Methods: a variable neighborhood descent heuristic approach and a framework of lot-sizing problem.Results: dynamic lot-sizing problem is supposed to be an NP-hard problem - a problem for which there is no known polynomial algorithm, so the time to find a solution grows with a problem size. In the article, we explain the basic lot-sizing, lot-sizing in closed-loop, describe methods used by other researchers, and provide reasoning for using a variable neighborhood descent heuristic method. Implementation of the method is presented in a numerical example. Application of the method significantly reduces estimation time when searching for lot-sizing problem solution. Application of the method significantly reduces estimation time when searching for lot-sizing problem solution.Scientific novelty: the method may significantly reduce time expenses during the solution of the lot-sizing problem. Practical significance: the method may be implemented for solving the lot-sizing problem in various industries, such as pulp and paper, consumer goods industry and heavy industry

Mejoramiento de programación de producción en planta de inyección de plásticos usando un algoritmo genético

70 páginasEste proyecto de grado buscó solucionar el problema de secuenciar un conjunto de trabajos y moldes de inyección en máquinas inyectoras en una planta de inyección, con el fin de reducir el makespan y tardanza. La planta de inyección es el primer eslabón de la cadena de producción en una fábrica de productos de consumo masivo. El proyecto se caracterizó como un problema de secuenciación de trabajos en máquinas no relacionadas paralelas. El proyecto aborda el problema con dos métodos, el primero usando programación lineal entera mixta (MILP) y el segundo usando un algoritmo genético. El método exacto funciona bien con instancias pequeñas de máximo 10 trabajos y cinco máquinas. Respecto al segundo método se diseñó un algoritmo genético con una función fitness completamente original, el algoritmo genético permite encontrar soluciones de calidad en corto tiempo para instancias más grandes y complejas si se compara con el método exacto. El algoritmo genético requirió un ajuste de sus parámetros usando diseño factorial multinivel. Con el objetivo de probar el método exacto de solución y lograr una comparación estricta entre los dos métodos de solución se desarrollaron instancias de dos tipos: random y reales con información de la planta de inyección. Luego se desarrollaron experimentos computacionales solucionando las instancias con los dos métodos. Los resultados de los experimentos permitieron establecer que el algoritmo genético propuesto genera soluciones iguales o mejores en instancias random comparado con el método exacto.This thesis developed a method to find solutions to a scheduling problem in an injection mold factory, intending to reduce the makespan and tardiness. The injection mould factory is the first stage in a massive consumer product factory location. The project was characterized as a nonrelated parallel machine scheduling problem. The project approached the problem with two methods: the 1st one using mixed-integer linear programming (MILP) and the 2nd one using a genetic algorithm. The exact solution method works fine with small instances, with a maximal size of ten jobs and five machines. About the second method, a genetic algorithm was designed with a completely original fitness function, the genetic algorithm was able to find several quality solutions in a shorter time for larger and complex instances if it is compared with the exact solution method. The genetic algorithm required several parameter adjustments, the multilevel factorial design was used to do so. With the objective of testing the exact method and to can achieve a strict comparison between both solution methods, two kinds of instances were developed: 1 kind with random data and the other one with real production data. After it, several computational experiments were developed, solving the whole instances with both solution methods. Experiments results allowed the researchers to conclude that the proposed genetic algorithm creates equal or better solutions if it is compared with the exact solution method with random instances. One study case was developed, this case represents the production schedule for a month for injected components labeled as type A, the study case was used to compare the current scheduling method used in the injection factory versus the proposed genetic algorithm. The genetic algorithm provides a scheduling program with Cmax and Tardiness values 30% lower than the current scheduling method.Maestría en Gerencia de IngenieríaMagíster en Gerencia de Ingenierí

Cost Factor Focused Scheduling and Sequencing: A Neoteric Literature Review

The hastily emergent concern from researchers in the application of scheduling and sequencing has urged the necessity for analysis of the latest research growth to construct a new outline. This paper focuses on the literature on cost minimization as a primary aim in scheduling problems represented with less significance as a whole in the past literature reviews. The purpose of this paper is to have an intensive study to clarify the development of cost-based scheduling and sequencing (CSS) by reviewing the work published over several parameters for improving the understanding in this field. Various parameters, such as scheduling models, algorithms, industries, journals, publishers, publication year, authors, countries, constraints, objectives, uncertainties, computational time, and programming languages and optimization software packages are considered. In this research, the literature review of CSS is done for thirteen years (2010-2022). Although CSS research originated in manufacturing, it has been observed that CSS research publications also addressed case studies based on health, transportation, railway, airport, steel, textile, education, ship, petrochemical, inspection, and construction projects. A detailed evaluation of the literature is followed by significant information found in the study, literature analysis, gaps identification, constraints of work done, and opportunities in future research for the researchers and experts from the industries in CSS