11 research outputs found
Viewpoint on “A note on unrelated parallel machine scheduling with time-dependent processing times”
Department of Logistics2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Parallel-machine scheduling with simple linear deterioration to minimize total completion time
2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Single-machine scheduling with deteriorating jobs under a series-parallel graph constraint
Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Single machine parallel-batch scheduling with deteriorating jobs
AbstractWe consider several single machine parallel-batch scheduling problems in which the processing time of a job is a linear function of its starting time. We give a polynomial-time algorithm for minimizing the maximum cost, an O(n5) time algorithm for minimizing the number of tardy jobs, and an O(n2) time algorithm for minimizing the total weighted completion time. Furthermore, we prove that the problem for minimizing the weighted number of tardy jobs is binary NP-hard
Scheduling problems with the effects of deterioration and learning
Author name used in this publication: T. C. E. Cheng2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Scheduling linear deteriorating jobs with an availability constraint on a single machine
2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
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
Fully polynomial time approximation schemes for sequential decision problems
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2005.Includes bibliographical references (p. 65-67).This thesis is divided into two parts sharing the common theme of fully polynomial time approximation schemes. In the first part, we introduce a generic approach for devising fully polynomial time approximation schemes for a large class of problems that we call list scheduling problems. Our approach is simple and unifying, and many previous results in the literature follow as direct corollaries of our main theorem. In the second part, we tackle a more difficult problem; the stochastic lot sizing problem, and give the first fully polynomial time approximation scheme for it. Our approach is based on simple techniques that could arguably have wider applications outside of just designing fully polynomial time approximation schemes.by Mohamed Mostagir.S.M