Determine The Optimal Sequence - dependent Setup Cost and / or Setup Time for Single Demand with Multiple Products Using Modified Assignment Method

Sequencing is the most impact factor on the total setup cost and / or time and the products sequences inside demands that consist from muti-products . It is very important in assembly line and batch production . The most important drawback of existing methods used to solve the sequencing problems is the sequence must has a few products and dependent setup cost or setup time . In this paper we modify the assignment method –based goal programming method to minimize the setup cost and / or setup time. The main advantage of this new method , it is not affected by the number of products in the sequence and can treatment the sequence problems with two or more objectives . Keywords: products sequences , setup cost , setup time , travel salesman problem (TSP ) , modified assignment method , goal programming

Determine The Optimal Sequence -dependent Setup Cost and / or Setup Time for Single Demand with Multiple Products Using Modified Assignment Method

Abstract Sequencing is the most impact factor on the total setup cost and / or time and the products sequences inside demands that consist from muti-products . It is very important in assembly line and batch production . The most important drawback of existing methods used to solve the sequencing problems is the sequence must has a few products and dependent setup cost or setup time . In this paper we modify the assignment method -based goal programming method to minimize the setup cost and / or setup time. The main advantage of this new method , it is not affected by the number of products in the sequence and can treatment the sequence problems with two or more objectives

Meta heuristic for Minimizing Makespan in a Flow-line Manufacturing Cell with Sequence Dependent Family Setup Times

This paper presents a new mathematical model for the problem of scheduling part families and jobs within each part family in a flow line manufacturing cell where the setup times for each family are sequence dependent and it is desired to minimize the maximum completion time of the last job on the last machine (makespan) while processing parts (jobs) in each family together. Gaining an optimal solution for this type of complex problem in large sizes in reasonable computational time using traditional approaches or optimization tools is extremely difficult. A meta-heuristic method based on Simulated Annealing (SA) is proposed to solve the presented model. Based on the computational analyses, the proposed algorithm was found efficient and effective at finding good quality solutions

Diseño y desarrollo de estructuras de planificación eficientes a través de técnicas de simulación y optimización aplicables a entornos productivos complejos

La tesis aborda problemas de secuenciamiento en entornos productivos del tipo flow shop en los que se retira la condición de ordenamientos permutativos. Este tipo de problemas se encuentran inmersos dentro de los sistemas de Planificación y Control de la Producción que dan soporte en la toma de decisiones a las organizaciones o empresas que producen bienes del tipo manufactura. Como primera aproximación al problema se presenta una revisión exhaustiva de la literatura científica sobre problemas flow shop no permutativos (NPFS). De esta forma se pudo enmarcar la tesis doctoral en la literatura de la temática y se definió concretamente la contribución a la literatura del tema. Como resultado del estudio de la literatura se planteó abordar los problemas NPFS desde una perspectiva que permitiera estudiar la estructura de las soluciones para así poder compararlos con los resultados de los problemas flow shop permutativos (PFS). Primeramente, se propuso estudiar los problemas NPFS con makespan como función objetivo bajo un nuevo enfoque de planificación. Para ello se utilizará la metodología de lotes de transferencia o lot streaming, la cual modifica el problema clásico de secuenciamiento incorporando nuevas variables de decisión al problema a optimizar. Las nuevas variables de decisión van asociadas al dimensionamiento del tamaño del lote de producción. Este estudio reportó resultados para NPFS y PFS bastante similares, aunque el caso NPFS obtuvo leves mejoras para las instancias más grandes. No obstante, el esfuerzo computacional requerido para resolver el caso NPFS fue considerablemente mayor que requerido para PFS. A partir de estos resultados, se planteó un estudio conceptual de las soluciones NPFS y PFS para el caso de dos trabajos en términos de caminos críticos (conjunto de actividades que definen el makespan) que posibilitaron caracterizar ambos conjuntos de soluciones de forma no-paramétrica, es decir, independizarse de los parámetros que definen un escenario. De este estudio de caminos críticos, se pudieron analizar una serie de propiedades y definir criterios de dominancia entre las soluciones NPFS y PFS que permitirían reducir el espacio factible. A su vez, el estudio no-paramétrico permitió realizar una serie experimentaciones computacionales innovadoras, que dieron sustento al desarrollo de algunas hipótesis sobre la relación de las soluciones NPFS y PFS para el caso de que los problemas sean evaluados en escenarios paramétricamente definidos. Para evaluar estas hipótesis se implementaron experimentaciones paramétricas a través de programación matemática, las cuales validaron las hipótesis planteadas.This dissertation focuseson non-permutation scheduling problems in flow shop production settings. These problems, proper of systems of Production Planning and Control, are central to the decision making processes in organizations or firms producing manufactured goods. A first look into these problems requires a thorough review of the scientific literature on non-permutation flow shop (NPFS) problems. This review provides a background on this issue and defines precisely the contribution of this thesis to the literature. A novel and interesting approach to address NPFS problems is by studying the structure of the solutions, comparing it to the corresponding structure of permutation flow shop (PFS) problems. In this light, we study NPFS problems where makespan is minimized considering a special planning technique involving lot streaming. This technique modifies the regular scheduling problem adding new decision variables, related to production lot sizing. From the implementation of lot streaming on these problems we obtain new results. The main conclusion is that the makespans of NPFS and PFS problems are quite similar, although NPFS yields a better makespan for larger instances. The computational effort required by NPFS problems is much larger than that of solving PFS ones. Up from these results, we develop a new approach to the analysis of solutions to NPFS and PFS problems. We center on the two jobs case, and on the concept of critical path (enumerating the set of activities that defines makespan). This allows the non-parametric characterization of the solutions, freeing them from the dependence on particular parameters. We analyze a family of propertiesthat yield dominance criteria for the comparison between NPFS and PFS solutions, reducing, in general, the number of feasible solutions. In addition, this non-parametric method allows the design of novel computational experimental frameworks, yielding newinsights on the relation between NPFS and PFS solutions for parametric scenarios. To assess these hypotheses, we obtain via an application of mathematical programming a set of parametric results that we test in experiments that confirm the aforementioned hypotheses.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; Argentin

Operation-Level Sequence- Dependent Setup Time Reduction In Dynamic Cellular Manufacturing Systems

In closed job shop, in which a fixed number of products are produced on a repetitive basis, when there are significant sequence dependent setup times and costs involved, cell formation (CF) problem should consider minimizing the sequence-dependent setup times in order to minimize the production cost. Setup time reduction in CMS has gained little to modest attention in the literature. This could be attributed to the fact that the fundamental problem in cell formation in CMS has been mainly about material handling and machine utilization while setup time was presumed to normally decrease as a result of grouping similar parts in a manufacturing cell. Despite more than three decades of history of CMS’s it has been relatively recent that setup time has been included in cell formation problems and found a place in the existing models. Sequence-dependent setup time in the literature has been dealt with mostly for scheduling part-families in a single manufacturing cell or in allocation of parts to cells in a pure flow shop. In this thesis, the issue of setup time has been extended to the members of a part family and to its lowest level which is operation-level and incorporated in general cell formation problem in a dynamic CMS. In this thesis we have developed a multi-period integer programming CF model to address the reduction of the sequence-dependent setup time as well as considering the dynamic nature of today’s manufacturing environment in CMS, where the product mix demanded would change in different time periods. Due to time complexity of the problem, a two stage solution approach has been adopted. First a GA-based heuristic was developed that provides near optimal solutions for single-period problems of the global model. The performance of the GA-based heuristic was successfully evaluated versus optimization software. Second, a dynamic programming (DP)-based heuristic was developed that reintegrates the single-period solutions into a multi-period solution. The performance of the DP-based heuristic was also evaluated against optimization softwar