Solving no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines and rework time by the adjusted discrete Multi Objective Invasive Weed Optimization and fuzzy dominance approach

Purpose: Adjusted discrete Multi-Objective Invasive Weed Optimization (DMOIWO) algorithm, which uses fuzzy dominant approach for ordering, has been proposed to solve No-wait two-stage flexible flow shop scheduling problem. Design/methodology/approach: No-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times and probable rework in both stations, different ready times for all jobs and rework times for both stations as well as unrelated parallel machines with regards to the simultaneous minimization of maximum job completion time and average latency functions have been investigated in a multi-objective manner. In this study, the parameter setting has been carried out using Taguchi Method based on the quality indicator for beater performance of the algorithm. Findings: The results of this algorithm have been compared with those of conventional, multi-objective algorithms to show the better performance of the proposed algorithm. The results clearly indicated the greater performance of the proposed algorithm. Originality/value: This study provides an efficient method for solving multi objective no-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times, probable rework in both stations, different ready times for all jobs, rework times for both stations and unrelated parallel machines which are the real constraints.Peer Reviewe

Lot Streaming in Different Types of Production Processes: A PRISMA Systematic Review

At present, any industry that wanted to be considered a vanguard must be willing to improve itself, developing innovative techniques to generate a competitive advantage against its direct competitors. Hence, many methods are employed to optimize production processes, such as Lot Streaming, which consists of partitioning the productive lots into overlapping small batches to reduce the overall operating times known as Makespan, reducing the delivery time to the final customer. This work proposes carrying out a systematic review following the PRISMA methodology to the existing literature in indexed databases that demonstrates the application of Lot Streaming in the different production systems, giving the scientific community a strong consultation tool, useful to validate the different important elements in the definition of the Makespan reduction objectives and their applicability in the industry. Two hundred papers were identified on the subject of this study. After applying a group of eligibility criteria, 63 articles were analyzed, concluding that Lot Streaming can be applied in different types of industrial processes, always keeping the main objective of reducing Makespan, becoming an excellent improvement tool, thanks to the use of different optimization algorithms, attached to the reality of each industry.This work was supported by the Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project CONIN-P-256-2019, and SENESCYT by grants “Convocatoria Abierta 2011” and “Convocatoria Abierta 2013”

An Energy-Efficient No Idle Permutations Flow Shop Scheduling Problem Using Grey Wolf Optimizer Algorithm

Energy consumption has become a significant issue in businesses. It is known that the industrial sector has consumed nearly half of the world's total energy consumption in some cases. This research aims to propose the Grey Wolf Optimizer (GWO) algorithm to minimize energy consumption in the No Idle Permutations Flowshop Problem (NIPFP). The GWO algorithm has four phases: initial population initialization, implementation of the Large Rank Value (LRV), grey wolf exploration, and exploitation. To determine the level of machine energy consumption, this study uses three different speed levels. To investigate this problem, 9 cases were used. The experiments show that it produces a massive amount of energy when a job is processed fast. Energy consumption is lower when machining at a slower speed. The performance of the GWO algorithm has been compared to that of the Cuckoo Search (CS) algorithm in several experiments. In tests, the Grey Wolf Optimizer (GWO) outperforms the Cuckoo Search (CS) algorithm

Estado del arte de las aplicaciónes del concepto de Lot Streaming a la secuenciación en talleres de flujo

[ES] El presente estudio, versa en una revisión de los articulaos publicados sobre la aplicación del concepto de Lot Streaming y su aplicación en la secuenciación de talleres de flujo. Los documentos se clasificaron de acuerdo a los dimensionamientos que se asocian a evidenciar las combinaciones y comparaciones de diversos algoritmos utilizados para mejorar los talleres de flujo a través de la división de Sub-lotes. Lo cual representa un particular enfoque para la revisión de artículos e investigadores que han abordado esta temática, aplicando una metodología con un enfoque cualitativo, por cuanto la información se sistematizó a través del software Atlas_ti en correspondencia a las variables exploradas en cada estudio que identifican la eficacia de la división de lotes en la solución de problemas de talleres de flujo. La mayoría de los estudios identificaron algoritmos genéticos y genéticos híbridos, mostrando algunas desventajas frente a los tradicionales y en pocos estudios evidenciando la eficacia en su aplicación.[EN] The present study is based on a review of the articles published on the application of the Lot Streaming concept and its application in the sequencing of flow workshops. The documents were classified according to the sizing that is associated to evidence the combinations and comparisons of different algorithms used to improve the workshops of flow through the division of sublots. This represents a particular approach to the review of articles and researchers that have addressed this issue, applying a methodology with a qualitative approach, as the information is systematized through the Atlas_ti software in correspondence to the variables explored in each study that identify the efficiency of the division of batches in the solution of problems of flow workshops. The majority of the studies identified hybrid genetic and genetic algorithms, showing some disadvantages compared to the traditional ones and in few studies showing the efficacy in their application.Velecela Rojas, SJ. (2018). Estado del arte de las aplicaciónes del concepto de Lot Streaming a la secuenciación en talleres de flujo. http://hdl.handle.net/10251/110033TFG

A bi-objective hybrid vibration damping optimization model for synchronous flow shop scheduling problems

Flow shop scheduling deals with the determination of the optimal sequence of jobs processing on machines in a fixed order with the main objective consisting of minimizing the completion time of all jobs (makespan). This type of scheduling problem appears in many industrial and production planning applications. This study proposes a new bi-objective mixed-integer programming model for solving the synchronous flow shop scheduling problems with completion time. The objective functions are the total makespan and the sum of tardiness and earliness cost of blocks. At the same time, jobs are moved among machines through a synchronous transportation system with synchronized processing cycles. In each cycle, the existing jobs begin simultaneously, each on one of the machines, and after completion, wait until the last job is completed. Subsequently, all the jobs are moved concurrently to the next machine. Four algorithms, including non-dominated sorting genetic algorithm (NSGA II), multi-objective simulated annealing (MOSA), multi-objective particle swarm optimization (MOPSO), and multi-objective hybrid vibration-damping optimization (MOHVDO), are used to find a near-optimal solution for this NP-hard problem. In particular, the proposed hybrid VDO algorithm is based on the imperialist competitive algorithm (ICA) and the integration of a neighborhood creation technique. MOHVDO and MOSA show the best performance among the other algorithms regarding objective functions and CPU Time, respectively. Thus, the results from running small-scale and medium-scale problems in MOHVDO and MOSA are compared with the solutions obtained from the epsilon-constraint method. In particular, the error percentage of MOHVDO’s objective functions is less than 2% compared to the epsilon-constraint method for all solved problems. Besides the specific results obtained in terms of performance and, hence, practical applicability, the proposed approach fills a considerable gap in the literature. Indeed, even though variants of the aforementioned meta-heuristic algorithms have been largely introduced in multi-objective environments, a simultaneous implementation of these algorithms as well as a compared study of their performance when solving flow shop scheduling problems has been so far overlooked

A water flow algorithm for optimization problems

Ph.DDOCTOR OF PHILOSOPH

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

Modelo de programación de producción en planta tipo Flexible Job Shop (FJSSP) con tiempos difusos de procesamiento y alistamiento dependiente de la secuencia, y ventanas de tiempo en las entregas, mediante algoritmos genéticos

El objeto de estudio del presente trabajo final de maestría es la programación de producción en plantas con configuración Flexible Job Shop en condiciones de incertidumbre. El problema de investigación se seleccionó debido los requerimientos actuales de muchas empresas manufactureras, que producen una alta cantidad de productos, con rutas de procesamiento diferentes y con ciclos de vidas muy cortos, que hace complejo en muchas ocasiones determinar tiempos de alistamiento y procesamiento con certidumbre. Consecuente con lo anterior, se diseñó una metodología basada en un algoritmo genético para programar los trabajos en un sistema Flexible Job Shop, con tiempos de alistamiento dependiente de la secuencia difusos, tiempos de procesamiento difusos y ventanas de tiempo en las entregas, con el fin de minimizar la tardanza ponderada y la prontitud ponderada. A partir de la experimentación, se evidencia un alto rendimiento del algoritmo desarrollado en cuanto a las soluciones encontradas y al rendimiento al compararse con otros algoritmos.Abstract: This thesis aims to present a production-scheduling model in plants with Flexible Job Shop configuration under uncertainty. The research problem was selected due to the current requirements of many manufacturing companies, which produce a high number of products, with different processing routes and very short life cycles, making difficult to determine setup and processing times. Consequently, a methodology based on a genetic algorithm is proposed to schedule jobs in a Flexible Job Shop system considering sequence-dependent fuzzy setup times, fuzzy processing times and due date time windows in order to minimize weighted tardiness and weighted earliness. From the experimentation, the proposed algorithm provide a high performance in terms of solution quality when compared with other scheduling algorithms.Maestrí