COMBINATION OF ACO AND PSO TO MINIMIZE MAKESPAN IN ORDERED FLOWSHOP SCHEDULING PROBLEMS

The problem of scheduling flowshop production is one of the most versatile problems and is often encountered in many industries. Effective scheduling is important because it has a significant impact on reducing costs and increasing productivity. However, solving the ordered flowshop scheduling problem with the aim of minimizing makespan requires a difficult computation known as NP-hard. This research will contribute to the application of combination ACO and PSO to minimize makespan in the ordered flowshop scheduling problem. The performance of the proposed scheduling algorithm is evaluated by testing the data set of 600 ordered flowshop scheduling problems with various combinations of job and machine size combinations. The test results show that the ACO-PSO algorithm is able to provide a better scheduling solution for the scheduling group with small dimensions, namely 76 instances from a total of 600 inctances and is not good at obtaining makespan in the scheduling group with large dimensions. The ACO-PSO algorithm uses execution time which increases as the dimension size (multiple jobs and many machines) increases in a scheduled instanc

Online scheduling: a survey

In this article a deep search of the literature of online scheduling is conducted. This paper intends to assess the developments and solutions found for online scheduling problems. Online scheduling is a very important topic since most of the real scheduling problems have dynamic characteristics. First, it was developed a literature review about scheduling problems, dividing them in stochastic and deterministic problems as well as in online and offline problems. Then, a bibliometric analysis was performed. Finally, some case studies in the field of online scheduling were analyzed. Online Scheduling is mostly explored in industry and health areas. In some articles explored there is a rescheduling, and the sequence of task may change due to the arrival of new tasks. In other cases, the new tasks are introduced in blocks of time that do not affect the previous schedule. This last technique is limited, since, with the arrival of new tasks, the schedule is not re-evaluated. Therefore, it is thought that, in future work, within the scope of online scheduling, when new tasks or other significant changes enter the system, the system should be evaluated, allowing the necessary changes to be made to the existing schedule. The Industry 4.0 and the evolution of Internet of Things (IoT), Deep Learning and Machine Learning favours a continuous and real-time flow of information, which allows the implementation of real-time online scheduling. This is a branch that should be explored in future works.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00319/2020

Sistema de soporte de decisiones para la programación de producción de la empresa Café Ruta 45

Café Ruta 45 se localiza en Pitalito, Huila (Colombia). Esta compañía se encarga de trillar, tostar, moler y empacar café de la región. Actualmente, la demanda de Café Ruta 45 es dinámica y altamente volátil. Por esta razón, la compañía tiene poco de control sobre la producción y los empleados se ven obligados a aumentar sus horas de trabajo para completar las ordenes. Ciertamente, la salud de los empleados y el servicio al cliente pueden verse comprometidos negativamente. Esta investigación presenta un sistema de soporte de decisiones para Café Ruta 45, programando las orden de producción, respondiendo a la demanda en un entorno de producción Flow-shop, respetando las horas de trabajo y entregando el producto al cliente en el menor tiempo posible. El sistema de soporte de decisiones proporciona la programación de producción semanal minimizando la tardanza total ponderada, utilizando un enfoque de programación predictiva-reactiva. Mientras que el componente predictivo programa la orden de producción al comienzo de la semana a través de una metaheurística de Tabu Search, el componente reactivo reorganiza el cronograma basado en la regla de despacho llamada Costo aparente de tardanza (ATC) para las órdenes que llegan inesperadamente e interrumpen el horario inicial. El sistema de soporte de decisiones fue validado a través de una simulación, donde se muestra visualmente la programación de producción. Se propone un sistema de soporte de decisiones para la programación de producción de Café Ruta 45, minimizando el impacto en los empleados y maximizando la satisfacción del cliente.The coffee roaster Café Ruta 45 is located in Pitalito, Colombia. This company is focused to threshing, roasting, grinding and packing coffee from the region of Huila. Currently, the demand of Café Ruta 45 is being dynamic and highly volatile. For this reason, the company has few or lack of control over the production schedule and employees are forced to increase their working hours until late shifts. Certainly, the employee health and customer service can be compromised negatively. This research presents a decision support system for Café Ruta 45, used to schedule the production order, responding the demand in a flow-shop production environment, respecting the working hours and delivering the product to the customer in the shortest possible time. The decision support system provides the weekly production scheduling minimizing the weighted total tardiness, using an approach predictive-reactive scheduling. While the predictive component schedules the production order at the beginning of the week through a Tabu Search metaheuristic, the reactive component re-organize the schedule based on the dispatching rule called the Apparent Tardiness Cost (ATC) for the orders that arrive unexpectedly and disrupt the initial schedule. The decisión support system was validated through a simulation, where the production scheduling is visually shown. A decision support system is proposed to production scheduling of Café Ruta 45 minimizing the impact on employees and maximizing customer satisfaction.Ingeniero (a) IndustrialPregrad

Online Scheduling of Ordered Flow Shops

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Online scheduling of ordered flow shops

We consider online as well as offline scheduling of ordered flow shops with the makespan as objective. In an online flow shop scheduling problem, jobs are revealed to a decisionmaker one by one going down a list. When a job is revealed to the decision maker, its operations have to be scheduled irrevocably without having any information regarding jobs that will be revealed afterwards. We consider for the online setting the so-called Greedy Algorithm which generates permutation schedules in which the jobs on the machines are at all times processed without any unnecessary delays. We focus on ordered flow shops, in particular proportionate flow shops with different speeds and proportionate flow shops with different setup times. We analyze the competitive ratio of the Greedy Algorithm for such flow shops in the online setting. For several cases, we derive lower bounds on the competitive ratios. (C) 2018 Elsevier B.V. All rights reserved.11Nscopu