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

Two-machine flowshop scheduling with flexible operations and controllable processing times

Ankara : The Department of Industrial Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011.Thesis (Master's) -- Bilkent University, 2011.Includes bibliographical references leaves 77-84.In this study, we consider a two-machine flowshop scheduling problem with identical jobs. Each of these jobs has three operations, where the first operation must be performed on the first machine, the second operation must be performed on the second machine, and the third operation (named as flexible operation) can be performed on either machine but cannot be preempted. Highly flexible CNC machines are capable of performing different operations as long as the required cutting tools are loaded on these machines. The processing times on these machines can be changed easily in albeit of higher manufacturing cost by adjusting the machining parameters like the speed of the machine, feed rate, and/or the depth of cut. The overall problem is to determine the assignment of the flexible operations to the machines and processing times for each job simultaneously, with the bicriteria objective of minimizing the manufacturing cost and minimizing makespan. For such a bicriteria problem, there is no unique optimum but a set of nondominated solutions. Using ǫ constraint approach, the problem could be transformed to be minimizing total manufacturing cost objective for a given upper limit on the makespan objective. The resulting single criteria problem is a nonlinear mixed integer formulation. For the cases where the exact algorithm may not be efficient in terms of computation time, we propose an efficient approximation algorithm.Uruk, ZeynepM.S

Cut generation based algorithms for unrelated parallel machine scheduling problems

Research on scheduling in the unrelated parallel machine environment is at best scarce. Moreover, almost all existing work in this area is focused on the minimization of completion time related performance measures and the solution approaches available in the literature suffer from scalability issues. In this dissertation, we leverage on the success of the mathematical programming based decomposition approaches and devise scalable, efficient, and effective cut generation based algorithms for four NP-hard unrelated parallel machine scheduling problems. In the first part,we develop a newpreemptive relaxation for the totalweighted tardiness and total weighted earliness/tardiness problems and devise a Benders decomposition algorithm for solving this preemptive relaxation formulated as a mixed integer linear program. We demonstrate the effectiveness of our approach with instances up to 5 machines and 200 jobs The second part deals with the problem of minimizing the total weighted completion time and proves that the preemptive relaxation developed in part one is an exact formulation for this problem. By exploiting the structural properties of the performance measure, we attain an exact Benders decomposition algorithm which solves instances with up to 1000 jobs and 8 machines to optimality within a few seconds. In the last part, we tackle the unrestricted common due date just-in-time scheduling problemand develop a logic-based Benders decomposition algorithm. Aside from offering the best solution approach for this problem, we demonstrate that it is possible to devise scalable logic-based algorithms for scheduling problems with irregular minsum objectives

Application of nature-inspired optimization algorithms to improve the production efficiency of small and medium-sized bakeries

Increasing production efficiency through schedule optimization is one of the most influential topics in operations research that contributes to decision-making process. It is the concept of allocating tasks among available resources within the constraints of any manufacturing facility in order to minimize costs. It is carried out by a model that resembles real-world task distribution with variables and relevant constraints in order to complete a planned production. In addition to a model, an optimizer is required to assist in evaluating and improving the task allocation procedure in order to maximize overall production efficiency. The entire procedure is usually carried out on a computer, where these two distinct segments combine to form a solution framework for production planning and support decision-making in various manufacturing industries. Small and medium-sized bakeries lack access to cutting-edge tools, and most of their production schedules are based on personal experience. This makes a significant difference in production costs when compared to the large bakeries, as evidenced by their market dominance. In this study, a hybrid no-wait flow shop model is proposed to produce a production schedule based on actual data, featuring the constraints of the production environment in small and medium-sized bakeries. Several single-objective and multi-objective nature-inspired optimization algorithms were implemented to find efficient production schedules. While makespan is the most widely used quality criterion of production efficiency because it dominates production costs, high oven idle time in bakeries also wastes energy. Combining these quality criteria allows for additional cost reduction due to energy savings as well as shorter production time. Therefore, to obtain the efficient production plan, makespan and oven idle time were included in the objectives of optimization. To find the optimal production planning for an existing production line, particle swarm optimization, simulated annealing, and the Nawaz-Enscore-Ham algorithms were used. The weighting factor method was used to combine two objectives into a single objective. The classical optimization algorithms were found to be good enough at finding optimal schedules in a reasonable amount of time, reducing makespan by 29 % and oven idle time by 8 % of one of the analyzed production datasets. Nonetheless, the algorithms convergence was found to be poor, with a lower probability of obtaining the best or nearly the best result. In contrast, a modified particle swarm optimization (MPSO) proposed in this study demonstrated significant improvement in convergence with a higher probability of obtaining better results. To obtain trade-offs between two objectives, state-of-the-art multi-objective optimization algorithms, non-dominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm, generalized differential evolution, improved multi-objective particle swarm optimization (OMOPSO) and speed-constrained multi-objective particle swarm optimization (SMPSO) were implemented. Optimization algorithms provided efficient production planning with up to a 12 % reduction in makespan and a 26 % reduction in oven idle time based on data from different production days. The performance comparison revealed a significant difference between these multi-objective optimization algorithms, with NSGA-II performing best and OMOPSO and SMPSO performing worst. Proofing is a key processing stage that contributes to the quality of the final product by developing flavor and fluffiness texture in bread. However, the duration of proofing is uncertain due to the complex interaction of multiple parameters: yeast condition, temperature in the proofing chamber, and chemical composition of flour. Due to the uncertainty of proofing time, a production plan optimized with the shortest makespan can be significantly inefficient. The computational results show that the schedules with the shortest and nearly shortest makespan have a significant (up to 18 %) increase in makespan due to proofing time deviation from expected duration. In this thesis, a method for developing resilient production planning that takes into account uncertain proofing time is proposed, so that even if the deviation in proofing time is extreme, the fluctuation in makespan is minimal. The experimental results with a production dataset revealed a proactive production plan, with only 5 minutes longer than the shortest makespan, but only 21 min fluctuating in makespan due to varying the proofing time from -10 % to +10 % of actual proofing time. This study proposed a common framework for small and medium-sized bakeries to improve their production efficiency in three steps: collecting production data, simulating production planning with the hybrid no-wait flow shop model, and running the optimization algorithm. The study suggests to use MPSO for solving single objective optimization problem and NSGA-II for multi-objective optimization problem. Based on real bakery production data, the results revealed that existing plans were significantly inefficient and could be optimized in a reasonable computational time using a robust optimization algorithm. Implementing such a framework in small and medium-sized bakery manufacturing operations could help to achieve an efficient and resilient production system.Die Steigerung der Produktionseffizienz durch die Optimierung von Arbeitsplänen ist eines der am meisten erforschten Themen im Bereich der Unternehmensplanung, die zur Entscheidungsfindung beiträgt. Es handelt sich dabei um die Aufteilung von Aufgaben auf die verfügbaren Ressourcen innerhalb der Beschränkungen einer Produktionsanlage mit dem Ziel der Kostenminimierung. Diese Optimierung von Arbeitsplänen wird mit Hilfe eines Modells durchgeführt, das die Aufgabenverteilung in der realen Welt mit Variablen und relevanten Einschränkungen nachbildet, um die Produktion zu simulieren. Zusätzlich zu einem Modell sind Optimierungsverfahren erforderlich, die bei der Bewertung und Verbesserung der Aufgabenverteilung helfen, um eine effiziente Gesamtproduktion zu erzielen. Das gesamte Verfahren wird in der Regel auf einem Computer durchgeführt, wobei diese beiden unterschiedlichen Komponenten (Modell und Optimierungsverfahren) zusammen einen Lösungsrahmen für die Produktionsplanung bilden und die Entscheidungsfindung in verschiedenen Fertigungsindustrien unterstützen. Kleine und mittelgroße Bäckereien haben zumeist keinen Zugang zu den modernsten Werkzeugen und die meisten ihrer Produktionspläne beruhen auf persönlichen Erfahrungen. Dies macht einen erheblichen Unterschied bei den Produktionskosten im Vergleich zu den großen Bäckereien aus, was sich in deren Marktdominanz widerspiegelt. In dieser Studie wird ein hybrides No-Wait-Flow-Shop-Modell vorgeschlagen, um einen Produktionsplan auf der Grundlage tatsächlicher Daten zu erstellen, der die Beschränkungen der Produktionsumgebung in kleinen und mittleren Bäckereien berücksichtigt. Mehrere einzel- und mehrzielorientierte, von der Natur inspirierte Optimierungsalgorithmen wurden implementiert, um effiziente Produktionspläne zu berechnen. Die Minimierung der Produktionsdauer ist das am häufigsten verwendete Qualitätskriterium für die Produktionseffizienz, da sie die Produktionskosten dominiert. Jedoch wird in Bäckereien durch hohe Leerlaufzeiten der Öfen Energie verschwendet was wiederum die Produktionskosten erhöht. Die Kombination beider Qualitätskriterien (minimale Produktionskosten, minimale Leerlaufzeiten der Öfen) ermöglicht eine zusätzliche Kostenreduzierung durch Energieeinsparungen und kurze Produktionszeiten. Um einen effizienten Produktionsplan zu erhalten, wurden daher die Minimierung der Produktionsdauer und der Ofenleerlaufzeit in die Optimierungsziele einbezogen. Um optimale Produktionspläne für bestehende Produktionsprozesse von Bäckereien zu ermitteln, wurden folgende Algorithmen untersucht: Particle Swarm Optimization, Simulated Annealing und Nawaz-Enscore-Ham. Die Methode der Gewichtung wurde verwendet, um zwei Ziele zu einem einzigen Ziel zu kombinieren. Die Optimierungsalgorithmen erwiesen sich als gut genug, um in angemessener Zeit optimale Pläne zu berechnen, wobei bei einem untersuchten Datensatz die Produktionsdauer um 29 % und die Leerlaufzeit des Ofens um 8 % reduziert wurde. Allerdings erwies sich die Konvergenz der Algorithmen als unzureichend, da nur mit einer geringen Wahrscheinlichkeit das beste oder nahezu beste Ergebnis berechnet wurde. Im Gegensatz dazu zeigte der in dieser Studie ebenfalls untersuchte modifizierte Particle-swarm-Optimierungsalgorithmus (mPSO) eine deutliche Verbesserung der Konvergenz mit einer höheren Wahrscheinlichkeit, bessere Ergebnisse zu erzielen im Vergleich zu den anderen Algorithmen. Um Kompromisse zwischen zwei Zielen zu erzielen, wurden moderne Algorithmen zur Mehrzieloptimierung implementiert: Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm, Generalized Differential Evolution, Improved Multi-objective Particle Swarm Optimization (OMOPSO), and Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO). Die Optimierungsalgorithmen ermöglichten eine effiziente Produktionsplanung mit einer Verringerung der Produktionsdauer um bis zu 12 % und einer Verringerung der Leerlaufzeit der Öfen um 26 % auf der Grundlage von Daten aus unterschiedlichen Produktionsprozessen. Der Leistungsvergleich zeigte signifikante Unterschiede zwischen diesen Mehrziel-Optimierungsalgorithmen, wobei NSGA-II am besten und OMOPSO und SMPSO am schlechtesten abschnitten. Die Gärung ist ein wichtiger Verarbeitungsschritt, der zur Qualität des Endprodukts beiträgt, indem der Geschmack und die Textur des Brotes positiv beeinflusst werden kann. Die Dauer der Gärung ist jedoch aufgrund der komplexen Interaktion von mehreren Größen abhängig wie der Hefezustand, der Temperatur in der Gärkammer und der chemischen Zusammensetzung des Mehls. Aufgrund der Variabilität der Gärzeit kann jedoch ein Produktionsplan, der auf die kürzeste Produktionszeit optimiert ist, sehr ineffizient sein. Die Berechnungsergebnisse zeigen, dass die Pläne mit der kürzesten und nahezu kürzesten Produktionsdauer eine erhebliche (bis zu 18 %) Erhöhung der Produktionsdauer aufgrund der Abweichung der Gärzeit von der erwarteten Dauer aufweisen. In dieser Arbeit wird eine Methode zur Entwicklung einer robusten Produktionsplanung vorgeschlagen, die Veränderungen in den Gärzeiten berücksichtigt, so dass selbst bei einer extremen Abweichung der Gärzeit die Schwankung der Produktionsdauer minimal ist. Die experimentellen Ergebnisse für einen Produktionsprozess ergaben einen robusten Produktionsplan, der nur 5 Minuten länger ist als die kürzeste Produktionsdauer, aber nur 21 Minuten in der Produktionsdauer schwankt, wenn die Gärzeit von -10 % bis +10 % der ermittelten Gärzeit variiert. In dieser Studie wird ein Vorgehen für kleine und mittlere Bäckereien vorgeschlagen, um ihre Produktionseffizienz in drei Schritten zu verbessern: Erfassung von Produktionsdaten, Simulation von Produktionsplänen mit dem hybrid No-Wait Flow Shop Modell und Ausführung der Optimierung. Für die Einzieloptimierung wird der mPSO-Algorithmus und für die Mehrzieloptimierung NSGA-II-Algorithmus empfohlen. Auf der Grundlage realer Bäckereiproduktionsdaten zeigten die Ergebnisse, dass die in den Bäckereien verwendeten Pläne ineffizient waren und mit Hilfe eines effizienten Optimierungsalgorithmus in einer angemessenen Rechenzeit optimiert werden konnten. Die Umsetzung eines solchen Vorgehens in kleinen und mittelgroßen Bäckereibetrieben trägt dazu bei effiziente und robuste Produktionspläne zu erstellen und somit die Wettbewerbsfähigkeit dieser Bäckereien zu erhöhen

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

Optimization of Aluminium Profiles Production Planning

Dissertação de Mestrado Métodos de Apoio à Decisão EmpresarialNo atual ambiente empresarial, a crescente competitividade impulsiona as empresas a implementar estratégias de otimização para assegurar ou melhorar a sua posição no mercado. Nesse sentido é crucial tomar as melhores decisões do ponto de vista do planeamento da produção. A produção de perfis de alumínio apresenta vários desafios ao responsável da produção. Este trabalho aborda um caso real de uma empresa do setor metalúrgico, cuja área de negocio é o desenvolvimento e produção de perfis de alumínio para aplicação em diversas áreas, como obras de engenharia, arquitetura e industria em geral. O objetivo é minimizar o desperdício ocorrido na produção, designado de sucata, através da minimização dos tempos de preparação, que ocorrem quando a matriz é trocada, respeitando os prazos de entrega do produto e garantindo a qualidade. Este estudo de caso centra-se num problema de sequenciamento de flow shop que envolve tempos de preparação dependentes da sequência decorrentes da necessidade de alterar as ferramentas usadas no processo de extrusão de alumínio. Um modelo de programação inteira mista é desenvolvido e implementado para responder ao desafio da empresa. O problema será formulado na linguagem de modelagem AMPL e seria usado o solver Gurobi para resolver instâncias reais extraídas dos dados providenciados pela empresa. Os resultados obtidos com o modelo desenvolvido são comparados com a regra de despacho FIFO, em termos da soma dos tempos de preparação (e consequentemente do número de trocas de matrizes), bem como o cumprimento do prazo de entrega. Além disso, embora o procedimento atual da empresa não seja conhecido, os resultados obtidos são comparados com a média de trocas de matriz obtidas a partir dos dados históricos de produção fornecidos pela empresa. Conclui-se que, utilizando as soluções obtidas com o modelo desenvolvido, a quantidade de sucata gerada durante o processo de produção _e minimizada, uma vez que, o modelo garante a minimização da soma dos tempos de preparação e minimiza o número de vezes que ocorrem trocas de matriz.In the current business world, the increasing competitiveness forces companies to adopt optimization strategies to ensure or improve their market position. It is crucial to take the best decisions from the production planning point of view. The production of aluminium pro les poses several challenges to the production manager. This work addresses a real case of a company that operates in the aluminium market, whose core business is the development and production of aluminium pro les for application in several areas, such as, engineering, architecture and industry works in general. The aim is to minimize production waste, commonly known as scrap, through minimization of setup times, that occur when the die is changed, while respecting product delivery times and maintaining quality. This case study focuses on a scheduling ow shop problem involving sequence-dependent setup times arising from the need to change the tools used in the process of aluminium extrusion. A mixed integer programming model is developed and implemented for answering the company's challenge. The problem is formulated the AMPL modeling language and the Gurobi solver is used to solve real instances extracted from data provided by the company. The results obtained with the developed model are compared to the dispatching rule FIFO, in terms the sum of the setup times (and consequently the number of exchanges of dies), as well as the ful llment of the deadline. Furthermore, although the company's current procedure is not known, the results obtained are compared with the average of die changes obtained from the historical production data provided by the company. It is observed that by using the solutions obtained with the developed model, the quantity of scrap that is generated during the production process is minimized, since the model guarantees the minimization of the sum of the setup times, therefore minimizes the number of times there are die changes.N/