A New Approach to Blending and Loading Problem of Molten Aluminum

The problems of blending electrolyzer and multi-constraint optimization of electrolytic aluminum scheduling in the electrolytic aluminum production process were addressed. Based on a mathematical model analysis, a novel hybrid optimization algorithm is proposed for optimization of blending together the molten aluminum in different electrolytic cells. An affinity degree function was designed to represent the path of aluminum scheduling. The mutation operators were designed to implement the transformation of electrolyzer combination and change the route of loading. A typical optimization example from an aluminum plant in northwest China is given in this paper, the results of which demonstrate the effectiveness of the proposed method

A Metaheuristic Compendium for Scheduling Problems

The flexible job shop scheduling problem (FJSSP) is a difficult and complex problem, proved to be NP-hard, in manufacturing environments, because it has to assign each operation to the appropriate machine besides sequencing operations on machines. Due to that complexity, metaheuristics became the best choice to solve in practice this kind of problem. Therefore, the aim of this paper is to offer a reliable compendium in order to cover a wide algorithmic spectrum of different techniques. Further, a study of their accuracy and computational effort is carried out in order to achieve a behavior comparison. This paper shows different algorithmic trends that can be observed through this analysis.Eje: XVIII Workshop de Agentes y Sistemas Inteligentes (WASI).Red de Universidades con Carreras en Informática (RedUNCI

Design and development of CSP techniques for finding robust solutions in job-shop scheduling problems with Operators

[ES] Se desarrolla una técnica CSP para buscar soluciones robustas en el problema job-shop scheduling. La técnica esta desarrollada en tres pasos. El primer paso resuelve el problema sin tener en cuenta operadores. El segundo paso introduce las restricciones de los operadores y obtiene soluciones teniendo en cuenta el makespan y la robustez. En el tercer paso se mejora la robustez redistribuyendo los buffers. Para probar las robustez de las soluciones obtenidas se aplican incidencias virtuales en las soluciones.[EN] A CSP technique have been developed for finding robust solutions in job-shop scheduling problems with operators. The technique is developed in three steps. The first step solve the problem without operators minimizing the makespan. The second step introduce the operator constraints and give solutions take into account makespan and robustness. The third step improve the robustness redistributing the buffer. Some virtual incidences are created and to check the robustness of the solutions.Escamilla Fuster, J. (2012). Design and development of CSP techniques for finding robust solutions in job-shop scheduling problems with Operators. http://hdl.handle.net/10251/18029Archivo delegad

Neighborhood structures for scheduling problems with additional resource types

The job shop scheduling is a challenging problem that has interested to researchers in the fields of Artificial Intelligence and Metaheuristics over the last decades. In this project, we face the job shop scheduling problem with an additional resource type (operators). This is a variant of the problem, which has been proposed recently in the literature. We start from a genetic algorithm that has been proposed previously to solve this problem and improve it in two different ways. Firstly, we introduce a modification in the schedule generation scheme in order to control the time of inactivity of the machines. Secondly we define a number of neighbourhood structures that are then incorporated in a memetic algorithm. In order to evaluate the proposed strategies, we have conducted an experimental study across a benchmark derived from a set of hard instances of the classic job shop problem

Ordonnancement d’ateliers en présence d’opérateurs

La théorie de l’ordonnancement a, depuis son avènement, suscité un grand intérêt de la part de chercheurs, de scientifiques, mais aussi d’industriels. Ceci est dû à la grande variété de problèmes réels pouvant être modélisés sous forme de problèmes d’ordonnancement. En effet, ce domaine peut trouver des applications aussi bien en gestion d’horaires qu’en informatique, ou encore en environnement de production. Les études relativement récentes dans le domaine ont vu l’introduction du paramètre humain et sa considération dans la prise de décision portant sur les ressources matérielles d’un problème d’ordonnancement. La présente thèse traite des problèmes d’ordonnancement d’ateliers avec opérateurs. Dans lesdits ateliers, des tâches devront être exécutées par plusieurs machines selon un ordre qui dépend du type d’atelier. Pour ce faire, lesdites tâches utilisent simultanément un opérateur et une machine. Le nombre d’opérateurs ainsi que leurs placements, i.e. leurs affectations aux machines, dépendra du modèle d’affectation choisi. Tout d’abord, nous considérons un problème de flow shop de permutation avec temps de réglages. Nous supposons que le nombre d’opérateurs est égal au nombre de machines et qu’ils s’occupent des opérations de réglage. Nous utilisons pour la résolution la métaheuristique Migrating Birds Optimization. Nous apportons des améliorations à l’algorithme de base et en présentons quatre versions, ce qui nous permet d’obtenir des résultats de relativement bonne qualité avec des configurations différentes qui apportent de la flexibilité lors de la prise de décision. Par la suite, nous étudions des problèmes où le nombre d’opérateurs est inférieur au nombre de machines. Nous étudions trois types d’ateliers : les flow shops, les job shops et les open shops. Nous commençerons d’abord par l’étude de complexité de nos problèmes. Nous présentons d’abord des cas résolubles en temps polynomial et exhibons les méthodes permettant de les résoudre. Pour les cas difficiles, nous proposons des méthodes de résolution ainsi que des bornes inférieures. Les résultats montrent que les méthodes proposées donnent de bons résultats, souvent proches des bornes théoriques. Since its advent, scheduling theory has generated great interest amidst researchers, scientists but also industrialists. This is due to the great diversity of real problems that can be modeled as scheduling problems. Indeed, this field can find applications in timetabling, computer science but also in production systems. Recent studies in the field have introduced the human resources and considered them in decision making processes involving the material resources of scheduling problems. This thesis deals with scheduling shop problems with operators. In the aforementioned shops, tasks are to be processed according to orderings that depend on the type of shop. In order to do so, the tasks need simultaneously an operator and a machine. The number of operators and their positions in the shop, i.e. their assignements to machines, depends on the chosen assignment mode. First, we consider a permutation flow shop problem with setup times. We assume that the number of operators is equal to the number of machines and that they handle setup operations. We use the metaheuristic called the Migrating Birds Optimization to solve this problem. We improve the basic algorithm and present four versions, which allows us to obtain results of good quality with different structures, which provides flexibility in decision making. Next, we study problems where the number of operators is less than the number of machines. We study three types of shops : flow shops, job shops and open shops.We first start by studying the complexity of our problems. Then we present well-solvable cases as well as their solution methods. For some N P-hard cases, we propose solution methods and a lower bound. The results show that the proposed methods provide good results, often close to the theoretical lower bounds

An on-demand fixture manufacturing cell for mass customisation production systems.

Master of Science in Engineering. University of KwaZulu-Natal, Durban, 2017.Increased demand for customised products has given rise to the research of mass customisation production systems. Customised products exhibit geometric differences that render the use of standard fixtures impractical. Fixtures must be configured or custom-manufactured according to the unique requirements of each product. Reconfigurable modular fixtures have emerged as a cost-effective solution to this problem. Customised fixtures must be made available to a mass customisation production system as rapidly as parts are manufactured. Scheduling the creation/modification of these fixtures must now be treated together with the production scheduling of parts on machines. Scheduling and optimisation of such a problem in this context was found to be a unique avenue of research. An on-demand Fixture Manufacturing Cell (FxMC) that resides within a mass customisation production system was developed. This allowed fixtures to be created or reconfigured on-demand in a cellular manufacturing environment, according to the scheduling of the customised parts to be processed. The concept required the research and development of such a cell, together with the optimisation modelling and simulation of this cell in an appropriate manufacturing environment. The research included the conceptualisation of a fixture manufacturing cell in a mass customisation production system. A proof-of-concept of the cell was assembled and automated in the laboratory. A three-stage optimisation method was developed to model and optimise the scheduling of the cell in the manufacturing environment. This included clustering of parts to fixtures; optimal scheduling of those parts on those fixtures; and a Mixed Integer Linear Programming (MILP) model to optimally synchronise the fixture manufacturing cell with the part processing cell. A heuristic was developed to solve the MILP problem much faster and for much larger problem sizes – producing good, feasible solutions. These problems were modelled and tested in MATLAB®. The cell was simulated and tested in AnyLogic®. The research topic is beneficial to mass customisation production systems, where the use of reconfigurable modular fixtures in the manufacturing process cannot be optimised with conventional scheduling approaches. The results showed that the model optimally minimised the total idle time of the production schedule; the heuristic also provided good, feasible solutions to those problems. The concept of the on-demand fixture manufacturing cell was found to be capable of facilitating the manufacture of customised products

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI