An efficient discrete artificial bee colony algorithm for the blocking flow shop problem with total flowtime minimization

This paper presents a high performing Discrete Artificial Bee Colony algorithm for the blocking flow shop problem with flow time criterion. To develop the proposed algorithm, we considered four strategies for the food source phase and two strategies for each of the three remaining phases (employed bees, onlookers and scouts). One of the strategies tested in the food source phase and one implemented in the employed bees phase are new. Both have been proved to be very effective for the problem at hand. The initialization scheme named HPF2(¿, µ) in particular, which is used to construct the initial food sources, is shown in the computational evaluation to be one of the main procedures that allow the DABC_RCT to obtain good solutions for this problem. To find the best configuration of the algorithm, we used design of experiments (DOE). This technique has been used extensively in the literature to calibrate the parameters of the algorithms but not to select its configuration. Comparing it with other algorithms proposed for this problem in the literature demonstrates the effectiveness and superiority of the DABC_RCTPeer ReviewedPostprint (author’s final draft

The hybrid flexible flowshop with transportation times

This paper presents the hybrid, flexible flowshop problem with transportation times between stages, which is an extension of an existing scheduling problem that is well-studied in the literature. We explore different models for the problem with Constraint Programming, MILP, and local search, and compare them on generated benchmark problems that reflect the problem of the industrial partner. We then study two different factory layout design problems, and use the optimization tool to understand the impact of the design choices on the solution quality

Approximate Methods For Solving Flowshop Problems

The flow shop scheduling problem is a classical combinatorial problem being studied for years. The focus of this research is to study two variants of the flow shop scheduling problem in order to minimize makespan by scheduling n jobs on m machines. A solution approach is developed for the modified flow shop problem with due dates and release times. This algorithm is an attempt to contribute to the limited literature for the problem. Another tabu search-based solution approach is developed to solve the classical flow shop scheduling problem. This meta-heuristic (called 3XTS) allows an efficient search of the neighboring solutions leading to a fast solution procedure. Several control parameters affecting the quality of the algorithm are experimentally tested, and certain rules are established for different problem instances. The 3XTS is compared to another tabu search method (that seems to be a champion) in terms of solution quality and computation time

Managing distributed flexible manufacturing systems

Per molti anni la ricerca scientifica si è concentrata sui diversi aspetti di gestione dei sistemi manifatturieri, dall’ottimizzazione dei singoli processi produttivi, fino alla gestione delle più complesse imprese virtuali. Tuttavia molti aspetti inerenti il coordinamento e il controllo, ancora presentano problematiche rilevanti in ambito industriale e temi di ricerca aperti. L’applicazione di tecnologie avanzate e di strumenti informatici evoluti non riesce da sola a garantire il successo nelle funzioni di controllo e di integrazione. Al fine di ottenere un alto grado di efficienza, è necessario supportare tali tecnologie e strumenti con dei modelli che siano in grado di rappresentare le funzionalità e i processi dei sistemi manifatturieri, e consentano di prevederne e gestirne l’evoluzione. Ne emerge l’esigenza di politiche di controllo e di gestio ne distribuite, che favoriscano l’auto-organizzazione e la cooperazione nei sistemi manifatturieri. I sistemi manifatturieri flessibili distribuiti (DFMS), in risposta a tale esigenza, sono sistemi di produzione dinamici in grado di garantire una risposta in tempo reale alla allocazione ottima delle risorse, e organizzare efficientemente le lavorazioni dei prodotti. In questa tesi viene proposta una modellizzazione a livelli per tali sistemi. Secondo tale rappresentazione un DFMS può essere visto come un grafo strutturato su più livelli, tale che: i vertici del grafo rappresentano le risorse interagenti nel sistema; ogni nodo di un livello rappresenta a sua volta un livello successivo. Partendo da questa rappresentazione, sono stati quindi sviluppati due modelli per lo studio dell’allocazione ottima delle risorse (task mapping) e per l’organizzazione di lavorazioni (task scheduling) che richiedono l’uso simultaneo di risorse condivise nel sistema. Il task mapping problem consiste nella suddivisione bilanciata di un certo insieme di lavorazioni tra le risorse del sistema. In questa tesi si è studiato il caso in cui le lavorazioni sono omogenee, non presentano vincoli di precedenza, ma necessitano di un certo volume di comunicazione tra le risorse cui sono assegnate per garantirne il coordinamento, incidendo in tal senso sulla complessità di gestione. L’analisi critica dei modelli che sono tipicamente usati in letteratura per rappresentare tale problema, ne hanno posto in evidenza l’inadeguatezza. Attraverso alcuni risultati teorici si è quindi dimostrato come il problema possa ricondursi ad un hypergraph partitioning problem. Studiando la formulazione matematica di tali problemi, e limitandosi al caso di due risorse produttive, si è infine giunti alla determinazione di una buona approssimazione sulla soluzione ottima. Il problema di sequenziamento delle lavorazioni (task scheduling) che richiedono l’uso simultaneo di risorse condivise è stato trattato nel caso specifico di celle robotizzate. E’ stata quindi dimostrata l’NP-completezza di questo problema ed è stata progettata una euristica di soluzione, validandone i risultati in diversi scenari produttivi.For several years, research has focused on several aspects of manufacturing, from the individual processes towards the management of virtual enterprises, but several aspects, like coordination and control, still have relevant problems in industry and remain challenging areas of research. The application of advanced technologies and informational tools by itself does not guarantee the success of control and integration applications. In order to get a high degree of integration and efficiency, it is necessary to match the technologies and tools with models that describe the existing knowledge and functionality in the system and allow the correct understanding of its behaviour. In a global and wide market competition, the manufacturing systems present requirements that lead to distributed, self-organised, co-operative and heterogeneous control applications. A Distributed Flexible Manufacturing System (DFMS) is a goal-driven and data-directed dynamic system which is designed to provide an effective operation sequence for the products to fulfil the production goals, to meet real-time requirements and to optimally allocate resources. In this work first a layered approach for modeling such production systems is proposed. According to that representation, a DFMS may be seen as multi-layer resource-graph such that: vertices on a layer represent interacting resources; a layer at level l is represented by a node in the layer at level (l-1). Then two models are developed concerning with two relevant managerial issues in DFMS, the task mapping problem and the task scheduling with multiple shared resources problem. The task mapping problem concerns with the balanced partition of a given set of jobs and the assignment of the parts to the resources of the manufacturing system. We study the case in which the jobs are quite homogeneous, do not have precedence constraints, but need some communications to be coordinated. So, jobs assignment to different parts causes a relevant communication effort between those parts, increasing the managerial complexity. We show that the standard models usually used to formal represent such a problem are wrong. Through some graph theoretical results we relate the problem to the well-known hypergraph partitioning problem and briefly survey the best techniques to solve the problem. A new formulation of the problem is then presented. Some considerations on an improved version of the formulation permit the computation of a good Lower Bound on the optimal solution in the case of the hypergraph bisection. The task scheduling with multiple shared resources problem is addressed for a robotic cell. We study the general problem of sequencing multiple jobs, where each job consists of multiple ordered tasks and tasks execution requires simultaneous usage of several resources. NP-completeness results are given. A heuristic with a guarantee approximation result is designed and evaluated

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Étude du problème de job shop avec un convoyeur

La densité des échanges commerciaux, ainsi que l'intensification de la concurrence qui a suivi, a conduit les entreprises à rationaliser leurs activités, particulièrement celles nécessitant des activités d'ordonnancement. La théorie de l'ordonnancement est une discipline bien établie de l'optimisation combinatoire. Son champ d'investigation concerne les problèmes d'allocation, dans le temps, d'un ensemble limité de ressources par un ensemble de tâches, afin d'optimiser un ou plusieurs critères donnés. Sa popularité vient du fait qu'une multitude de situations peut être ramenée à cette problématique d'ordonnancement. Cela est dû, en grande partie, à la richesse de l'interprétation que peuvent avoir les termes ressources et tâches. Nous pouvons citer, entre autres, des applications dans l'industrie (réalisation de produits sur des machines), la santé (confection d'horaires), l'informatique (exécution de processus). Dans ce mémoire de maîtrise, nous nous intéressons spécifiquement aux problèmes d'ordonnancement d'ateliers de production. Ainsi, notre étude porte sur l'ordonnancement de n tâches (jobs) sur m ressources (machines) dans un environnement de type job shop. Dans le modèle de job shop, chaque tâche doit passer sur l'ensemble des machines, à chaque fois pendant un temps connu à l'avance, et selon également un ordre donné. Le critère, que nous avons choisi pour évaluer la qualité d'une solution, est celui du makespan (la durée totale d'accomplissement des « tâches). Pour rester proche de la réalité industrielle, notre modèle incorpore un convoyeur chargé de transporter les tâches semi-finies d'une machine à une autre. Ce modèle peut être illustré par l'exemple d'une entreprise d'assemblage d'ordinateurs. Les machines assemblent divers éléments (cartes mères, disques durs, barrettes mémoires, etc) dans un boîtier. Un convoyeur déplace le boîtier entre les différentes machines. Suivant les spécifications de chaque ordinateur, chaque boîtier suit un chemin particulier. En effet, si un client souhaite acheter un boîtier contenant uniquement la carte-mère et l'alimentation, ce boîtier ne passera que sur deux machines. Nous nous sommes restreint au problème de job shop à deux machines et un seul convoyeur. Notons que nous supposons que les deux machines possèdent des espaces de stockage de taille illimitée pour recevoir les travaux semi-finis. Notre but était au départ de trouver un algorithme polynomial pour résoudre ce problème. Or, il s'est avéré que même avec ce modèle restreint et simplifié, le problème est NP-difficile. Pour le résoudre, nous nous sommes alors tournés vers l'approche heuristique. Néanmoins, nous avons pu trouver des cas particuliers où ce problème est résoluble en un temps polynomial. Notre démarche a été d'abord d'introduire brièvement les problèmes de la théorie de l'ordonnancement ainsi que quelques concepts de la NP-complétude, avant d'aborder les différentes approches algorithmiques de résolution des problèmes d'ordonnancement. Dans une seconde étape, la littérature relative à cette problématique a été passée en revue. Ensuite, nous avons décrit plus en détail notre modèle de job shop ainsi que son fonctionnement. Nous avons discuté de l'influence du convoyeur sur la minimisation du critère du makespan. Nous avons également proposé une borne inférieure pour ce même critère. Enfin, nous avons discuté et proposé deux approches de résolution approchée. La première est constructive : trois algorithmes, basés sur des règles de priorité, ont été conçus. Les deux premières règles sont fondées sur l'appariement des travaux et la troisième est la règle bien connue de Jackson que nous avons modifiée ; ces règles ont une complexité temporelle en O(nlogn). La seconde approche de résolution est itérative: l'algorithme de recherche avec tabous a été notre choix. Cette méthode étend à n travaux la méthode classique de résolution graphique à deux travaux. Finalement, nous avons entrepris une étude comparative de l'ensemble des algorithmes de résolution proposés. Les deux approches de résolution ont été simulées par un programme Java sur des instances générées de manière aléatoire à partir d'une distribution uniforme. Ces instances sont de tailles n ? 10, 20, 50 et 200. Les temps d'exécution et de transport sont compris entre 0 et 50 unités de temps. La borne inférieure a été utilisée pour évaluer la qualité des solutions générées par chacune de ces heuristiques. Cette étude a montré que, parmi les algorithmes basés sur les règles de priorité, celui de Jackson donne des solutions de meilleure qualité. L'algorithme de recherche avec tabous donne, en moyenne, de meilleures solutions que les algorithmes basés sur les règles de priorité. Toutefois, les temps de calculs de cette approche sont de loin plus importants que ceux générés par les règles de priorité, surtout lorsque la taille des problèmes devient de plus en plus grande. Nous concluons notre travail par la suggestion de nouvelles pistes à explorer pour des recherches futures

Energy-aware coordination of machine scheduling and support device recharging in production systems

Electricity generation from renewable energy sources is crucial for achieving climate targets, including greenhouse gas neutrality. Germany has made significant progress in increasing renewable energy generation. However, feed-in management actions have led to losses of renewable electricity in the past years, primarily from wind energy. These actions aim to maintain grid stability but result in excess renewable energy that goes unused. The lost electricity could have powered a multitude of households and saved CO2 emissions. Moreover, feed-in management actions incurred compensation claims of around 807 million Euros in 2021. Wind-abundant regions like Schleswig-Holstein are particularly affected by these actions, resulting in substantial losses of renewable electricity production. Expanding the power grid infrastructure is a costly and time-consuming solution to avoid feed-in management actions. An alternative approach is to increase local electricity consumption during peak renewable generation periods, which can help balance electricity supply and demand and reduce feed-in management actions. The dissertation focuses on energy-aware manufacturing decision-making, exploring ways to counteract feed-in management actions by increasing local industrial consumption during renewable generation peaks. The research proposes to guide production management decisions, synchronizing a company's energy consumption profile with renewable energy availability for more environmentally friendly production and improved grid stability

Programmation linéaire en nombres entiers pour l'ordonnancement cyclique sous contraintes de ressources

Un problème d'ordonnancement cyclique consiste à ordonner dans le temps l'exécution répétitive d'un ensemble d'opérations liées par des contraintes de précédence, en utilisant un nombre limité de ressources. Ces problèmes ont des applications immédiates dans les systèmes de production ou en informatique parallèle. Particulièrement, ils permettent de modéliser l'ensemble des contraintes de précédence et de ressource à prendre en compte pour l'ordonnancement d'instructions dans les processeurs de type VLIW (Very Long Instruction Word). Dans ce cas, une opération représente une instance d'une instruction dans un programme. L'ordonnancement d'instructions de boucles internes est connu sous le nom de pipeline logiciel. Le pipeline logiciel désigne une méthode efficace pour l'optimisation de boucles qui permet la réalisation en parallèle des opérations des différentes itérations de la boucle. Dans cette thèse, nous nous intéressons principalement au problème d'ordonnancement périodique qui est un cas particulier de l'ordonnancement cyclique et qui est également la base du pipeline logiciel. Le terme ordonnancement modulo désigne un ordonnancement périodique tel que l'allocation de ressources pour une opération donnée n'est pas modifiée d'une itération sur l'autre. Pour résoudre le problème, nous nous intéressons aux formulations de programmation linéaire en nombres entiers, et notamment à la résolution du problème par des techniques de séparation, évaluation, génération de colonnes, relaxation lagrangienne et des méthodes hybrides. En particulier, nous proposons des nouvelles formulations basées sur des variables binaires représentant l'exécution d'ensembles d'instructions en parallèle. Enfin, les méthodes développées ont été validées sur des jeux d'instances industrielles pour des processeurs de type VLIW.The resource-constrained modulo scheduling problem (RCMSP) is a general periodic cyclic scheduling problem, abstracted from the problem solved by compilers when optimizing inner loops at instruction level for very long instruction word parallel processors. Since solving the instruction scheduling problem at compilation phase in less time critical than for real time scheduling, integer linear programming (ILP) is a relevant technique for the RCMSP. In this work, we are interested in the methods based on the integer linear programming for the RCMSP. At first, we present a study of the two classic integer linear formulation for the RCMSP. A theoretical evidence of the equivalence between the classic formulations is shown in terms of linear programming (LP) relaxation. Secondly, based on the ILP formulations for the RCMSP, stronger formulations for the RCMSP derived from Dantzig-Wolfe decomposition are presented. In these formulations, the number of variables can be huge, for this reason, we proposed a column generation scheme to solve their LP relaxations. We propose also the heuristics methods based on the Lagragian relaxation and decomposed software pipelining. The heuristic methods search the transformation of the classic integer linear programming for the RCMSP for the performance improvement in the time for the search of solutions. All formulations are compared experimentally on problem instances generated from real data issued from the STMicroelectronics ST200 VLIW processor family