Ordonnancement et contrôle avancé des procédés en fabrication de semi-conducteurs.

Dans cette thèse, nous avons examiné différentes possibilités d'intégration des décisions d'ordonnancement avec des informations provenant de systèmes avancés des contrôles des procédés dans la fabrication de semi-conducteurs. Nous avons développé des idées d'intégration et défini des nouveaux problèmes d'ordonnancement originales : Problème d'ordonnancement avec des contraintes de temps (PTC) et problème d'ordonnancement avec l'état de santé des équipement (PEHF). PTC et PEHF ont des fonctions objectives multicritères.PTC est un problème d'ordonnancement des familles de jobs sur des machines parallèles non identiques en tenant compte des temps de setup et des contraintes de temps. Les machines non identiques signifient que toutes les machines ne peuvent pas traités (qualifiés) tous les types de familles d'emplois. Les contraintes de temps nommés aussi Thresholds sont inspirées des besoins de l'APC. Elle est liée à l'alimentation régulière des boucles de contrôle de l'APC. L'objectif est de minimiser la somme des dates de fin et les pertes de qualification des machines lorsqu'une famille de jobs n'est pas ordonnancée sur la machine donnée avant un seuil de temps donné.D'autre part, PEHF est une extension de PTC. Il consiste d'intégrer les indices de santé des équipements (EHF). EHF est un indicateur associé à l'équipement qui donne l'état de la. L'objectif est d'ordonnancer des tâches de familles de jobs différents sur les machines tout en minimisant la somme des temps d'achèvement, les pertes de qualification de la machine et d'optimiser un rendement attendu. Ce rendement est défini comme une fonction d'EDH et de la criticité de jobs considérés.In this thesis, we discussed various possibilities of integrating scheduling decisions with information and constraints from Advanced Process Control (APC) systems in semiconductor Manufacturing. In this context, important questions were opened regarding the benefits of integrating scheduling and APC. An overview on processes, scheduling and Advanced Process Control in semiconductor manufacturing was done, where a description of semiconductor manufacturing processes is given. Two of the proposed problems that result from integrating bith systems were studied and analyzed, they are :Problem of Scheduling with Time Constraints (PTC) and Problem of Scheduling with Equipement health Factor (PEHF). PTC and PEHF have multicriteria objective functions.PTC aims at scheduling job in families on non-identical parallel machines with setup times and time constraints.Non-identical machines mean that not all miachines can (are qualified to) process all types of job families. Time constraints are inspired from APC needs, for which APC control loops must be regularly fed with information from metrology operations (inspection) within a time interval (threshold). The objective is to schedule job families on machines while minimizing the sum of completion times and the losses in machine qualifications.Moreover, PEHF was defined which is an extension of PTC where scheduling takes into account the equipement Health Factors (EHF). EHF is an indicator on the state of a machine. Scheduling is now done by considering a yield resulting from an assignment of a job to a machine and this yield is defined as a function of machine state and job state.ST ETIENNE-ENS des Mines (422182304) / SudocGARDANNE-Centre microélec. (130412301) / SudocSudocFranceF

Scheduling job families on non identical parallel machines under Run-To-Run control constraints

International audienceNew challenges are arising when solving scheduling problems in semiconductor manufacturing plants (fabs) with Advanced Process Control (APC) constraints. In particular, a Run-To-Run (R2R) control loop for a given product on a machine requires to regularly collect data for the product on the machine. This paper aims at introducing and modeling a new scheduling problem in which there is a time constraint on jobs of the same product, i.e. the time interval between two consecutive jobs of the same product should be smaller than a given threshold. Two Mixed Integer Linear Programming models are presented for scheduling jobs on non-identical parallel machines with setup times

A new CP-approach for a parallel machine scheduling problem with time constraints on machine qualifications

International audienceThis paper considers the scheduling of job families on parallel machines with time constraints on machine qualifications. In this problem, each job belongs to a family and a family can only be executed on a subset of qualified machines. In addition, machines can lose their qualifications during the schedule. Indeed, if no job of a family is scheduled on a machine during a given amount of time, the machine loses its qualification for this family. The goal is to minimize the sum of job completion times, i.e. the flow time, while maximizing the number of qualifications at the end of the schedule. The paper presents a new Constraint Programming (CP) model taking more advantages of the CP feature to model machine disqualifications. This model is compared with two existing models: an Integer Linear Programming (ILP) model and a Constraint Programming model. The experiments show that the new CP model outperforms the other model when the priority is given to the number of disqualifications objective. Furthermore, it is competitive with the other model when the flow time objective is prioritized

Bi-objective optimization of the tactical allocation of job types to machines: mathematical modeling, theoretical analysis, and numerical tests

We introduce a tactical resource allocation model for a large aerospace engine system manufacturer aimed at long-term production planning. Our model identifies the routings a product takes through the factory, and which machines should be qualified for a balanced resource loading, to reduce product lead times. We prove some important mathematical properties of the model that are used to develop a heuristic providing a good initial feasible solution. We propose a tailored approach for our class of problems combining two well-known criterion space search algorithms, the bi-directional ε-constraint method and the augmented weighted Tchebycheff method. A computational investigation comparing solution times for several solution methods is presented for 60 numerical\ua0instances

Maintenance activities scheduling under competencies constraints.

International audienceCompetencies management in the industry is one of the most important keys in order to obtain good performance with production means. Especially in maintenance services field where the different practical knowledges or skills are their working tools. We propose here a methodology, which compares the human resource with parallel machine. As human resource competence levels of each are all differents, they are considered like unrelated parallel machines. Our aim is to assign tasks to the adequate resources by minimizing time treatment for each task and the makespan

Mathematical Optimization of the Tactical Allocation of Machining Resources in Aerospace Industry

In the aerospace industry, efficient management of machining capacity is crucial to meet the required service levels to customers (which includes, measures of quality and production lead-times) and to maintain control of the tied-up working capital. We introduce a new multi-item, multi-level capacitated planning model with a medium-to-long term planning horizon. The model can be used by most companies having functional workshops where costly and/or time- and resource demanding preparations (or qualifications) are required each time a product needs to be (re)allocated to a machining resource. Our goal is to identify possible product routings through the factory which minimizes the maximum excess resource loading above a given loading threshold, while incurring as low qualification costs as possible. In Paper I (Bi-objective optimization of the tactical allocation of jobtypes to machines), we propose a new bi-objective mathematical optimization model for the Tactical Resource Allocation Problem (TRAP). We highlight some of the mathematical properties of the TRAP which are utilized to enhance the solution process. Another contribution is a modified version of the bi-directional $\epsilon$ -constraint method especially tailored for our problem. We perform numerical tests on industrial test cases generated for our class of problem which indicates computational superiority of our method over conventional solution approaches. In Paper II (Robust optimization of a bi-objective tactical resource allocation problem with uncertain qualification costs), we address the uncertainty in the coefficients of one of the objective functions considered in the bi-objective TRAP. We propose a new bi-objective robust efficiency concept and highlight its benefits over existing robust efficiency concepts. We also suggest a solution approach for identifying all the relevant robust efficient (RE) solutions. Our proposed approach is significantly faster than an existing approach for robust bi-objective optimization problems

A Decision Support System for Effective Scheduling in an F-16 Pilot Training Squadron

Scheduling of flights for a flight training squadron involves the coordination of time and resources in a dynamic environment. The generation of a daily flight schedule (DFS) requires the proper coordination of resources within established time windows. This research provides a decision support tool to assist in the generation of the DFS. Three different priority rules are investigated for determining an initial ordering of flights and a shifting bottleneck heuristic is used to establish a candidate DFS. A user interface allows a scheduler to interact with the decision support tool during the DFS generation process. Furthermore, the decision support tool provides the capability to produce a weekly schedule for short-term planning purposes as well as the entire flight training program schedule for long- term planning purposes

A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce

Nowadays many companies have available large amounts of raw, unstructured data. Among Big Data enabling technologies, a central place is held by the MapReduce framework and, in particular, by its open source implementation, Apache Hadoop. For cost effectiveness considerations, a common approach entails sharing server clusters among multiple users. The underlying infrastructure should provide every user with a fair share of computational resources, ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In this paper we consider two mathematical programming problems that model the optimal allocation of computational resources in a Hadoop 2.x cluster with the aim to develop new capacity allocation techniques that guarantee better performance in shared data centers. Our goal is to get a substantial reduction of power consumption while respecting the deadlines stated in the SLAs and avoiding penalties associated with job rejections. The core of this approach is a distributed algorithm for runtime capacity allocation, based on Game Theory models and techniques, that mimics the MapReduce dynamics by means of interacting players, namely the central Resource Manager and Class Managers

A biased-randomized discrete-event algorithm for the hybrid flow shop problem with time dependencies and priority constraints

Based on a real-world application in the semiconductor industry, this article models and discusses a hybrid flow shop problem with time dependencies and priority constraints. The analyzed problem considers a production where a large number of heterogeneous jobs are processed by a number of machines. The route that each job has to follow depends upon its type, and, in addition, some machines require that a number of jobs are combined in batches before starting their processing. The hybrid flow model is also subject to a global priority rule and a “same setup” rule. The primary goal of this study was to find a solution set (permutation of jobs) that minimizes the production makespan. While simulation models are frequently employed to model these time-dependent flow shop systems, an optimization component is needed in order to generate high-quality solution sets. In this study, a novel algorithm is proposed to deal with the complexity of the underlying system. Our algorithm combines biased-randomization techniques with a discrete-event heuristic, which allows us to model dependencies caused by batching and different paths of jobs efficiently in a near-natural way. As shown in a series of numerical experiments, the proposed simulation-optimization algorithm can find solutions that significantly outperform those provided by employing state-of-the-art simulation software.Peer ReviewedPostprint (published version

Mathematical Multi-Objective Optimization of the Tactical Allocation of Machining Resources in Functional Workshops

In the aerospace industry, efficient management of machining capacity is crucial to meet the required service levels to customers and to maintain control of the tied-up working capital. We introduce new multi-item, multi-level capacitated resource allocation models with a medium--to--long--term planning horizon. The model refers to functional workshops where costly and/or time- and resource-demanding preparations (or qualifications) are required each time a product needs to be (re)allocated to a machining resource. Our goal is to identify possible product routings through the factory which minimize the maximum excess resource loading above a given loading threshold while incurring as low qualification costs as possible and minimizing the inventory.In Paper I, we propose a new bi-objective mixed-integer (linear) optimization model for the Tactical Resource Allocation Problem (TRAP). We highlight some of the mathematical properties of the TRAP which are utilized to enhance the solution process. In Paper II, we address the uncertainty in the coefficients of one of the objective functions considered in the bi-objective TRAP. We propose a new bi-objective robust efficiency concept and highlight its benefits over existing robust efficiency concepts. In Paper III, we extend the TRAP with an inventory of semi-finished as well as finished parts, resulting in a tri-objective mixed-integer (linear) programming model. We create a criterion space partitioning approach that enables solving sub-problems simultaneously. In Paper IV, using our knowledge from our previous work we embarked upon a task to generalize our findings to develop an approach for any discrete tri-objective optimization problem. The focus is on identifying a representative set of non-dominated points with a pre-defined desired coverage gap