Multiobjective scheduling for semiconductor manufacturing plants

Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment

Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

This paper presents a flexible genetic algorithm optimisation approach for multi-objective biopharmaceutical planning problems under uncertainty. The optimisation approach combines a continuous-time heuristic model of a biopharmaceutical manufacturing process, a variable-length multi-objective genetic algorithm, and Graphics Processing Unit (GPU)-accelerated Monte Carlo simulation. The proposed approach accounts for constraints and features such as rolling product sequence-dependent changeovers, multiple intermediate demand due dates, product QC/QA release times, and pressure to meet uncertain product demand on time. An industrially-relevant case study is used to illustrate the functionality of the approach. The case study focused on optimisation of conflicting objectives, production throughput, and product inventory levels, for a multi-product biopharmaceutical facility over a 3-year period with uncertain product demand. The advantages of the multi-objective GA with the embedded Monte Carlo simulation were demonstrated by comparison with a deterministic GA tested with Monte Carlo simulation post-optimisation

Solution Strategies in Short-term Scheduling for Multitasking Multipurpose Plants

This thesis addresses challenges in short-term scheduling of multipurpose facilities using mathematical optimization. Such approach involves the formulation of a predictive model and an objective function, and the development of a solution strategy around such scheduling model formulation in order to obtain an operating schedule that achieves certain objectives, such as maximization of throughput or minimization of makespan. There are many choices that must be made in these aspects of short-term scheduling, and these choices often lead to a trade-off between the solution quality and computational time. This thesis presents two studies analyzing the quality-CPU time trade-off in two major aspects: time representations in model formulation, and the strategy for handling multiple conflicting objectives. The ultimate goal is to develop bi-objective short-term scheduling approaches to tackle industrial-sized problems for multitasking multipurpose plants that are computationally inexpensive, but provide practical schedules with a good balance between throughput and makespan. The first study addresses the first aspect of interest and compares two different time representation approaches: discrete-time and continuous-time approaches. This comparison is made considering maximization of throughput as the sole objective. We show that, for the modeling framework implemented in this work, the selected discrete-time formulation typically obtained higher quality solutions, and required less time to solve compared to the selected continuous-time formulation, as the continuous-time formulation exhibited detrimental trade-off between computational time and solution quality. We also show that within the scope of this study, non-uniform discretization schemes typically yielded solutions of similar quality compared to a fine uniform discretization scheme, but required only a fraction of the computational time. The second study builds on the first study and develops a strategy around an efficient non-uniform discretization approach to handle the conflicting objectives of throughput maximization and makespan minimization, focusing on a priori multi-objective methods. Two main contributions are presented in this regard. The first contribution is to propose a priori bi-objective methods based on the hybridization of compromise programming and the U+03B5-constraint method. The second is to present short-term operational objective functions, that can be used within short-term scheduling to optimize desired long term objectives of maximizing throughput and minimizing makespan. Two numerical case studies, one in a semiconductor processing plant and an analytical services facility, are presented using a rolling horizon framework, which demonstrate the potential for the proposed methods to improve solution quality over a traditional a priori approac

Systemic approach and decision process for sustainability in chemical engineering: Application to computer aided product design

Dans un contexte de prise en compte croissante des enjeux environnementaux, l'industrie de la chimie et des procédés se retrouve confrontée à des problématiques de substitution de molécules. Les méthodes de formulation inverse, qui consistent en la recherche assistée par ordinateur de molécules satisfaisant un ensemble de contraintes, répondent de manière efficace à ces problématiques. A partir de l'analyse systémique des usages et fonctionnalités nécessaires dans ce contexte, nous développons un outil logiciel de formulation inverse mettant en oeuvre un algorithme génétique. Celui-ci est capable d'explorer un espace de solutions plus vaste en considérant les mélanges et non les molécules seules. Par ailleurs, il propose une définition des problèmes très flexible qui permet la recherche efficiente de molécules issues de filières renouvelables. En s'appuyant sur l'ingénierie système et l'ingénierie d'entreprise, nous proposons un processus formel de prise de décision pour la substitution de produit dans un contexte industriel. Ce processus de décision multi-critères englobe les phases de définition des exigences, de génération de solutions alternatives, de sélection de la meilleure alternative et de mise en oeuvre du produit. Il utilise une approche dirigée par les modèles et des techniques de prises de décision qui garantissent un alignement opérationnel en complément de l'alignement stratégique. A travers un cas d'étude, nous montrons comment l'utilisation conjointe de notre outil de recherche par formulation inverse et de notre processus de décision permet une démarche environnementale de substitution de produit à la fois efficiente et conforme à la réalité de l'entreprise. ABSTRACT : In a context where environmental issues are increasingly taken into account, the chemical related industry faces situations imposing a chemical product substitution. Computer aided molecular design methods, which consist in finding molecules satisfying a set of constraints, are well adapted to these situations. Using a systemic analysis of the needs and uses linked to this context, we develop a computer aided product design tool implementing a genetic algorithm. It is able to explore a wider solution space thanks to a flexible molecular framework. Besides, by allowing a very flexible setting of the problem to be solved, it enables the search of molecules sourced from renewable resources. Based on concepts from system and enterprise engineering, we formalize a decision making process dedicated to the product substitution in an industrial context. This multi-criteria decision process includes the phases of the requirements definition, of the generation of alternative solutions, of the selection of the best alternative and of the product application. It uses a model driven approach and decision making techniques that guaranty an operational alignment in addition to the strategic alignment across the chemical enterprise. Through a case study, we expose how the combination of our computer aided product design tool and our decision making process enables an environmentally compliant approach of product substitution which is both efficient and in adequacy with enterprise context