Agent-based strategic planner for the production of samll lots of complex products: theoretical and practical perspectives

This paper presents a strategic planner that assists the decision-makers to take strategic decisions on short term to respond faster and efficiently to unexpected events in the ramp-up production of complex and highly customized products, namely in situations of peak of demand, late change requests and immature technology. This tool combines the flexibility of multi-agent systems with the optimization capability of mathematical optimization solvers. The application of the strategic planner is illustrated by playing iterative what-if games supporting implementation of mitigation strategies addressing a real use case of a peak demand of a specific product.The research leading to these results has received funding from the European Union Seventh Frame-work Programme FP7 ARUM project, under grant agreement n° 314056info:eu-repo/semantics/publishedVersio

A general framework integrating techniques for scheduling under uncertainty

Ces dernières années, de nombreux travaux de recherche ont porté sur la planification de tâches et l'ordonnancement sous incertitudes. Ce domaine de recherche comprend un large choix de modèles, techniques de résolution et systèmes, et il est difficile de les comparer car les terminologies existantes sont incomplètes. Nous avons cependant identifié des familles d'approches générales qui peuvent être utilisées pour structurer la littérature suivant trois axes perpendiculaires. Cette nouvelle structuration de l'état de l'art est basée sur la façon dont les décisions sont prises. De plus, nous proposons un modèle de génération et d'exécution pour ordonnancer sous incertitudes qui met en oeuvre ces trois familles d'approches. Ce modèle est un automate qui se développe lorsque l'ordonnancement courant n'est plus exécutable ou lorsque des conditions particulières sont vérifiées. Le troisième volet de cette thèse concerne l'étude expérimentale que nous avons menée. Au-dessus de ILOG Solver et Scheduler nous avons implémenté un prototype logiciel en C++, directement instancié de notre modèle de génération et d'exécution. Nous présentons de nouveaux problèmes d'ordonnancement probabilistes et une approche par satisfaction de contraintes combinée avec de la simulation pour les résoudre. ABSTRACT : For last years, a number of research investigations on task planning and scheduling under uncertainty have been conducted. This research domain comprises a large number of models, resolution techniques, and systems, and it is difficult to compare them since the existing terminologies are incomplete. However, we identified general families of approaches that can be used to structure the literature given three perpendicular axes. This new classification of the state of the art is based on the way decisions are taken. In addition, we propose a generation and execution model for scheduling under uncertainty that combines these three families of approaches. This model is an automaton that develops when the current schedule is no longer executable or when some particular conditions are met. The third part of this thesis concerns our experimental study. On top of ILOG Solver and Scheduler, we implemented a software prototype in C++ directly instantiated from our generation and execution model. We present new probabilistic scheduling problems and a constraintbased approach combined with simulation to solve some instances thereof

The Integration of Maintenance Decisions and Flow Shop Scheduling

In the conventional production and service scheduling problems, it is assumed that the machines can continuously process the jobs and the information is complete and certain. However, in practice the machines must stop for preventive or corrective maintenance, and the information available to the planners can be both incomplete and uncertain. In this dissertation, the integration of maintenance decisions and production scheduling is studied in a permutation flow shop setting. Several variations of the problem are modeled as (stochastic) mixed-integer programs. In these models, some technical nuances are considered that increase the practicality of the models: having various types of maintenance, combining maintenance activities, and the impact of maintenance on the processing times of the production jobs. The solution methodologies involve studying the solution space of the problems, genetic algorithms, stochastic optimization, multi-objective optimization, and extensive computational experiments. The application of the problems and managerial implications are demonstrated through a case study in the earthmoving operations in construction projects

What-if game simulation in agent-based strategic production planners

In the nowadays highly unstable manufacturing market, companies are faced, on a daily basis, with important strategic decisions, such as “does the company has the necessary capacity to accept a high volume order?” or “what measures need to be implemented if the product demand increases x% a year?”. Decision-makers, i.e. company’s managers, rely on their experience and insights supported by classical tools to take such decisions. Classical mathematical solvers or agent-based systems are typical architectural solutions to implement strategic planning tools to support decision-makers on this important task. Within the ARUM (Adaptive Production Management) project, a hybrid strategic planning tool was specified and developed, combining the optimization features of classical solvers with the flexibility and agility of agent systems. This paper briefly presents such architecture and focuses on the generation of the “what-if game” mechanism to support the generation of more intelligent and dynamic planning solutions.The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007- 2013 under grant agreement n° 314056.info:eu-repo/semantics/publishedVersio

Reducing physical ergonomic risks at assembly lines by line balancing and job rotation: A survey

Factors such as repetitiveness of work, required application of forces, handling of heavy loads, and awkward, static postures expose assembly line workers to risks of musculoskeletal disorders. As a rule, companies perform a post hoc analysis of ergonomic risks and examine ways to modify workplaces with high ergonomic risks. However, it is possible to lower ergonomic risks by taking ergonomics aspects into account right from the planning stage. In this survey, we provide an overview of the existing optimization approaches to assembly line balancing and job rotation scheduling that consider physical ergonomic risks. We summarize major findings to provide helpful insights for practitioners and identify research directions

Multi-agent deep Q-network-based metaheuristic algorithm for Nurse Rostering Problem

The Nurse Rostering Problem (NRP) aims to create an efficient and fair work schedule that balances both the needs of employees and the requirements of hospital operations. Traditional local search-based metaheuristic algorithms, such as adaptive neighborhood search (ANS) and variable neighborhood descent (VND), mainly focus on optimizing the current solution without considering potential long-term consequences, which may easily get stuck in local optima and limit the overall performance. Thus, we propose a multi-agent deep Q-network-based metaheuristic algorithm (MDQN-MA) for NRP to harness the strengths of various metaheuristics. Each agent encapsulates a metaheuristic algorithm, where its available actions represent different perspectives of the problem environment. By combining their strengths and various perspectives, these agents can work collaboratively to navigate and search for a broader range of potential solutions effectively. Furthermore, to improve the performance of an individual agent, we model its neighborhood search as a Markov Decision Process model and integrate a deep Q-network to consider long-term impacts for its neighborhood sequential decision-making. The experimental results clearly show that an individual agent in MDQN-MA can outperform ANS and VND, and multiple agents in MDQN-MA even perform better, achieving the best results among metaheuristic algorithms on the Second International Nurse Rostering Competition dataset

Integrated methodological frameworks for modelling agent-based advanced supply chain planning systems: a systematic literature review

Purpose: The objective of this paper is to provide a systematic literature review of recent developments in methodological frameworks for the modelling and simulation of agent-based advanced supply chain planning systems. Design/methodology/approach: A systematic literature review is provided to identify, select and make an analysis and a critical summary of all suitable studies in the area. It is organized into two blocks: the first one covers agent-based supply chain planning systems in general terms, while the second one specializes the previous search to identify those works explicitly containing methodological aspects. Findings: Among sixty suitable manuscripts identified in the primary literature search, only seven explicitly considered the methodological aspects. In addition, we noted that, in general, the notion of advanced supply chain planning is not considered unambiguously, that the social and individual aspects of the agent society are not taken into account in a clear manner in several studies and that a significant part of the works are of a theoretical nature, with few real-scale industrial applications. An integrated framework covering all phases of the modelling and simulation process is still lacking in the literature visited. Research limitations/implications: The main research limitations are related to the period covered (last four years), the selected scientific databases, the selected language (i.e. English) and the use of only one assessment framework for the descriptive evaluation part. Practical implications: The identification of recent works in the domain and discussion concerning their limitations can help pave the way for new and innovative researches towards a complete methodological framework for agent-based advanced supply chain planning systems. Originality/value: As there are no recent state-of-the-art reviews in the domain of methodological frameworks for agent-based supply chain planning, this paper contributes to systematizing and consolidating what has been done in recent years and uncovers interesting research gaps for future studies in this emerging fieldPeer Reviewe

Risk-Based Optimal Scheduling for the Predictive Maintenance of Railway Infrastructure

In this thesis a risk-based decision support system to schedule the predictive maintenance activities, is proposed. The model deals with the maintenance planning of a railway infrastructure in which the due-dates are defined via failure risk analysis.The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, according to ISO 55000 guidelines, thus implying that the maintenance priorities are based on asset criticality, determined taking into account the relevant failure probability, related to asset degradation conditions, and the consequent damages

Collaborative and adaptive supply chain planning

Dans le contexte industriel d'aujourd'hui, la compétitivité est fortement liée à la performance de la chaîne d'approvisionnement. En d'autres termes, il est essentiel que les unités d'affaires de la chaîne collaborent pour coordonner efficacement leurs activités de production, de façon a produire et livrer les produits à temps, à un coût raisonnable. Pour atteindre cet objectif, nous croyons qu'il est nécessaire que les entreprises adaptent leurs stratégies de planification, que nous appelons comportements, aux différentes situations auxquelles elles font face. En ayant une connaissance de l'impact de leurs comportements de planification sur la performance de la chaîne d'approvisionnement, les entreprises peuvent alors adapter leur comportement plutôt que d'utiliser toujours le même. Cette thèse de doctorat porte sur l'adaptation des comportements de planification des membres d'une même chaîne d'approvisionnement. Chaque membre pouvant choisir un comportement différent et toutes les combinaisons de ces comportements ayant potentiellement un impact sur la performance globale, il est difficile de connaître à l'avance l'ensemble des comportements à adopter pour améliorer cette performance. Il devient alors intéressant de simuler les différentes combinaisons de comportements dans différentes situations et d'évaluer les performances de chacun. Pour permettre l'utilisation de plusieurs comportements dans différentes situations, en utilisant la technologie à base d'agents, nous avons conçu un modèle d'agent à comportements multiples qui a la capacité d'adapter son comportement de planification selon la situation. Les agents planificateurs ont alors la possibilité de se coordonner de façon collaborative pour améliorer leur performance collective. En modélisant les unités d'affaires par des agents, nous avons simulé avec la plateforme de planification à base d'agents de FORAC des agents utilisant différents comportements de planification dits de réaction et de négociation. Cette plateforme, développée par le consortium de recherche FORAC de l'Université Laval, permet de simuler des décisions de planification et de planifier les opérations de la chaîne d'approvisionnement. Ces comportements de planification sont des métaheurisciques organisationnelles qui permettent aux agents de générer des plans de production différents. La simulation est basée sur un cas illustrant la chaîne d'approvisionnement de l'industrie du bois d'œuvre. Les résultats obtenus par l'utilisation de multiples comportements de réaction et de négociation montrent que les systèmes de planification avancée peuvent tirer avantage de disposer de plusieurs comportements de planification, en raIson du contexte dynamique des chaînes d'approvisionnement. La pertinence des résultats de cette thèse dépend de la prémisse que les entreprises qui adapteront leurs comportements de planification aux autres et à leur environnement auront un avantage concurrentiel important sur leurs adversaires

Planification socio-responsable du travail dans les chaînes de montage d'aéronefs : comment satisfaire à la fois objectifs ergonomiques et économiques

Dans cette thèse, le problème de planification des tâches dans les chaînes de montage des aéronefs est étudié. Ces lignes de production sont principalement manuelles et tactées. L'échec de la livraison dans les délais pouvant entraîner des pénalités importantes pour le fabricant, il est essentiel de respecter le calendrier de chaque poste de travail en tenant compte à la fois de critères économiques et ergonomiques. Ce problème de planification peut être considéré comme un problème généralisé de planification de projets avec contraintes de ressources (RCPSP). Dans un premier temps, nous passons en revue les méthodes ergonomiques existantes qui peuvent être utilisées pour évaluer la charge de travail physique dans les lignes de production et examinons leur applicabilité au contexte des chaînes de montage d'aéronefs avec des temps de cycle longs. Sur la base de cette évaluation, nous développons des modèles mathématiques à introduire dans les problèmes considérés du RCPSP afin de prendre en compte l'impact ergonomique sur les opérateurs. Tenant compte de ces contraintes ergonomiques, le problème industriel initial est modélisé comme un RCPSP avec des contraintes et des objectifs spéciaux intégrant à la fois des aspects économiques et ergonomiques. Plusieurs formulations avec des opérateurs polyvalents, des ressources avec des capacités dépendantes du temps, des contraintes sur les facteurs ergonomiques et des tâches multimodales ordonnées par des relations de précédence complexes sont considérées. Des modèles de programmation par contraintes et de programmation linéaire en nombres entiers ont été développés pour ces formulations. Afin d'améliorer les procédures de solution, de nouvelles techniques de propagation de contraintes sont proposées et mises en œuvre. Un nouvel algorithme pour le calcul de la borne inférieure est également développé. L'efficacité des modèles et méthodes présentés est validée par des expériences numériques.In this thesis, the scheduling problem of tasks in aircraft assembly lines is studied. These production lines are mainly manual and paced. Since the failure of delivery on time may result in significant penalties for the manufacturer, it is crucial to meet the schedule at each workstation taking into account both economic and ergonomic criteria. This scheduling problem can be considered as a generalized Resource-Constraints Project Scheduling Problem (RCPSP). Firstly, we review the existing ergonomic methods that can be used to evaluate the physical workload in production lines and examine their applicability to the context of aircraft assembly lines with long takt times. On the basis of this evaluation, we develop mathematical models to be introduced in considered RCPSP problems in order to take into account the ergonomic impact on the operators. Taking into consideration these ergonomic constraints, the original industrial problem is modeled as a RCPSP with special constraints and objectives integrating both economic and ergonomic aspects. Several formulations with multi-skilled operators, resources with time-dependent capacities, constraints on ergonomic factors and multi-mode tasks ordered by precedence relations with time lags are considered. Constraint Programming and Integer Linear Programming models are developed for these formulations. In order to enhance the solution procedures, novel constraint propagation techniques are proposed and implemented. A new algorithm for lower bound calculation is developed as well. The efficiency of presented models and methods are validated through numerical experiments