Fuzzy uncertainty modelling for project planning; application to helicopter maintenance

Maintenance is an activity of growing interest specially for critical systems. Particularly, aircraft maintenance costs are becoming an important issue in the aeronautical industry. Managing an aircraft maintenance center is a complex activity. One of the difficulties comes from the numerous uncertainties that affect the activity and disturb the plans at short and medium term. Based on a helicopter maintenance planning and scheduling problem, we study in this paper the integration of uncertainties into tactical and operational multiresource, multi-project planning (respectively Rough Cut Capacity Planning and Resource Constraint Project Scheduling Problem). Our main contributions are in modelling the periodic workload on tactical level considering uncertainties in macro-tasks work contents, and modelling the continuous workload on operational level considering uncertainties in tasks durations. We model uncertainties by a fuzzy/possibilistic approach instead of a stochastic approach since very limited data are available. We refer to the problems as the Fuzzy RoughCut Capacity Problem (FRCCP) and the Fuzzy Resource Constraint Project Scheduling Problem (RCPSP).We apply our models to helicopter maintenance activity within the frame of the Helimaintenance project, an industrial project approved by the French Aerospace Valley cluster which aims at building a center for civil helicopter maintenance

Theory and Practice of Supply Chain Synchronization

In this dissertation, we develop strategies to synchronize component procurement in assemble-to-order (ATO) production and overhaul operations. We focus on the high-tech and mass customization industries which are not only considered to be very important to create or keep U.S. manufacturing jobs, but also suffer most from component inventory burden. In the second chapter, we address the deterministic joint replenishment inventory problem with batch size constraints (JRPB). We characterize system regeneration points, derive a closed-form expression of the average product inventory, and formulate the problem of finding the optimal joint reorder interval to minimize inventory and ordering costs per unit of time. Thereafter, we discuss exact solution approaches and the case of variable reorder intervals. Computational examples demonstrate the power of our methodology. In the third chapter, we incorporate stochastic demand to the JRPB. We propose a joint part replenishment policy that balances inventory and ordering costs while providing a desired service level. A case study and guided computational experiments show the magnitudes of savings that are possible using our methodology. In the fourth chapter, we show how lack of synchronization in assembly systems with long and highly variable component supply lead times can rapidly deteriorate system performance. We develop a full synchronization strategy through time buffering of component orders, which not only guarantees meeting planned production dates but also drastically reduces inventory holding costs. A case study has been carried out to prove the practical relevance, assess potential risks, and evaluate phased implementation policies. The fifth chapter explores the use of condition information from a large number of distributed working units in the field to improve the management of the inventory of spare parts required to maintain those units. Synchronization is again paramount here since spare part inventory needs to adapt to the condition of the engine fleet. All needed parts must be available to complete the overhaul of a unit. We develop a complex simulation environment to assess the performance of different inventory policies and the value of health monitoring. The sixth chapter concludes this dissertation and outlines future research plans as well as opportunities

Project scheduling under uncertainty using fuzzy modelling and solving techniques

In the real world, projects are subject to numerous uncertainties at different levels of planning. Fuzzy project scheduling is one of the approaches that deal with uncertainties in project scheduling problem. In this paper, we provide a new technique that keeps uncertainty at all steps of the modelling and solving procedure by considering a fuzzy modelling of the workload inspired from the fuzzy/possibilistic approach. Based on this modelling, two project scheduling techniques, Resource Constrained Scheduling and Resource Leveling, are considered and generalized to handle fuzzy parameters. We refer to these problems as the Fuzzy Resource Constrained Project Scheduling Problem (FRCPSP) and the Fuzzy Resource Leveling Problem (FRLP). A Greedy Algorithm and a Genetic Algorithm are provided to solve FRCPSP and FRLP respectively, and are applied to civil helicopter maintenance within the framework of a French industrial project called Helimaintenance

Robust Design of Supply Network Subject to Disruptions by Considering Congestion Effects

This thesis is focused on the supply chain disruptions and it reviews cost-efficient risk mitigation strategies to sustain supply chain functionality when disruptions occur. In particular, we study the robust design of supply flow subject to minor operational risks and major disruptions. The contingent sourcing along with strategic stock is incorporated as risk management strategies. We consider a firm with two suppliers where the main supplier is cost-effective but prone to disruptions and the back-up supplier is reliable but expensive. The back-up supplier can scale up its capacity according to a speed related to its configuration in order to supply the required flow of material when the main supplier disrupts. When minor disruption occurs, the strategic stock can cover the losses. The design problem considered is to determine optimal strategic stock level and response speed of volume-flexible back-up supplier. The back-up supplier might not provide the required supply level instantaneously due to non-steady production state and congestion during the response time. Therefore, there could be material shortages if the actual level of available capacity during the response time is ignored. The first chapter includes the incorporation of the clearing function into a contingency capacity planning model in order to represent the impact of congestion. The appropriate response speed is selected through a decision tree analysis considering different attitudes of the decision maker towards risk. The results show that considering congestion impact is especially critical for risk-neutral decision makers. The second chapter considers the randomness associated with the available capacity through a two-stage robust optimization model. The results show improvement in the quality of optimal solution by considering the randomness. The objective in the third chapter is to find an equitable solution which has an efficient performance with respect to all plausible scenarios. Therefore, the Ordered Weighted Averaging aggregation operator is incorporated in the objective function of a MIP robust model. In order to address the computational complexity associated with large set of scenarios, a novel clustering based scenario reduction model based on location covering model is proposed. The results show that the proposed methodology provide an accurate reduced scenario set within relatively short computational time

Economic effects of mobile technologies on operations of sales agents

In the presented paper we introduce an approach to assess particular economic effects which may arise with bringing mobile technologies into the field of sales and distribution. The research problem posed here comprises quite a special case where sales operations of a company are carried by its sales representatives, which may count as a resource allocation problem. We apply stochastic programming methodology to model the agent's multistage decision making in a distribution system with uncertain customer demands, and exemplify a potential improvement in the company's overall performance when mobile facilities are utilized for making decisions. We provide finally an efficient computational algorithm that delivers optimal decision making with and without mobile technologies, and computers the expected overall performance in both cases, for any configuration of a distribution system. Some computational results are presented. --

Multi-objective optimization for time-based preventive maintenance within the transport network: a review

Preventive maintenance in transportation is essential not only to safeguard billions in business and infrastructure investment, but also to guarantee safety, reliability and efficacy within the network. Government, industry and society have been increasingly recognising the importance of keeping transport units condition well-preserved. The challenge, however, is to achieve optimal performance of the existing transport systems within acceptable costs, effective workforce use and minimum disruption. Those are generally conflicting objectives. Multi-objective optimisation approaches have served as powerful tools to assist stakeholders to properly deploy preventive maintenance in industry. In this study, we review the research conducted in the application of multi-objective optimisation for preventive maintenance in transport-related activities. We focus on time-based preventive maintenance for production, infrastructure, rail and energy providers. In our review, we are interested in aspects such as the types of problems addressed, the existing objectives, the approaches to solutions, and how the outcomes obtained support decision

Aerospace Manufacturing-Remanufacturing System Modeling and Optimization

In recent years, increasing environmental concerns, costs of raw materials, and stricter government regulations have resulted in companies striving to reduce their waste materials. An earlier approach adopted was the recycling of materials such as waste paper, glass and metals. However, recycled products typically lose a portion of their added values. Different waste reduction options such as direct reuse, repair, refurbishing, cannibalization, and remanufacturing were studied to overcome this drawback. Remanufacture recaptures the value added to materials when a product was first manufactured. In the aerospace industry, where safety and performance are the overriding concerns and repairs are highly regulated, it could be perceived that remanufacturing has minimal appeal. However, the very low design tolerance of manufactured components results in a high percentage of defects. Due to the high price of raw materials, remanufacturing and components saving through “transforming” could be applied in imperfect production systems to reduce the amount of scrap materials. In this thesis, a general model is first proposed for a closed-loop supply chain network which includes the following processes: repairs, remanufacturing and transforming of selected defective components and end-of-life products, and cannibalization. A mixed integer linear programming formulation is developed to investigate the effect of various factors on profit, inventory carrying cost, and number of scrap components. Uncertainty in demand and lead-time is one of the major issues in any manufacturing supply chain. Uncertainty is incorporated into an extended model through the scenario-analysis approach and outsourcing is considered as an option for remanufacturing of the customer owned components. Demand of final products is assumed to be deterministic. The defect rate of disassembled components, however, is considered to be variable which makes the demand for spares to be variable. The lead-time of in-house remanufacturing of the customer owned components is also considered to be variable. Sensitivity analysis is performed to investigate the effect of capacity, inventory carrying cost, outsourcing cost, lead-time, and defect rate variation on profit and amount of scraps. The inventory carrying cost variations have direct effect on the inventory turnover ratio. The maximum capacity of the outsourced company and process costs per unit have significant effect on the profitability. Maintaining a long-term relationship with third-party service providers, designing the components with a longer life cycle, and transforming and remanufacturing of defective components directly impact the profitability over the life cycle of a product

Next generation smart manufacturing and service systems using big data analytics

© 2018 Elsevier Ltd This special issue explores advancements in the next generation manufacturing and service systems by examining the novel methods, practical challenges and opportunities in the use of big data analytics. The selected articles analyse a range of scenarios where big data analytics and its applications were used for improving decision making in manufacturing and services sector such as online data analytics, sourcing decisions with considerations for big data analytics, barriers in the adoption of big data analytics, maintenance planning, and multi-sensor data for fault pattern extraction. The paper summarises the discussions on the use of big data analytics in manufacturing and service sectors

Stochastic Delay Cost Functions to Estimate Delay Propagation under Uncertainty

We provide a mathematical formulation of flight-specific delay cost functions that enables a detailed tactical consideration of how a given flight delay will interact with all downstream constraints in the respective aircraft rotation. These functions are reformulated into stochastic delay cost functions to respect conditional probabilities and increasing uncertainty related to more distant operational constraints. Conditional probabilities are learned from historical operations data, such that typical delay propagation patterns can support the flight prioritization process as a part of tactical airline schedule recovery. A case study compares the impact of deterministic and stochastic cost functions on optimal recovery decisions during an airport constraint. We find that deterministic functions systematically overestimate potential disruption costs as well as optimal schedule recovery costs in high delay situations. Thus, an optimisation based on stochastic costs outperforms the deterministic approach by up to 15%, as it reveals ’hidden’ downstream recovery potentials. This results in different slot allocations and in fewer passengers missing their connections

Conception et application d'une méthodologie multicritère floue de sélection de logiciels de planification et d'ordonnancement avancé (APS)

Avec la mondialisation, la croissance des entreprises et les besoins de plus en plus exigeants des clients, les défis en termes de planification et d’ordonnancement des opérations en environnement manufacturier ne cessent de croitre. Face à cette situation, les entreprises manufacturières sont dans l’obligation de mettre à jour leurs politiques de planification et d’ordonnancement en adoptant des systèmes et des approches de planifications nouvelles telles que la planification et l’ordonnancement avancés (POA). Dans cet exercice, les entreprises désirant implanter des approches de POA ont généralement deux possibilités. Elles peuvent choisir de développer une solution personnalisée ou alors d’implanter des logiciels commerciaux de POA. La deuxième piste est plus courue de nos jours. L’objectif de ce travail est d’accompagner les entreprises désirant améliorer la planification et l’ordonnancement de leurs opérations par la sélection et l’implantation d’un logiciel commercial de POA. Plus précisément, le but de ce travail est d’évaluer et de sélectionner parmi les logiciels commerciaux de POA disponibles sur le marché celui qui satisfait au mieux les besoins de l’entreprise. Trois sous objectifs ont été identifiés : la cartographie des processus de planification et d’ordonnancement de l’entreprise, la capture des besoins de l’entreprise et la conception d’une nouvelle méthodologie de sélection intégrant sous incertitude à la fois les besoins de l’entreprise et les critères et sous critères de sélection. La méthodologie adoptée pour cette étude est celle dictée par la science de la conception, qui permet l’itération du processus de conception afin de perfectionner et de valider les résultats ou les livrables obtenus. Des données sont recueillies auprès d’experts et des preneurs de décisions internes à l’entreprise à l’aide d’entrevues individuelles et de groupes. Par ailleurs, en guise de contributions de cette recherche, trois méthodes ont été conçues. La première méthode permet de cartographier les processus de l’entreprise. La deuxième méthode est destinée à la capture des besoins de l’entreprise tandis que la troisième méthode intègre le déploiement de la fonction qualité (DFQ), l’analyse hiérarchique des processus (AHP) et la méthode VIKOR pour la sélection du logiciel qui satisfait au mieux les besoins de l’entreprise. Cette intégration est rendue possible en mettant en place une version modifiée du DFQ. L’incertitude sur les données provenant des enquêtes adressées aux experts et aux preneurs de décision est considérée par l’utilisation de la logique floue et des variables linguistiques. L’approche globale de l’étude est appliquée à un cas réel d’entreprise manufacturière. Les résultats montrent la pertinence des méthodes développées face au problème de selection d’un logiciel de POA