Proposed procedure for optimal maintenance scheduling under emergent failures

Production lines are usually subjected to emergent machine failures. Such emergent failures disrupt pre-established maintenance schedules, which challenge maintenance engineers to react to those failures in real time. This research proposes an optimization procedure for optimizing scheduling repairs of emergent failures. Three optimization models are developed. Model I schedules failures in newly idle repair shops with the objective of maximizing the number of scheduled repairs. Model II maximizes the number of assigned repairs to untapped ranges. Model III maximizes both the number of assigned failure repairs and satisfaction on regular and emergency repairs by resequencing regular and emergent failures in the shop that contains the largest free margin. A real case study is provided to illustrate the proposed optimization procedure. Results reveal that the proposed models efficiently scheduled and sequenced emergent failures in the idle maintenance shops, the untapped ranges between repairs of regular failures, and in the maintenance shop with the largest free margin. In conclusions, the proposed models can greatly support maintenance engineers in planning repairs under unexpected failures.

Optimising cost and availability estimates at the bidding stage of performance-based contracting

Performance-Based Contracting (PBC), e.g. Contracting for Availability (CfA), has been extensively applied in many industry sectors such as defence, aerospace and railway. Under PBC, complex support activities (e.g. maintenance, training, etc.) are outsourced, under mid to long term contracting arrangements, to maintain certain level of systems’ performance (e.g. availability). However, building robust cost and availability estimates is particularly challenging at the bidding stage because therei is lack of methods and limited availability of data for analysis. Driven by this contextual challenge this PhD aims to develop a process to simulate and optimise cost and availability estimates at the bidding stage of CfA. The research methodology follows a human-centred design approach, focusing on the end-user stakeholders. An interaction with seven manufacturing organisations involved in the bidding process of CfA enabled to identify the state-of-practice and the industry needs, and a review of literature in PBC and cost estimation enabled to identify the research gaps. A simulation model for cost and availability trade-off and estimation (CATECAB) has been developed, to support cost engineers during the bidding preparation. Also, a multi-objective genetic algorithm (EMOGA) has been developed to combine with the CATECAB and build a cost and availability estimation and optimisation model (CAEOCAB). Techniques such as Monte-Carlo simulation, bootstrapping resampling, multi-regression analysis and genetic algorithms have been applied. This model is able to estimate the optimal investment in the attributes that impact the availability of the systems, according to total contract cost, availability and duration targets. The validation of the models is performed by means of four case studies with twenty-one CfA scenarios, in the maritime and air domains. The outcomes indicate a representable accuracy for the estimates produced by the models, which has been considered suitable for the early stages of the bidding process

Algorithms for vehicle routing problems with heterogeneous fleet, flexible time windows and stochastic travel times

Orientador: Vinícius Amaral ArmentanoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho aborda três variantes multiatributo do problema de roteamento de veículos. A primeira apresenta frota heterogênea, janelas de tempo invioláveis e tempos de viagem determinísticos. Para resolvê-la, são propostos algoritmos ótimos baseados na decomposição de Benders. Estes algoritmos exploram a estrutura do problema em uma formulação de programação inteira mista, e três diferentes técnicas são desenvolvidas para acelerá-los. A segunda variante contempla os atributos de frota heterogênea, janelas de tempo flexíveis e tempos de viagem determinísticos. As janelas de tempo flexíveis permitem o início do serviço nos clientes com antecipação ou atraso limitados em relação às janelas de tempo invioláveis, com custos de penalidade. Este problema é resolvido por extensões dos algoritmos de Benders, que incluem novos algoritmos de programação dinâmica para a resolução de subproblemas com a estrutura do problema do caixeiro viajante com janelas de tempo flexíveis. A terceira variante apresenta frota heterogênea, janelas de tempo flexíveis e tempos de viagem estocásticos, sendo representada por uma formulação de programação estocástica inteira mista de dois estágios com recurso. Os tempos de viagem estocásticos são aproximados por um conjunto finito de cenários, gerados por um algoritmo que os descreve por meio da distribuição de probabilidade Burr tipo XII, e uma matheurística de busca local granular é sugerida para a resolução do problema. Extensivos testes computacionais são realizados em instâncias da literatura, e as vantagens das janelas de tempo flexíveis e dos tempos de viagem estocásticos são enfatizadasAbstract: This work addresses three multi-attribute variants of the vehicle routing problem. The first one presents a heterogeneous fleet, hard time windows and deterministic travel times. To solve this problem, optimal algorithms based on the Benders decomposition are proposed. Such algorithms exploit the structure of the problem in a mixed-integer programming formulation, and three algorithmic enhancements are developed to accelerate them. The second variant comprises a heterogeneous fleet, flexible time windows and deterministic travel times. The flexible time windows allow limited early and late servicing at customers with respect to their hard time windows, at the expense of penalty costs. This problem is solved by extensions of the Benders algorithms, which include novel dynamic programming algorithms for the subproblems with the special structure of the traveling salesman problem with flexible time windows. The third variant presents a heterogeneous fleet, flexible time windows and stochastic travel times, and is represented by a two-stage stochastic mixed-integer programming formulation with recourse. The stochastic travel times are approximated by a finite set of scenarios generated by an algorithm which describes them using the Burr type XII distribution, and a granular local search matheuristic is suggested to solve the problem. Extensive computational tests are performed on instances from the literature, and the advantages of flexible windows and stochastic travel times are stressed.DoutoradoAutomaçãoDoutor em Engenharia Elétrica141064/2015-3CNP