A review of simulation-based optimisation in maintenance operations

This paper aims to report the state of the art of research in simulation-based optimisation of maintenance operations by systematically classifying the published literature and outlining various tools and techniques used by researchers to model and optimise maintenance operations. The authors investigate the critical elements and aspects of maintenance systems and how well they are represented in the literature as well as various approaches to problem formulation. On this basis, the paper identifies the current gaps and discusses future prospects. It is observed that discrete event is the most widely used simulation technique while non-traditional optimisation algorithms such as genetic algorithms and simulated annealing are the most reported optimisation techniques. Little attention has been paid to the discussion and analysis of different elements in the maintenance environment and their effect on the maintenance system behaviour. There is a need for verifying suggested models through real life case studies

Integrated Production and Maintenance Planning for Flow Line Systems

This study extends the investigation of the capacitated lot-sizing problem to the production and maintenance planning in unreliable flow line systems. An integrated modelling framework is proposed with the aim of seeking a cost-optimal plan for both production and maintenance. In the model, preventive maintenance is scheduled to avoid unplanned failures, and corrective maintenance is carried out in any machine in which an unplanned failure occurs. A regression-based approximation approach was introduced to calculate the production time under random failures. Then, the integrated planning model can be solved by any commercial optimization software. The numerical example demonstrates that the integrated model guarantees the effectiveness of the production and maintenance plan. It also showed that the buffer capacity has significant effects on the capacity utilization

A novel approach for modelling complex maintenance systems using discrete event simulation

Existing approaches for modelling maintenance rely on oversimplified assumptions which prevent them from reflecting the complexity found in industrial systems. In this paper, we propose a novel approach that enables the modelling of non-identical multi-unit systems without restrictive assumptions on the number of units or their maintenance characteristics. Modelling complex interactions between maintenance strategies and their effects on assets in the system is achieved by accessing event queues in Discrete Event Simulation (DES). The approach utilises the wide success DES has achieved in manufacturing by allowing integration with models that are closely related to maintenance such as production and spare parts systems. Additional advantages of using DES include rapid modelling and visual interactive simulation. The proposed approach is demonstrated in a simulation based optimisation study of a published case. The current research is one of the first to optimise maintenance strategies simultaneously with their parameters while considering production dynamics and spare parts management. The findings of this research provide insights for non-conflicting objectives in maintenance systems. In addition, the proposed approach can be used to facilitate the simulation and optimisation of industrial maintenance systems

State of the art in simulation-based optimisation for maintenance systems

Recently, more attention has been directed towards improving and optimising maintenance in manufacturing systems using simulation. This paper aims to report the state of the art in simulation-based optimisation of maintenance by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. The authors investigate application areas and published real case studies as well as researched maintenance strategies and policies. Much of the research in this area is focusing on preventive maintenance and optimising preventive maintenance frequency that will lead to the minimum cost. Discrete event simulation was the most reported technique to model maintenance systems whereas modern optimisation methods such as Genetic Algorithms was the most reported optimisation method in the literature. On this basis, the paper identifies the current gaps and discusses future prospects. Further research can be done to develop a framework that guides the experimenting process with different maintenance strategies and policies. More real case studies can be conducted on multi-objective optimisation and condition based maintenance especially in a production context

Strategies for Increased Productivity Through Control of Process Constraints

In Nigeria, manufacturing businesses play a vital role in the industrial growth of the nation and have many dynamic benefits crucial for the growth of a sustainable economy. The manufacturing sector has added substantially to the gross domestic products of many countries. Nonetheless, 50% of manufacturing firms in Nigeria experience a decline in production capacity utilization and profitability because of inefficient production processes. The purpose of this qualitative multiple case study was to explore strategies leaders of manufacturing firms in Nigeria use to support efficient manufacturing operations. The theory of constraints served as the conceptual framework for this study. Eight participants from two manufacturing firms in Nigeria who had strategies to support efficient manufacturing operations participated in this study. Data sources included semistructured interviews and the review of organizational documents consisting of corporate quality policy, quality objectives, and mission statements. Analysis involved data compilation, data coding by breaking it down into categories, and reassembling the data into emergent themes. Member checking and methodological triangulation strengthened the credibility of the findings. Four major themes emerged: strategic planning, continuous process improvement, strategic equipment maintenance, and strategic capacity expansion. The findings from this research might provide the basis for developing an advanced manufacturing practice for some Nigerian manufacturing firms that could contribute to social change by improving production efficiency, local consumption, and sustainable economic growth

Redução do espaço de busca em problemas de Otimização via Simulação utilizando Análise Envoltória de Dados e Arranjos Ortogonais de Taguchi.

O desenvolvimento de diversas metaheurísticas possibilitaram o uso da otimização em ambientes de simulação a eventos discretos. No entanto, este campo de pesquisa ainda é pouco utilizado, principalmente, em função do tempo necessário para a convergência desses algoritmos. Nesse sentido, a otimização via simulação é influenciada pela complexidade do modelo de simulação, pelo número de variáveis de decisão e por seus limites de variação. Neste contexto, este trabalho propõe um método capaz de identificar os melhores limites de variação, para cada variável de decisão, em um problema de otimização via simulação, proporcionando uma redução do tempo computacional, ao mesmo tempo em que permite alcançar soluções de elevada qualidade (soluções ótimas ou estatisticamente iguais a ela). Para isso, o método proposto combina a simulação a eventos discretos, arranjos ortogonais de Taguchi e a análise de supereficiência desenvolvida no modelo DEA BCC. Neste método, o espaço de busca do problema de otimização via simulação é representado por meio de um arranjo ortogonal de Taguchi. Para gerar as saídas do modelo DEA BCC, executou-se a simulação do arranjo ortogonal (cenários) e posteriormente a análise de supereficiência. Com base nestes resultados, os cenários são ordenados, sendo adotados como novos limites do problema de otimização os valores das variáveis dos dois cenários de maior supereficiência. Para validar o método proposto, foram utilizados quinze objetos de estudo. Os casos representam problemas complexos de empresas de manufatura e da área hospitalar. Dessa forma, sua eficácia pode ser verificada, uma vez que permitiu reduções médias de 97% no espaço de busca, e de 42% no tempo computacional necessário para se obter uma solução. Além disso, para quatro dos casos estudados, foi realizada a comparação entre o resultado ótimo obtido com a simulação de toda região de solução, e o resultado da otimização realizada no espaço de busca reduzido. Pode-se concluir, com um nível de 95% de confiança, que as respostas obtidas foram estatisticamente iguais