Review on ship onboard machinery maintenance strategy selection using multi-criteria optimization

Abstract: Marine shipping is an important aspect of the transportation system in Canada. It is estimated that 70-80% of items that we are surrounded by and use daily are brought by ships. Canadian businesses need to sell to the world and ships carries their products abroad. For people that live in Canada’s island or northern communities, marine shipping is often the only source they have for essentials. It is estimated that marine shipping directly contributes about $3 billion annually to Canada’s GDP through employment and other impacts. In a marine ship system, safety and reliability are very important considerations. The various system elements must be properly maintained and organizations are now looking to maintenance optimization to achieve optimum safety, machinery reliability and reduced costs. Modern day maintenance optimization is a decision-making problem which need to satisfy multiple and conflicting criteria. Multi-Criteria Optimization (MCO) techniques have been used in maintenance optimization. Two main classes of maintenance MCO problems have been identified as strategy selection and interval optimization. In marine ships, maintenance strategy selection is a complex decision-making problem that has become ever more challenging to address and is accompanied by diverse constraints and economic considerations. Each maintenance strategy has its own characteristics, importance and drawbacks. The use of inappropriate maintenance strategy affects the safety of a ship, crew, machinery reliability, maintenance cost etc. MCO techniques have been used in selecting optimal maintenance strategy for ship onboard machinery.Communication présentée lors du congrès international tenu conjointement par Canadian Society for Mechanical Engineering (CSME) et Computational Fluid Dynamics Society of Canada (CFD Canada), à l’Université de Sherbrooke (Québec), du 28 au 31 mai 2023

Mathematical model for maintenance planning of machine tools

Preventive maintenance planning of machine tools may be a complex task for tools with multiple components. For manufacturing processes with high setup and downtime costs, components replacement should be combined to avoid too many production stoppages and therefore reduce costs. The combination that minimizes costs should take into account the lifetime distribution and the age of each component at the replacement time. Replacing too soon may imply a high number of replacements for a given component, while replacing too late may imply a high number of failures that lead to shutdowns, increasing costs. In this paper, a tool is seen as a series system, which means that whenever a component fails, a corrective action is needed and at least the failed component has to be replaced. In the literature, some of the models and heuristics for maintenance planning of series systems consider that a minimal repair is made when a component fails, while other models propose static approaches, i.e., the same combination and the same interval is used over time regardless of the ages of the components involved. This paper aims to propose a dynamic approach and presents a mathematical model to determine both the next time to perform a preventive maintenance task and the components that should be replaced in order to minimize the total cost. The model also intends to determine the components to be replaced preventively when unexpected events occur (such as the failure of a tool component or the machine, the shortage of raw material, etc.) or during planned stoppages (such as the end of a production order, machine preventive maintenance task, etc.).We would like to thank the companies associated with the project and express our appreciation for the commitment of the employees involved.This work has been supported by Norte 010247 FEDER 017833 – TechParts I&D

Analisis Efisiensi Pekerjaan Pada Pemeliharaan Komponen Mesin Belt Conveyor Kritis Menggunakan Pendekatan Preventive Maintenance di PT Varia Usaha Beton Cabang Batang

PT Varia Usaha Beton merupakan salah satu anak perusahaan dari PT Semen Indonesia Beton yang memproduksi beton. Dengan semakin naiknya permintaan produksi batu pecah, maka diperlukan kesiapan alat yang mendukung untuk proses produksi yang optimal. Penelitian ini bertujuan untuk menentukan komponen mesin belt conveyor kritis dan interval waktu pemeliharaan komponen. Metode yang digunakan dalam penelitian ini yaitu (FMEA) dan metode preventive maintenance. Penentuan mesin belt conveyor kritis diawali dengan pembuatan kuesioner. Penentuan mesin belt conveyor yang kritis berdasarkan dari kriteria mesin kritis dengan pendekatan critical analysis. Pendekatan ini untuk mendapatkan nilai kekritisan dari mesin belt conveyor. Berdasarkan hasil penelitian diatas dari MTTR didapatkan Portabel 1 paling lama dalam waktu perbaikannya, berbanding terbalik dengan Portabel 3 yang rata-rata waktu perbaikannya lebih sedikit dibanding 4 mesin belt conveyor kritis tersebut, Sedangkan nilai MTTF didapati Ground 2 paling lama selang waktu dari perbaikan menuju rusak kembali, berbanding terbalik dengan Portabel 1

A novel framework for simulation-based optimisation of maintenance systems

The maintenance function in manufacturing has been gaining growing interest and significance. Simulation based optimisation has a high potential in supporting maintenance managers to make the right decisions in complex maintenance systems. Surveys in maintenance optimisation have repeatedly reported the need of a framework that provides adequate level of details to guide both academics and practitioners in optimising maintenance systems. The purpose of the current study is to address this gap by developing a novel framework that supports decision making for maintenance in manufacturing systems. The framework is developed by synthesising research attempts to optimise maintenance by simulation, examining existing maintenance optimisation frameworks and capturing framework requirements from review papers in the area as well as publications on future maintenance applications. As a result, the framework addresses current issues in maintenance such as complexity, multi-objective optimisation and uncertainty. The framework is represented by a standard flowchart to facilitate its use

Sustainable Maintenance Practices and Skills for Competitive Production System

Many industries are becoming moribund, while those who are in operatives are producing at low efficiency which are not commensurate to the resources invested. These imbalances and poor performance attest to the fact that the maintenance practices adopted were unskilfully implemented and are not sustainable. Therefore, this chapter discusses the maintenance strategies and skills needed in establishing a sustainable maintenance technology for the manufacturing industries by the formulation of sustainable maintenance framework practices for competitive production systems and translation of the formulated framework to iconic and equation models. The expected outcome showed that maintenance sustainable practices cannot be undermined if production set goals and overall equipment effectiveness are to be achieved. Successful implementation of this instructive methodology will reduce wastages, eliminate machine downtime, increase machines performance with improved functionality of parts thereby providing maximum usability and reusability of parts/components and thus increase the machine optimal functionality and efficiency

Maintenance grouping for multi-component systems with availability constraints and limited maintenance teams

International audienceThe paper deals with a maintenance grouping approach for multi-component systems whose components are connected in series. The considered systems are required to serve a sequence of missions with limited breaks/stoppage durations while maintenance teams (repairmen) are limited and may vary over time. The optimization of the maintenance grouping decision for such multi-component systems leads to a NP-complete problem. The aim of the paper is to propose and to optimize a dynamic maintenance decision rule on a rolling horizon. The heuristic optimization scheme for the maintenance decision is developed by implementing two optimization algorithms (genetic algorithm and MULTIFIT) to find an optimal maintenance planning under both availability and limited repairmen constraints. Thanks to the proposed maintenance approach, impacts of availability constraints or/and limited maintenance teams on the maintenance planning and grouping are highlighted. In addition, the proposed grouping approach allows also updating online the maintenance planning in dynamic contexts such as the change of required availability level and/or the change of repairmen over time. A numerical example of a 20-component system is introduced to illustrate the use and the advantages of the proposed approach in the maintenance optimization framework

Maintenance scheduling for multicomponent systems with hidden failures

This paper develops a maintenance policy for a multicomponent system subject to hidden failures. Components of the system are assumed to suffer from hidden failures, which can only be detected at inspection. The objective of the maintenance policy is to determine the inspection intervals for each component such that the long-run cost rate is minimized. Due to the dependence among components, an exact optimal solution is difficult to obtain. Concerned with the intractability of the problem, a heuristic method named “base interval approach” is adopted to reduce the computational complexity. Performance of the base interval approach is analyzed, and the result shows that the proposed policy can approximate the optimal policy within a small factor. Two numerical examples are presented to illustrate the effectiveness of the policy

The stochastic opportunistic replacement problem, part III: improved bounding procedures

We consider the problem to find a schedule for component replacement in a multi-component system, whose components possess stochastic lives and economic dependencies, such that the expected costs for maintenance during a pre-defined time period are minimized. The problem was considered in Patriksson et al. (Ann Oper Res 224:51–75, 2015), in which a two-stage approximation of the problem was optimized through decomposition (denoted the optimization policy). The current paper improves the effectiveness of the decomposition approach by establishing a tighter bound on the value of the recourse function (i.e., the second stage in the approximation). A general lower bound on the expected maintenance cost is also established. Numerical experiments with 100 simulation scenarios for each of four test instances show that the tighter bound yields a decomposition generating fewer optimality cuts. They also illustrate the quality of the lower bound. Contrary to results presented earlier, an age-based policy performs on par with the optimization policy, although most simple policies perform worse than the optimization policy