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

Simulation-based optimisation of complex maintenance systems

There is a potential as well as a growing interest amongst researchers to utilise simulation in optimising maintenance systems. The state of the art in simulation-based optimisation of maintenance was established by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. In general, approaches to optimise maintenance varied significantly in the literature. Overall, these studies highlight the need for a framework that unifies the approach to optimising maintenance systems. Framework requirements were established through two main sources of published research. Surveys on maintenance simulation optimisation were examined to document comments on the approaches authors follow while optimising maintenance systems. In addition, advanced and future maintenance strategies were documented to ensure it can be accommodated in the proposed framework. The proposed framework was developed using a standard flowchart tool due to its familiarity and ability to depict decision structures clearly. It provides a systematic methodology that details the steps required to connect the simulation model to an optimisation engine. Not only it provides guidance in terms of formulating the optimal problem for the maintenance system at hand but it also provides support and assistance in defining the optimisation scope and investigating applicable maintenance strategies. Additionally, it considers current issues relating to maintenance systems both in research and in practice such as uncertainty, complexity and multi-objective optimisation. The proposed framework cannot be applied using existing approaches for modelling maintenance. Existing modelling approaches using simulation have a number of limitations: The maintenance system is modelled separately from other inter-related systems such as production and spare parts logistics. In addition, these approaches are used to model one maintenance strategy only. A novel approach for modelling maintenance using Discrete Event Simulation is proposed. The proposed approach enables the modelling of interactions amongst various maintenance strategies and their effects on the assets in non-identical multi-unit systems. Using the proposed framework and modelling approach, simulation-based optimisation was conducted on an academic case and two industrial cases that are varied in terms of sector, size, number of manufacturing processes and level of maintenance documentation. Following the structured framework enabled discussing and selecting the suitable optimisation scope and applicable maintenance strategies as well as formulating a customised optimal problem for each case. The results of the study suggest that over-looking the optimisation of maintenance strategies may lead to sub-optimal solutions. In addition, this research provides insights for non-conflicting objectives in maintenance systems

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

Optimisation of inspection policy for multi-line production systems

This paper develops a simulation model to determine the cost-optimum inspection policy for a multi-line production system taking account of simultaneous downtime. The machines in the multi-line system are subject to a two stage failure process that is modelled using the delay-time concept. Our study indicates that: consecutive inspection of lines with priority for failure repair is cost-optimal, with a cost reduction of 61% compared to a ‘run-to-failure’ policy; and maintainers need to be responsive to operational requirements. Our ideas are developed in the context of a case study of a plant with three parallel lines, one of which is on cold-standby. Keywords: maintenance; delay-time model; simulation; production; parallel lines; manufacturing; preventive maintenance

Preventive maintenance task balancing with spare parts optimisation via big-bang big-crunch algorithm

Work balancing increasingly plays an important role in both the production and maintenance functions. However, the literature on work balancing problems in transfer line manufacturing systems provides little information on the contributions of maintenance technicians and spare parts with a focus on penalty, technicians’ costs and incentives for staff. Unlike existing reports, the current investigation attempts to solve the maintenance task balancing problem. It combines preventive maintenance technicians’ assignments with product demand and spares utilisation in a transfer line manufacturing system. It uses an optimisation framework that measures the success of post-line balancing solution performance in a system from a holistic perspective. The novelty of the approach lies in the integration of technicians and spare parts theory and the introduction of penalty, technicians’ costs and incentive for staff. The proposed optimisation method was applied to a case study for detergent manufacturing system as a means of testing the effectiveness and robustness of the approach. The results show that the proposed model appears to be effective. Some simulations were also carried out to complement practical result

Simulation-based optimisation of maintenance systems: Industrial case studies

Investigating the optimum blend of maintenance strategies for a given manufacturing system is a continuing concern amongst maintenance academics and professionals. Recent evidence suggests that little research is conducted on the simulation optimisation of maintenance in industrial systems. This study was designed to make an important contribution to the field of simulation-based optimisation of maintenance by presenting two empirical case studies: a tyre re-treading factory and a petro-chemical plant. It is one of the first to optimise various maintenance strategies simultaneously with their parameters in industrial manufacturing systems while considering production dynamics. Stochastic Discrete Event Simulation models were developed and connected to a Multi-Objective Optimisation engine. Various maintenance strategies were investigated including Corrective Maintenance, Preventive Maintenance, Opportunistic Maintenance and Condition-Based Maintenance. The results of this research suggest that over-looking the optimisation of maintenance on the strategic level may lead to sub-optimal solutions. In addition, it appears that traditional trade-offs between maintenance cost and production throughput are not present in some maintenance systems. This is an interesting observation that requires further investigation and experimentation

Joint maintenance-inventory optimisation of parallel production systems

We model a joint inspection and spare parts inventory policy for maintaining machines in a parallel system, where simultaneous downtime seriously impacts upon production performance and has a significant financial consequence. This dependency between system components means that analysis of realistic maintenance models is intractable. Therefore we use simulation and a numerical optimisation tool to study the cost-optimality of several policies. Inspection maintenance is modelled using the delay-time concept. Critical spare parts replenishment is considered using several variants of a periodic review policy. In particular, our results indicate that the cost-optimal policy is characterised by equal frequencies of inspection and replenishment, and delivery of spare parts that coincides with maintenance intervention. In general, our model provides a framework for studying the interaction of spare parts ordering with maintenance scheduling. The sensitivity analysis that we present offers insights for the effective management of such parallel systems, not only in a paper-making plant, which motivates our modelling development, but also in other manufacturing contexts

Joint optimisation of inspection maintenance and spare parts provisioning: a comparative study of inventory policies using simulation and survey data

The demand for industrial plant spare parts is driven, at least in part, by maintenance requirements. It is therefore important to jointly optimise planned maintenance and the associated spare parts inventory using the most appropriate maintenance and replenishment policies. In this simulation-based study, we address this challenge in the context of the random failure of parts in service and the replacement of defective parts at inspections of period T. Inspections are modelled using the delay-time concept. A number of simultaneous periodic review and continuous review replenishment policies are compared. A paper making plant provides a real context for the presentation of our ideas. We survey practitioners working with such plant to collect real data that inform the values of parameters in the models. Our simulation results indicate that a periodic review policy with ordering that is twice as frequent as inspection is cost optimal in the context of the plant that we study. For the purpose of comparison, we also present and discuss the characteristics of the various policies considered

Design project 1968/9: management report

1. INTRODUCTION The design of an automatic assembly machine with versatility in application was undertaken as a group project by post-graduate students attending a course in production technology. This report summarises the work clone and conclusions reached during the project. In addition there are available five other reports which describe the designing of different areas of the machine in full detail (refs. 1 to 6). There is also the report of a technical survey which was carried out to investigate industrial requirements for automatic assembly. In order that this report may serve as a guide, a summary of the content of each of the other reports is included